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scTCRpy/utils.py delete mode 100644 scTCRpy/utils.pyc diff --git a/.codecov.yaml b/.codecov.yaml new file mode 100644 index 000000000..d0c0e2917 --- /dev/null +++ b/.codecov.yaml @@ -0,0 +1,17 @@ +# Based on pydata/xarray +codecov: + require_ci_to_pass: no + +coverage: + status: + project: + default: + # Require 1% coverage, i.e., always succeed + target: 1 + patch: false + changes: false + +comment: + layout: diff, flags, files + behavior: once + require_base: no diff --git a/.cruft.json b/.cruft.json new file mode 100644 index 000000000..27dbd62e4 --- /dev/null +++ b/.cruft.json @@ -0,0 +1,43 @@ +{ + "template": "https://github.com/scverse/cookiecutter-scverse", + "commit": "94ef9fb6f9ad8cfe65a3d9575679c03c80c49cd1", + "checkout": "v0.5.0", + "context": { + "cookiecutter": { + "project_name": "scirpy", + "package_name": "scirpy", + "project_description": "A very interesting piece of code", + "author_full_name": "Gregor Sturm", + "author_email": "mail@gregor-sturm.de", + "github_user": "grst", + "github_repo": "scirpy", + "license": "BSD 3-Clause License", + "ide_integration": true, + "_copy_without_render": [ + ".github/workflows/build.yaml", + ".github/workflows/test.yaml", + "docs/_templates/autosummary/**.rst" + ], + "_exclude_on_template_update": [ + "CHANGELOG.md", + "LICENSE", + "README.md", + "docs/api.md", + "docs/index.md", + "docs/notebooks/example.ipynb", + "docs/references.bib", + "docs/references.md", + "src/**", + "tests/**" + ], + "_render_devdocs": false, + "_jinja2_env_vars": { + "lstrip_blocks": true, + "trim_blocks": true + }, + "_template": "https://github.com/scverse/cookiecutter-scverse", + "_commit": "94ef9fb6f9ad8cfe65a3d9575679c03c80c49cd1" + } + }, + "directory": null +} diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 000000000..66678e378 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,15 @@ +root = true + +[*] +indent_style = space +indent_size = 4 +end_of_line = lf +charset = utf-8 +trim_trailing_whitespace = true +insert_final_newline = true + +[{*.{yml,yaml,toml},.cruft.json}] +indent_size = 2 + +[Makefile] +indent_style = tab diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 000000000..3ca1ccbde --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,94 @@ +name: Bug report +description: Report something that is broken or incorrect +labels: bug +body: + - type: markdown + attributes: + value: | + **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) + detailing how to provide the necessary information for us to reproduce your bug. In brief: + * Please provide exact steps how to reproduce the bug in a clean Python environment. + * In case it's not clear what's causing this bug, please provide the data or the data generation procedure. + * Sometimes it is not possible to share the data, but usually it is possible to replicate problems on publicly + available datasets or to share a subset of your data. + + - type: textarea + id: report + attributes: + label: Report + description: A clear and concise description of what the bug is. + validations: + required: true + + - type: textarea + id: versions + attributes: + label: Versions + description: | + Which version of packages. + + Please install `session-info2`, run the following command in a notebook, + click the “Copy as Markdown” button, then paste the results into the text box below. + + ```python + In[1]: import session_info2; session_info2.session_info(dependencies=True) + ``` + + Alternatively, run this in a console: + + ```python + >>> import session_info2; print(session_info2.session_info(dependencies=True)._repr_mimebundle_()["text/markdown"]) + ``` + render: python + placeholder: | + anndata 0.11.3 + ---- ---- + charset-normalizer 3.4.1 + coverage 7.7.0 + psutil 7.0.0 + dask 2024.7.1 + jaraco.context 5.3.0 + numcodecs 0.15.1 + jaraco.functools 4.0.1 + Jinja2 3.1.6 + sphinxcontrib-jsmath 1.0.1 + sphinxcontrib-htmlhelp 2.1.0 + toolz 1.0.0 + session-info2 0.1.2 + PyYAML 6.0.2 + llvmlite 0.44.0 + scipy 1.15.2 + pandas 2.2.3 + sphinxcontrib-devhelp 2.0.0 + h5py 3.13.0 + tblib 3.0.0 + setuptools-scm 8.2.0 + more-itertools 10.3.0 + msgpack 1.1.0 + sparse 0.15.5 + wrapt 1.17.2 + jaraco.collections 5.1.0 + numba 0.61.0 + pyarrow 19.0.1 + pytz 2025.1 + MarkupSafe 3.0.2 + crc32c 2.7.1 + sphinxcontrib-qthelp 2.0.0 + sphinxcontrib-serializinghtml 2.0.0 + zarr 2.18.4 + asciitree 0.3.3 + six 1.17.0 + sphinxcontrib-applehelp 2.0.0 + numpy 2.1.3 + cloudpickle 3.1.1 + sphinxcontrib-bibtex 2.6.3 + natsort 8.4.0 + jaraco.text 3.12.1 + setuptools 76.1.0 + Deprecated 1.2.18 + packaging 24.2 + python-dateutil 2.9.0.post0 + ---- ---- + Python 3.13.2 | packaged by conda-forge | (main, Feb 17 2025, 14:10:22) [GCC 13.3.0] + OS Linux-6.11.0-109019-tuxedo-x86_64-with-glibc2.39 + Updated 2025-03-18 15:47 diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 000000000..5b62547f9 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: Scverse Community Forum + url: https://discourse.scverse.org/ + about: If you have questions about “How to do X”, please ask them here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 000000000..585af6936 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,11 @@ +name: Feature request +description: Propose a new feature for scirpy +labels: enhancement +body: + - type: textarea + id: description + attributes: + label: Description of feature + description: Please describe your suggestion for a new feature. It might help to describe a problem or use case, plus any alternatives that you have considered. + validations: + required: true diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml new file mode 100644 index 000000000..83e01a1ee --- /dev/null +++ b/.github/workflows/build.yaml @@ -0,0 +1,33 @@ +name: Check Build + +on: + push: + branches: [main] + pull_request: + branches: [main] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +defaults: + run: + # to fail on error in multiline statements (-e), in pipes (-o pipefail), and on unset variables (-u). + shell: bash -euo pipefail {0} + +jobs: + package: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + filter: blob:none + fetch-depth: 0 + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + cache-dependency-glob: pyproject.toml + - name: Build package + run: uv build + - name: Check package + run: uvx twine check --strict dist/*.whl diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 000000000..d4f2594aa --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,34 @@ +name: Release + +on: + release: + types: [published] + +defaults: + run: + # to fail on error in multiline statements (-e), in pipes (-o pipefail), and on unset variables (-u). + shell: bash -euo pipefail {0} + +# Use "trusted publishing", see https://docs.pypi.org/trusted-publishers/ +jobs: + release: + name: Upload release to PyPI + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/scirpy + permissions: + id-token: write # IMPORTANT: this permission is mandatory for trusted publishing + steps: + - uses: actions/checkout@v4 + with: + filter: blob:none + fetch-depth: 0 + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + cache-dependency-glob: pyproject.toml + - name: Build package + run: uv build + - name: Publish package distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 000000000..d5cfb2a9c --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,59 @@ +name: Test + +on: + push: + branches: [main] + pull_request: + branches: [main] + schedule: + - cron: "0 5 1,15 * *" + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +defaults: + run: + # to fail on error in multiline statements (-e), in pipes (-o pipefail), and on unset variables (-u). + shell: bash -euo pipefail {0} + +jobs: + test: + runs-on: ${{ matrix.os }} + + strategy: + fail-fast: false + matrix: + include: + - os: ubuntu-latest + python: "3.10" + - os: ubuntu-latest + python: "3.12" + - os: ubuntu-latest + python: "3.12" + pip-flags: "--pre" + name: PRE-RELEASE DEPENDENCIES + + name: ${{ matrix.name }} Python ${{ matrix.python }} + + env: + OS: ${{ matrix.os }} + PYTHON: ${{ matrix.python }} + + steps: + - uses: actions/checkout@v4 + with: + filter: blob:none + fetch-depth: 0 + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + cache-dependency-glob: pyproject.toml + - name: run tests using hatch + env: + MPLBACKEND: agg + PLATFORM: ${{ matrix.os }} + DISPLAY: :42 + run: uvx hatch test --cover --python ${{ matrix.python }} + - name: Upload coverage + uses: codecov/codecov-action@v4 diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..31e10b3ea --- /dev/null +++ b/.gitignore @@ -0,0 +1,20 @@ +# Temp files +.DS_Store +*~ +buck-out/ + +# Compiled files +.venv/ +__pycache__/ +.*cache/ + +# Distribution / packaging +/dist/ + +# Tests and coverage +/data/ +/node_modules/ + +# docs +/docs/generated/ +/docs/_build/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..0fcce11e2 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,47 @@ +fail_fast: false +default_language_version: + python: python3 +default_stages: + - pre-commit + - pre-push +minimum_pre_commit_version: 2.16.0 +repos: + - repo: https://github.com/biomejs/pre-commit + rev: v1.9.4 + hooks: + - id: biome-format + exclude: ^\.cruft\.json$ # inconsistent indentation with cruft - file never to be modified manually. + - repo: https://github.com/tox-dev/pyproject-fmt + rev: v2.5.1 + hooks: + - id: pyproject-fmt + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.11.2 + hooks: + - id: ruff + types_or: [python, pyi, jupyter] + args: [--fix, --exit-non-zero-on-fix] + - id: ruff-format + types_or: [python, pyi, jupyter] + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: detect-private-key + - id: check-ast + - id: end-of-file-fixer + - id: mixed-line-ending + args: [--fix=lf] + - id: trailing-whitespace + - id: check-case-conflict + # Check that there are no merge conflicts (could be generated by template sync) + - id: check-merge-conflict + args: [--assume-in-merge] + - repo: local + hooks: + - id: forbid-to-commit + name: Don't commit rej files + entry: | + Cannot commit .rej files. These indicate merge conflicts that arise during automated template updates. + Fix the merge conflicts manually and remove the .rej files. + language: fail + files: '.*\.rej$' diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 000000000..69897c3b3 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,16 @@ +# https://docs.readthedocs.io/en/stable/config-file/v2.html +version: 2 +build: + os: ubuntu-20.04 + tools: + python: "3.10" +sphinx: + configuration: docs/conf.py + # disable this for more lenient docs builds + fail_on_warning: true +python: + install: + - method: pip + path: . + extra_requirements: + - doc diff --git a/.vscode/extensions.json b/.vscode/extensions.json new file mode 100644 index 000000000..caaeb4f73 --- /dev/null +++ b/.vscode/extensions.json @@ -0,0 +1,18 @@ +{ + "recommendations": [ + // GitHub integration + "github.vscode-github-actions", + "github.vscode-pull-request-github", + // Language support + "ms-python.python", + "ms-python.vscode-pylance", + "ms-toolsai.jupyter", + "tamasfe.even-better-toml", + // Dependency management + "ninoseki.vscode-mogami", + // Linting and formatting + "editorconfig.editorconfig", + "charliermarsh.ruff", + "biomejs.biome", + ], +} diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 000000000..36d187461 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,33 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Build Documentation", + "type": "debugpy", + "request": "launch", + "module": "sphinx", + "args": ["-M", "html", ".", "_build"], + "cwd": "${workspaceFolder}/docs", + "console": "internalConsole", + "justMyCode": false, + }, + { + "name": "Python: Debug Test", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "purpose": ["debug-test"], + "console": "internalConsole", + "justMyCode": false, + "env": { + "PYTEST_ADDOPTS": "--color=yes", + }, + "presentation": { + "hidden": true, + }, + }, + ], +} diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 000000000..e034b91f7 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,18 @@ +{ + "[python][json][jsonc]": { + "editor.formatOnSave": true, + }, + "[python]": { + "editor.defaultFormatter": "charliermarsh.ruff", + "editor.codeActionsOnSave": { + "source.fixAll": "always", + "source.organizeImports": "always", + }, + }, + "[json][jsonc]": { + "editor.defaultFormatter": "biomejs.biome", + }, + "python.analysis.typeCheckingMode": "basic", + "python.testing.pytestEnabled": true, + "python.testing.pytestArgs": ["-vv", "--color=yes"], +} diff --git a/basic_tcr.py b/basic_tcr.py deleted file mode 100644 index 6eb7aed26..000000000 --- a/basic_tcr.py +++ /dev/null @@ -1,134 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - -import sys - -from scTCRpy.utils import stampTime -from scTCRpy.layoutExp import eXperiment -from scTCRpy.reportHTML import htmlReporter -from scTCRpy import talkR -from scTCRpy import resultData - -def __main__(): - al = sys.argv - arg, project_root, out_dir, samples_file, celltypes, gen_list_file, report_file, html_template = sys.argv - sample_features = [] - with open(project_root+'/'+samples_file, 'r') as f: - for line in f: - line = line.replace('\n', '') - line = line.replace('\r', '') - sample_features.append(line.split('\t')) - starttime = stampTime('Starting execution at') - #Create an empty layout structure, set top folder for files and initialize main data containers (also offering a possibility to pass prepopulated data containers form upstream steps) - experiment = eXperiment(projectRoot=project_root, outDir=out_dir, reportFile=report_file, genListFile=gen_list_file, criticalClonotypeSize=6, verbose=True) - #Add samples to the experiment by defining name, barcode prefix and file paths needed for downstream steps - preprocesstime = stampTime('Preprocessing sample data') - for a1, a2, a3, a4, a5 in sample_features: - experiment.addSample(a1, a2, a3, t_tcrDir=a4, t_gexDir=a5) - timepoint = stampTime('Parsed basic sample info.', before=preprocesstime) - #Reassign clonotypes based on the dominant TCR chain pair - experiment.cellTCR.reassessClonotypes() - timepoint = stampTime('Reassigned clonotypes.', before=timepoint) - #Add some two-dimensional projections of cell populations - primarily based on gene expression, but theoratically any coordinate collection can be imported - experiment.addProjection('cells', proFile=celltypes) - timepoint = stampTime('Cell population projections added.', before=timepoint) - #A possible method to claculate distances is the sequence similarity of the CDR3 regions, a method reimplemented from tcrDist - experiment.distanceCalc('seqSim', metric='TCR') - timepoint = stampTime('Distance matrix based on TCR sequences calculated.', before=timepoint) - timepoint = stampTime('First round of data preprocessing finised.', before=preprocesstime) - #Export data to tables for R scripts and other third party analysis. while recollecting diversity and Kidera calculations from R - talkrtime = stampTime('Starting data exchange with R.') - talkR.featRchain(experiment) - talkR.exportCellFeatures(experiment) - talkR.groupDiversity(experiment) - stampTime('Diversity scores and Kidera factors called with R scripts.', before=talkrtime) - #Another distance matrix could also be calculated, based on Kidera factors of the CDR3 regions of the dominant TCR pair - experiment.distanceCalc('Kidera', metric='Kidera') #Infinite distances after calculation, fix it later! - timepoint = stampTime('Distance matrix based on Kidera factors calculated.', before=timepoint) - #After all data is preprocessed, final calculations and visualizations can be done - reporttime = stampTime('Starting to plot graphs for the report file.') - reporter = htmlReporter(experiment.reportFile, template=html_template, saveres=150, savepics=True) - #Some intital setup of html is needed - html_frame = [ - ('title', {'div': {'class': 'main_title', 'innerHTML': 'Analysis of TCR sequencing results'}}), - ('side_bar', {'div': {'class': 'side_content'}}), - ('content', {'div': {'class': 'main_content'}}) - ] - reporter.add(html_frame) - timepoint = stampTime('Report file initialized', before=timepoint) - #Add a clonotype frequency table - clonotype_table = resultData.clonoTable(experiment) - reporter.add(**clonotype_table.html()) - timepoint = stampTime('Clonotype tables created for the report', before=reporttime) - #Plot the overlap between clonotypes of the two samples - clonoverlap = (resultData.clonOverlap(experiment)) - reporter.add(**clonoverlap.html()) - timepoint = stampTime('Clonotype overlap plotted', before=timepoint) - #Add a tab for basic TCR statistics - tcr_stats = { - 'div': { - 'class': 'toggle_tab', - 'id': 'tcr_stats' - }, - 'parent': 'content', - 'sidelabel': [ - 'side_bar', 'button_tcr_stats', - { - 'innerHTML': 'TCR chain statistics', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, 'tcr_stats')" - } - ] - } - reporter.add('tcr_stats', **tcr_stats) - #Plot CDR3 amino acid lengths - lena_tcr = resultData.lenCdrAa(experiment) - reporter.add(**lena_tcr.html()) - timepoint = stampTime('CDR3 amino acid lengths plotted', before=timepoint) - #Plot CDR3 nucleotide lengths - lent_tcr = resultData.lenCdrNt(experiment) - reporter.add(**lent_tcr.html()) - timepoint = stampTime('CDR3 nucleotide lengths plotted', before=timepoint) - #Plot added nucleotides - adn_tcr = resultData.lenCdrIns(experiment) - reporter.add(**adn_tcr.html()) - timepoint = stampTime('CDR3 added nucleotides plotted', before=timepoint) - #Plot TCR chain expression - xpr_tcr = resultData.exprTVio(experiment) - reporter.add(**xpr_tcr.html()) - timepoint = stampTime('TCR chain expression levels plotted', before=timepoint) - #Plot TCR chain pairing in samples and cell types - orhpan_tcr = resultData.orphanBar(experiment) - reporter.add(**orhpan_tcr.html()) - timepoint = stampTime('TCR chain pairing stats plotted', before=timepoint) - #Show VDJ segment usage and frequency of segments - seg_use = resultData.segmentBar(experiment) - reporter.add(**seg_use.html()) - timepoint = stampTime('VDJ segment usage plotted', before=timepoint) - #Plot clonotypes and cell types over a projection of cell features (mainly cell types, based on gene expression) - cl_project = resultData.clonTopology(experiment) - reporter.add(**cl_project.html()) - timepoint = stampTime('Clonotype projection plotted', before=timepoint) - #Show diversity of samples and cell types - group_div = resultData.diverBar(experiment) - reporter.add(**group_div.html()) - timepoint = stampTime('Diversity of sample groups plotted', before=timepoint) - #Show how the cells clustered based on distance metrics - cell_dist = resultData.clonoDist(experiment) - reporter.add(**cell_dist.html()) - timepoint = stampTime('T cells clustered based on TCR similarity', before=timepoint) - #Plot TCR based distances between cell types - type_dist = resultData.cellTypeDist(experiment) - reporter.add(**type_dist.html()) - timepoint = stampTime('T cell types clustered based on TCR similarity', before=timepoint) - #Plot the expression of top genes in clonotype groups - top_gex = resultData.clonoGex(experiment) - reporter.add(**top_gex.html()) - timepoint = stampTime('Expression of top genes plotted', before=timepoint) - reporter.report() - stampTime('Report compiled to HTML file ' + experiment.reportFile, before=reporttime) - stampTime('TCR results successfully analysed, script execution ends.', before=starttime) - return - -if __name__ == '__main__': - __main__() diff --git a/biome.jsonc b/biome.jsonc new file mode 100644 index 000000000..2175c16e6 --- /dev/null +++ b/biome.jsonc @@ -0,0 +1,16 @@ +{ + "$schema": "https://biomejs.dev/schemas/1.9.4/schema.json", + "formatter": { "useEditorconfig": true }, + "overrides": [ + { + "include": ["./.vscode/*.json", "**/*.jsonc"], + "json": { + "formatter": { "trailingCommas": "all" }, + "parser": { + "allowComments": true, + "allowTrailingCommas": true, + }, + }, + }, + ], +} diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 000000000..d4bb2cbb9 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_static/.gitkeep b/docs/_static/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css new file mode 100644 index 000000000..b8c8d47fa --- /dev/null +++ b/docs/_static/css/custom.css @@ -0,0 +1,4 @@ +/* Reduce the font size in data frames - See https://github.com/scverse/cookiecutter-scverse/issues/193 */ +div.cell_output table.dataframe { + font-size: 0.8em; +} diff --git a/docs/_templates/.gitkeep b/docs/_templates/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/docs/_templates/autosummary/class.rst b/docs/_templates/autosummary/class.rst new file mode 100644 index 000000000..7b4a0cf87 --- /dev/null +++ b/docs/_templates/autosummary/class.rst @@ -0,0 +1,61 @@ +{{ fullname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. add toctree option to make autodoc generate the pages + +.. autoclass:: {{ objname }} + +{% block attributes %} +{% if attributes %} +Attributes table +~~~~~~~~~~~~~~~~ + +.. autosummary:: +{% for item in attributes %} + ~{{ name }}.{{ item }} +{%- endfor %} +{% endif %} +{% endblock %} + +{% block methods %} +{% if methods %} +Methods table +~~~~~~~~~~~~~ + +.. autosummary:: +{% for item in methods %} + {%- if item != '__init__' %} + ~{{ name }}.{{ item }} + {%- endif -%} +{%- endfor %} +{% endif %} +{% endblock %} + +{% block attributes_documentation %} +{% if attributes %} +Attributes +~~~~~~~~~~ + +{% for item in attributes %} + +.. autoattribute:: {{ [objname, item] | join(".") }} +{%- endfor %} + +{% endif %} +{% endblock %} + +{% block methods_documentation %} +{% if methods %} +Methods +~~~~~~~ + +{% for item in methods %} +{%- if item != '__init__' %} + +.. automethod:: {{ [objname, item] | join(".") }} +{%- endif -%} +{%- endfor %} + +{% endif %} +{% endblock %} diff --git a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 000000000..d9e79ba64 --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1,3 @@ +```{include} ../CHANGELOG.md + +``` diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 000000000..591429cb6 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,131 @@ +# Configuration file for the Sphinx documentation builder. + +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- +import sys +from datetime import datetime +from importlib.metadata import metadata +from pathlib import Path + +HERE = Path(__file__).parent +sys.path.insert(0, str(HERE / "extensions")) + + +# -- Project information ----------------------------------------------------- + +# NOTE: If you installed your project in editable mode, this might be stale. +# If this is the case, reinstall it to refresh the metadata +info = metadata("scirpy") +project_name = info["Name"] +author = info["Author"] +copyright = f"{datetime.now():%Y}, {author}." +version = info["Version"] +urls = dict(pu.split(", ") for pu in info.get_all("Project-URL")) +repository_url = urls["Source"] + +# The full version, including alpha/beta/rc tags +release = info["Version"] + +bibtex_bibfiles = ["references.bib"] +templates_path = ["_templates"] +nitpicky = True # Warn about broken links +needs_sphinx = "4.0" + +html_context = { + "display_github": True, # Integrate GitHub + "github_user": "grst", + "github_repo": project_name, + "github_version": "main", + "conf_py_path": "/docs/", +} + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. +# They can be extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. +extensions = [ + "myst_nb", + "sphinx_copybutton", + "sphinx.ext.autodoc", + "sphinx.ext.intersphinx", + "sphinx.ext.autosummary", + "sphinx.ext.napoleon", + "sphinxcontrib.bibtex", + "sphinx_autodoc_typehints", + "sphinx_tabs.tabs", + "sphinx.ext.mathjax", + "IPython.sphinxext.ipython_console_highlighting", + "sphinxext.opengraph", + *[p.stem for p in (HERE / "extensions").glob("*.py")], +] + +autosummary_generate = True +autodoc_member_order = "groupwise" +default_role = "literal" +napoleon_google_docstring = False +napoleon_numpy_docstring = True +napoleon_include_init_with_doc = False +napoleon_use_rtype = True # having a separate entry generally helps readability +napoleon_use_param = True +myst_heading_anchors = 6 # create anchors for h1-h6 +myst_enable_extensions = [ + "amsmath", + "colon_fence", + "deflist", + "dollarmath", + "html_image", + "html_admonition", +] +myst_url_schemes = ("http", "https", "mailto") +nb_output_stderr = "remove" +nb_execution_mode = "off" +nb_merge_streams = True +typehints_defaults = "braces" + +source_suffix = { + ".rst": "restructuredtext", + ".ipynb": "myst-nb", + ".myst": "myst-nb", +} + +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "anndata": ("https://anndata.readthedocs.io/en/stable/", None), + "scanpy": ("https://scanpy.readthedocs.io/en/stable/", None), + "numpy": ("https://numpy.org/doc/stable/", None), +} + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "sphinx_book_theme" +html_static_path = ["_static"] +html_css_files = ["css/custom.css"] + +html_title = project_name + +html_theme_options = { + "repository_url": repository_url, + "use_repository_button": True, + "path_to_docs": "docs/", + "navigation_with_keys": False, +} + +pygments_style = "default" + +nitpick_ignore = [ + # If building the documentation fails because of a missing link that is outside your control, + # you can add an exception to this list. + # ("py:class", "igraph.Graph"), +] diff --git a/docs/contributing.md b/docs/contributing.md new file mode 100644 index 000000000..95920f65a --- /dev/null +++ b/docs/contributing.md @@ -0,0 +1,214 @@ +# Contributing guide + +Scanpy provides extensive [developer documentation][scanpy developer guide], most of which applies to this project, too. +This document will not reproduce the entire content from there. +Instead, it aims at summarizing the most important information to get you started on contributing. + +We assume that you are already familiar with git and with making pull requests on GitHub. +If not, please refer to the [scanpy developer guide][]. + +[scanpy developer guide]: https://scanpy.readthedocs.io/en/latest/dev/index.html + +## Installing dev dependencies + +In addition to the packages needed to _use_ this package, +you need additional python packages to [run tests](#writing-tests) and [build the documentation](#docs-building). + +:::::{tabs} +::::{group-tab} Hatch +The easiest way is to get familiar with [hatch environments][], with which these tasks are simply: + +```bash +hatch test # defined in the table [tool.hatch.envs.hatch-test] in pyproject.toml +hatch run docs:build # defined in the table [tool.hatch.envs.docs] +``` + +:::: + +::::{group-tab} Pip +If you prefer managing environments manually, you can use `pip`: + +```bash +cd scirpy +python3 -m venv .venv +source .venv/bin/activate +pip install -e ".[dev,test,doc]" +``` + +:::: +::::: + +[hatch environments]: https://hatch.pypa.io/latest/tutorials/environment/basic-usage/ + +## Code-style + +This package uses [pre-commit][] to enforce consistent code-styles. +On every commit, pre-commit checks will either automatically fix issues with the code, or raise an error message. + +To enable pre-commit locally, simply run + +```bash +pre-commit install +``` + +in the root of the repository. +Pre-commit will automatically download all dependencies when it is run for the first time. + +Alternatively, you can rely on the [pre-commit.ci][] service enabled on GitHub. +If you didn't run `pre-commit` before pushing changes to GitHub it will automatically commit fixes to your pull request, or show an error message. + +If pre-commit.ci added a commit on a branch you still have been working on locally, simply use + +```bash +git pull --rebase +``` + +to integrate the changes into yours. +While the [pre-commit.ci][] is useful, we strongly encourage installing and running pre-commit locally first to understand its usage. + +Finally, most editors have an _autoformat on save_ feature. +Consider enabling this option for [ruff][ruff-editors] and [biome][biome-editors]. + +[pre-commit]: https://pre-commit.com/ +[pre-commit.ci]: https://pre-commit.ci/ +[ruff-editors]: https://docs.astral.sh/ruff/integrations/ +[biome-editors]: https://biomejs.dev/guides/integrate-in-editor/ + +(writing-tests)= + +## Writing tests + +This package uses [pytest][] for automated testing. +Please write {doc}`scanpy:dev/testing` for every function added to the package. + +Most IDEs integrate with pytest and provide a GUI to run tests. +Just point yours to one of the environments returned by + +```bash +hatch env create hatch-test # create test environments for all supported versions +hatch env find hatch-test # list all possible test environment paths +``` + +Alternatively, you can run all tests from the command line by executing + +:::::{tabs} +::::{group-tab} Hatch + +```bash +hatch test # test with the highest supported Python version +# or +hatch test --all # test with all supported Python versions +``` + +:::: + +::::{group-tab} Pip + +```bash +source .venv/bin/activate +pytest +``` + +:::: +::::: + +in the root of the repository. + +[pytest]: https://docs.pytest.org/ + +### Continuous integration + +Continuous integration will automatically run the tests on all pull requests and test +against the minimum and maximum supported Python version. + +Additionally, there's a CI job that tests against pre-releases of all dependencies (if there are any). +The purpose of this check is to detect incompatibilities of new package versions early on and +gives you time to fix the issue or reach out to the developers of the dependency before the package is released to a wider audience. + +## Publishing a release + +### Updating the version number + +Before making a release, you need to update the version number in the `pyproject.toml` file. +Please adhere to [Semantic Versioning][semver], in brief + +> Given a version number MAJOR.MINOR.PATCH, increment the: +> +> 1. MAJOR version when you make incompatible API changes, +> 2. MINOR version when you add functionality in a backwards compatible manner, and +> 3. PATCH version when you make backwards compatible bug fixes. +> +> Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format. + +Once you are done, commit and push your changes and navigate to the "Releases" page of this project on GitHub. +Specify `vX.X.X` as a tag name and create a release. +For more information, see [managing GitHub releases][]. +This will automatically create a git tag and trigger a Github workflow that creates a release on [PyPI][]. + +[semver]: https://semver.org/ +[managing GitHub releases]: https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository +[pypi]: https://pypi.org/ + +## Writing documentation + +Please write documentation for new or changed features and use-cases. +This project uses [sphinx][] with the following features: + +- The [myst][] extension allows to write documentation in markdown/Markedly Structured Text +- [Numpy-style docstrings][numpydoc] (through the [napoloen][numpydoc-napoleon] extension). +- Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks)) +- [sphinx-autodoc-typehints][], to automatically reference annotated input and output types +- Citations (like {cite:p}`Virshup_2023`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/) + +See scanpy’s {doc}`scanpy:dev/documentation` for more information on how to write your own. + +[sphinx]: https://www.sphinx-doc.org/en/master/ +[myst]: https://myst-parser.readthedocs.io/en/latest/intro.html +[myst-nb]: https://myst-nb.readthedocs.io/en/latest/ +[numpydoc-napoleon]: https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html +[numpydoc]: https://numpydoc.readthedocs.io/en/latest/format.html +[sphinx-autodoc-typehints]: https://github.com/tox-dev/sphinx-autodoc-typehints + +### Tutorials with myst-nb and jupyter notebooks + +The documentation is set-up to render jupyter notebooks stored in the `docs/notebooks` directory using [myst-nb][]. +Currently, only notebooks in `.ipynb` format are supported that will be included with both their input and output cells. +It is your responsibility to update and re-run the notebook whenever necessary. + +If you are interested in automatically running notebooks as part of the continuous integration, +please check out [this feature request][issue-render-notebooks] in the `cookiecutter-scverse` repository. + +[issue-render-notebooks]: https://github.com/scverse/cookiecutter-scverse/issues/40 + +#### Hints + +- If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. + Only if you do so can sphinx automatically create a link to the external documentation. +- If building the documentation fails because of a missing link that is outside your control, + you can add an entry to the `nitpick_ignore` list in `docs/conf.py` + +(docs-building)= + +#### Building the docs locally + +:::::{tabs} +::::{group-tab} Hatch + +```bash +hatch run docs:build +hatch run docs:open +``` + +:::: + +::::{group-tab} Pip + +```bash +source .venv/bin/activate +cd docs +make html +(xdg-)open _build/html/index.html +``` + +:::: +::::: diff --git a/docs/extensions/typed_returns.py b/docs/extensions/typed_returns.py new file mode 100644 index 000000000..0fbffefe3 --- /dev/null +++ b/docs/extensions/typed_returns.py @@ -0,0 +1,32 @@ +# code from https://github.com/theislab/scanpy/blob/master/docs/extensions/typed_returns.py +# with some minor adjustment +from __future__ import annotations + +import re +from collections.abc import Generator, Iterable + +from sphinx.application import Sphinx +from sphinx.ext.napoleon import NumpyDocstring + + +def _process_return(lines: Iterable[str]) -> Generator[str, None, None]: + for line in lines: + if m := re.fullmatch(r"(?P\w+)\s+:\s+(?P[\w.]+)", line): + yield f"-{m['param']} (:class:`~{m['type']}`)" + else: + yield line + + +def _parse_returns_section(self: NumpyDocstring, section: str) -> list[str]: + lines_raw = self._dedent(self._consume_to_next_section()) + if lines_raw[0] == ":": + del lines_raw[0] + lines = self._format_block(":returns: ", list(_process_return(lines_raw))) + if lines and lines[-1]: + lines.append("") + return lines + + +def setup(app: Sphinx): + """Set app.""" + NumpyDocstring._parse_returns_section = _parse_returns_section diff --git a/pilot_project_tcr_analysis.py b/pilot_project_tcr_analysis.py deleted file mode 100644 index 3fa605421..000000000 --- a/pilot_project_tcr_analysis.py +++ /dev/null @@ -1,132 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - -from scTCRpy.utils import stampTime -from scTCRpy.layoutExp import eXperiment -from scTCRpy.reportHTML import htmlReporter -from scTCRpy import talkR -from scTCRpy import resultData - -def __main__(): - sample_features = [ - ['', 'Sample1', 'Sample_1_', 'pilot_VDJ1/outs', 'pilot_count1/call-count/shard-0/execution/pilot_count1_lane1/outs'], - ['', 'Sample2', 'Sample_2_', 'pilot_VDJ2/outs', 'pilot_count2/call-count/shard-0/execution/pilot_count2_lane1/outs'], - ['', 'Sample3', 'Sample_3_', 'pilot_VDJ3/outs', 'pilot_count3/call-count/shard-0/execution/pilot_count3_lane1/outs'], - ['', 'Sample4', 'Sample_4_', 'pilot_VDJ4/outs', 'pilot_count4/call-count/shard-0/execution/pilot_count4_lane1/outs'] - ] - starttime = stampTime('Starting execution at') - #Create an empty layout structure, set top folder for files and initialize main data containers (also offering a possibility to pass prepopulated data containers form upstream steps) - experiment = eXperiment(projectRoot='/home/singlecell/singleCellSeq/results/Giorgos/pilot_project', outDir='tcr_out', reportFile='TCR_pilot_report.html', genListFile='integrate/genelist.txt', criticalClonotypeSize=6, verbose=True) - #Add samples to the experiment by defining name, barcode prefix and file paths needed for downstream steps - preprocesstime = stampTime('Preprocessing sample data') - for a1, a2, a3, a4, a5 in sample_features: - experiment.addSample(a1, a2, a3, t_tcrDir=a4, t_gexDir=a5) - timepoint = stampTime('Parsed basic sample info.', before=preprocesstime) - #Reassign clonotypes based on the dominant TCR chain pair - experiment.cellTCR.reassessClonotypes() - timepoint = stampTime('Reassigned clonotypes.', before=timepoint) - #Add some two-dimensional projections of cell populations - primarily based on gene expression, but theoratically any coordinate collection can be imported - experiment.addProjection('cells', proFile='integrate/celltypes.txt') - experiment.addProjection('tSNE', coordFile='integrate/tsne.csv', annotFile='integrate/a_tsne.tsv') - experiment.addProjection('umap', coordFile='integrate/umap.csv', annotFile='integrate/a_umap.tsv') - timepoint = stampTime('Cell population projections added.', before=timepoint) - #A possible method to claculate distances is the sequence similarity of the CDR3 regions, a method reimplemented from tcrDist - experiment.distanceCalc('seqSim', metric='TCR') - timepoint = stampTime('Distance matrix based on TCR sequences calculated.', before=timepoint) - timepoint = stampTime('First round of data preprocessing finised.', before=preprocesstime) - #Export data to tables for R scripts and other third party analysis. while recollecting diversity and Kidera calculations from R - talkrtime = stampTime('Starting data exchange with R.') - talkR.featRchain(experiment) - talkR.exportCellFeatures(experiment) - talkR.groupDiversity(experiment) - stampTime('Diversity scores and Kidera factors called with R scripts.', before=talkrtime) - #Another distance matrix could also be calculated, based on Kidera factors of the CDR3 regions of the dominant TCR pair - experiment.distanceCalc('Kidera', metric='Kidera') #Infinite distances after calculation, fix it later! - timepoint = stampTime('Distance matrix based on Kidera factors calculated.', before=timepoint) - #After all data is preprocessed, final calculations and visualizations can be done - reporttime = stampTime('Starting to plot graphs for the report file.') - reporter = htmlReporter(experiment.reportFile, template='/home/singlecell/scripts/Tamas/scTCRpy/report_template.html', saveres=150, savepics=True) - #Some intital setup of html is needed - html_frame = [ - ('title', {'div': {'class': 'main_title', 'innerHTML': 'Analysis of TCR sequencing results'}}), - ('side_bar', {'div': {'class': 'side_content'}}), - ('content', {'div': {'class': 'main_content'}}) - ] - reporter.add(html_frame) - timepoint = stampTime('Report file initialized', before=timepoint) - #Add a clonotype frequency table - clonotype_table = resultData.clonoTable(experiment) - reporter.add(**clonotype_table.html()) - timepoint = stampTime('Clonotype tables created for the report', before=reporttime) - #Plot the overlap between clonotypes of the two samples - clonoverlap = (resultData.clonOverlap(experiment)) - reporter.add(**clonoverlap.html()) - timepoint = stampTime('Clonotype overlap plotted', before=timepoint) - #Add a tab for basic TCR statistics - tcr_stats = { - 'div': { - 'class': 'toggle_tab', - 'id': 'tcr_stats' - }, - 'parent': 'content', - 'sidelabel': [ - 'side_bar', 'button_tcr_stats', - { - 'innerHTML': 'TCR chain statistics', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, 'tcr_stats')" - } - ] - } - reporter.add('tcr_stats', **tcr_stats) - #Plot CDR3 amino acid lengths - lena_tcr = resultData.lenCdrAa(experiment) - reporter.add(**lena_tcr.html()) - timepoint = stampTime('CDR3 amino acid lengths plotted', before=timepoint) - #Plot CDR3 nucleotide lengths - lent_tcr = resultData.lenCdrNt(experiment) - reporter.add(**lent_tcr.html()) - timepoint = stampTime('CDR3 nucleotide lengths plotted', before=timepoint) - #Plot added nucleotides - adn_tcr = resultData.lenCdrIns(experiment) - reporter.add(**adn_tcr.html()) - timepoint = stampTime('CDR3 added nucleotides plotted', before=timepoint) - #Plot TCR chain expression - xpr_tcr = resultData.exprTVio(experiment) - reporter.add(**xpr_tcr.html()) - timepoint = stampTime('TCR chain expression levels plotted', before=timepoint) - #Plot TCR chain pairing in samples and cell types - orhpan_tcr = resultData.orphanBar(experiment) - reporter.add(**orhpan_tcr.html()) - #Show VDJ segment usage and frequency of segments - seg_use = resultData.segmentBar(experiment) - reporter.add(**seg_use.html()) - timepoint = stampTime('VDJ segment usage plotted', before=timepoint) - #Plot clonotypes and cell types over a projection of cell features (mainly cell types, based on gene expression) - cl_project = resultData.clonTopology(experiment) - reporter.add(**cl_project.html()) - timepoint = stampTime('Clonotype projection plotted', before=timepoint) - #Show diversity of samples and cell types - group_div = resultData.diverBar(experiment) - reporter.add(**group_div.html()) - timepoint = stampTime('Diversity of sample groups plotted', before=timepoint) - timepoint = stampTime('TCR chain pairing stats plotted', before=timepoint) - #Show how the cells clustered based on distance metrics - cell_dist = resultData.clonoDist(experiment) - reporter.add(**cell_dist.html()) - timepoint = stampTime('T cells clustered based on TCR similarity', before=timepoint) - #Plot TCR based distances between cell types - type_dist = resultData.cellTypeDist(experiment) - reporter.add(**type_dist.html()) - timepoint = stampTime('T cell types clustered based on TCR similarity', before=timepoint) - #Plot the expression of top genes in clonotype groups - top_gex = resultData.clonoGex(experiment) - reporter.add(**top_gex.html()) - timepoint = stampTime('Expression of top genes plotted', before=timepoint) - reporter.report() - stampTime('Report compiled to HTML file ' + experiment.reportFile, before=reporttime) - stampTime('TCR results successfully analysed, script execution ends.', before=starttime) - return - -if __name__ == '__main__': - __main__() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..505845666 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,135 @@ +[build-system] +build-backend = "hatchling.build" +requires = [ "hatchling" ] + +[project] +name = "scirpy" +version = "0.0.1" +description = "A very interesting piece of code" +readme = "README.md" +license = { file = "LICENSE" } +maintainers = [ + { name = "Gregor Sturm", email = "mail@gregor-sturm.de" }, +] +authors = [ + { name = "Gregor Sturm" }, +] +requires-python = ">=3.10" +classifiers = [ + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "anndata", + # for debug logging (referenced from the issue template) + "session-info2", +] +optional-dependencies.dev = [ + "pre-commit", + "twine>=4.0.2", +] +optional-dependencies.doc = [ + "docutils>=0.8,!=0.18.*,!=0.19.*", + "ipykernel", + "ipython", + "myst-nb>=1.1", + "pandas", + # Until pybtex >0.24.0 releases: https://bitbucket.org/pybtex-devs/pybtex/issues/169/ + "setuptools", + "sphinx>=4", + "sphinx-autodoc-typehints", + "sphinx-book-theme>=1", + "sphinx-copybutton", + "sphinx-tabs", + "sphinxcontrib-bibtex>=1", + "sphinxext-opengraph", +] +optional-dependencies.test = [ + "coverage", + "pytest", +] +# https://docs.pypi.org/project_metadata/#project-urls +urls.Documentation = "https://scirpy.readthedocs.io/" +urls.Homepage = "https://github.com/grst/scirpy" +urls.Source = "https://github.com/grst/scirpy" + +[tool.hatch.envs.default] +installer = "uv" +features = [ "dev" ] + +[tool.hatch.envs.docs] +features = [ "doc" ] +scripts.build = "sphinx-build -M html docs docs/_build {args}" +scripts.open = "python -m webbrowser -t docs/_build/html/index.html" +scripts.clean = "git clean -fdX -- {args:docs}" + +[tool.hatch.envs.hatch-test] +features = [ "test" ] + +[tool.ruff] +line-length = 120 +src = [ "src" ] +extend-include = [ "*.ipynb" ] + +format.docstring-code-format = true + +lint.select = [ + "B", # flake8-bugbear + "BLE", # flake8-blind-except + "C4", # flake8-comprehensions + "D", # pydocstyle + "E", # Error detected by Pycodestyle + "F", # Errors detected by Pyflakes + "I", # isort + "RUF100", # Report unused noqa directives + "TID", # flake8-tidy-imports + "UP", # pyupgrade + "W", # Warning detected by Pycodestyle +] +lint.ignore = [ + "B008", # Errors from function calls in argument defaults. These are fine when the result is immutable. + "D100", # Missing docstring in public module + "D104", # Missing docstring in public package + "D105", # __magic__ methods are often self-explanatory, allow missing docstrings + "D107", # Missing docstring in __init__ + # Disable one in each pair of mutually incompatible rules + "D203", # We don’t want a blank line before a class docstring + "D213", # <> We want docstrings to start immediately after the opening triple quote + "D400", # first line should end with a period [Bug: doesn’t work with single-line docstrings] + "D401", # First line should be in imperative mood; try rephrasing + "E501", # line too long -> we accept long comment lines; formatter gets rid of long code lines + "E731", # Do not assign a lambda expression, use a def -> lambda expression assignments are convenient + "E741", # allow I, O, l as variable names -> I is the identity matrix +] +lint.per-file-ignores."*/__init__.py" = [ "F401" ] +lint.per-file-ignores."docs/*" = [ "I" ] +lint.per-file-ignores."tests/*" = [ "D" ] +lint.pydocstyle.convention = "numpy" + +[tool.pytest.ini_options] +testpaths = [ "tests" ] +xfail_strict = true +addopts = [ + "--import-mode=importlib", # allow using test files with same name +] + +[tool.coverage.run] +source = [ "scirpy" ] +omit = [ + "**/test_*.py", +] + +[tool.cruft] +skip = [ + "tests", + "src/**/__init__.py", + "src/**/basic.py", + "docs/api.md", + "docs/changelog.md", + "docs/references.bib", + "docs/references.md", + "docs/notebooks/example.ipynb", +] diff --git a/runTCR.sge b/runTCR.sge deleted file mode 100644 index 66b518e46..000000000 --- a/runTCR.sge +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/sh - -#$ -S /bin/sh - -#$ -pe smp 8 -#$ -cwd -#$ -V -#$ -N scTCR - -#### Error Outputfile -#$ -e ./LOGS/$JOB_NAME-$JOB_ID.err -#$ -o ./LOGS/$JOB_NAME-$JOB_ID.log - -#### Resubmit -#$ -r y - -hostname - -source /usr/local/bioinf/conda/bin/activate tcr `#Some dependencies of the python script (biopython, scikit-learn, seaborn) are installed in a conda environment` -python basic_tcr.py `#This python script boundles athe currently impemented TCR anlysis steps` \ - /home/singlecell/singleCellSeq/results/Giorgos/pilot_project `#Most files and paths will be retrieved within this project home directiory` \ - cmd_tcr_out `#The folder where output files can be saved (relative to project home)` \ - tcr_samples.txt `#The input file, containing path and naming information about samples (relative to project home, each line is a separate sample)` \ - integrate/celltypes.txt `#Coordinates of an annotated tsne or umap created by a previous analysis step` \ - integrate/genelist.txt `#List of genes of interest (T cell activation genes or genes diferentially expressed according to previous analysis)` \ - TCR_pilot_report.html `#The name of the main output file` \ - /home/singlecell/scripts/Tamas/scTCRpy/report_template.html `#The html template to use when assembling the report (path not relative to the project home directory)` -source /usr/local/bioinf/conda/bin/deactivate tcr \ No newline at end of file diff --git a/sample_data_for_tsne/celltypes.txt b/sample_data_for_tsne/celltypes.txt deleted file mode 100644 index 2081d7da9..000000000 --- a/sample_data_for_tsne/celltypes.txt +++ /dev/null @@ -1,3116 +0,0 @@ -Sample_1_AAACCTGTCTGCTGTC-1 -0.463559031486511 0.27128928899765 CD8+ Tem -Sample_1_AAACGGGGTTTGCATG-1 -3.87942218780518 8.66335678100586 CD8+ Tcm -Sample_1_AAACGGGTCCTTTACA-1 4.32222509384155 5.85532712936401 CD8+ Tem -Sample_1_AAAGTAGCAAACAACA-1 -2.52073574066162 7.39792203903198 CD8+ T-cells -Sample_1_AAAGTAGCATAGAAAC-1 -2.83736324310303 7.09420871734619 CD8+ T-cells -Sample_1_AAATGCCCATGATCCA-1 2.83420872688293 6.36483526229858 CD8+ Tem -Sample_1_AAATGCCGTTAGTGGG-1 -1.20449864864349 7.43769502639771 CD8+ Tcm -Sample_1_AAATGCCTCAGTTAGC-1 2.44457602500916 6.51109457015991 CD8+ Tem -Sample_1_AACACGTGTCGCTTCT-1 3.0569281578064 6.97420263290405 CD4+ Tem -Sample_1_AACCATGGTCTCACCT-1 -1.00512826442719 4.89711999893188 CD8+ Tcm -Sample_1_AACTCAGGTACCGTAT-1 1.63045561313629 -10.2235765457153 NK cells -Sample_1_AACTCAGGTCCATCCT-1 -0.514523267745972 -8.9795503616333 NK cells -Sample_1_AACTCAGGTCGCTTCT-1 2.7136561870575 7.42743873596191 CD8+ Tem -Sample_1_AACTCAGTCCTTTCTC-1 1.52632403373718 -10.1818990707397 NK cells -Sample_1_AACTCTTCAAGTAATG-1 2.9951708316803 -7.35612678527832 NK cells -Sample_1_AACTCTTCACACATGT-1 -0.302775233983994 2.80618619918823 CD8+ Tem -Sample_1_AACTCTTGTGCCTGTG-1 -3.84051656723022 8.87424945831299 CD4+ T-cells -Sample_1_AACTCTTTCAAACAAG-1 2.9142427444458 -10.4455261230469 NK cells -Sample_1_AACTCTTTCCTCGCAT-1 -0.765527725219727 0.791369080543518 CD8+ Tem -Sample_1_AACTGGTGTGCGGTAA-1 2.92453765869141 6.73920774459839 CD8+ Tem -Sample_1_AACTTTCGTAGCGATG-1 2.16546869277954 -11.2901582717896 NK cells -Sample_1_AAGACCTAGATGTCGG-1 4.41119146347046 6.45614290237427 CD4+ Tem -Sample_1_AAGCCGCAGCTGAACG-1 2.89784550666809 -7.08886766433716 NK cells -Sample_1_AAGCCGCGTTCGCGAC-1 1.7858579158783 -4.43002367019653 CD8+ Tcm -Sample_1_AAGGAGCCAATACGCT-1 2.30580568313599 8.25133419036865 CD8+ Tem -Sample_1_AAGGCAGAGAGGTAGA-1 -0.490111291408539 6.93663835525513 CD4+ Tcm -Sample_1_AAGGCAGAGGAATCGC-1 -2.19970774650574 6.22334337234497 CD4+ T-cells -Sample_1_AAGGTTCGTCATATGC-1 -2.30830264091492 8.37007999420166 CD8+ T-cells -Sample_1_AAGGTTCTCAGTGCAT-1 2.9704282283783 6.91797733306885 CD8+ Tem -Sample_1_AAGTCTGAGAGGTAGA-1 2.35080862045288 -4.71541213989258 NK cells -Sample_1_AATCCAGCAAGCCCAC-1 1.57670319080353 -10.2813453674316 NK cells -Sample_1_AATCCAGGTAAGCACG-1 -0.579985201358795 1.02347385883331 CD8+ Tem -Sample_1_AATCCAGTCACAACGT-1 2.78580284118652 4.79348230361938 CD8+ Tem -Sample_1_AATCGGTCATCACAAC-1 4.20543670654297 5.05584049224854 CD8+ Tem -Sample_1_ACACCCTCAGCTGTGC-1 2.89558959007263 -9.03116321563721 NK cells -Sample_1_ACACCCTCATACCATG-1 1.22310066223145 -7.33824348449707 NK cells -Sample_1_ACACCCTGTCACAAGG-1 4.37484931945801 -8.38545608520508 NK cells -Sample_1_ACACTGAAGCTGAACG-1 -1.99516558647156 6.16904783248901 CD4+ T-cells -Sample_1_ACACTGATCTTACCTA-1 1.20480942726135 -9.90668106079102 NK cells -Sample_1_ACAGCCGAGCTACCGC-1 -0.381799399852753 -10.9794597625732 NK cells -Sample_1_ACAGCTAAGGCAGTCA-1 -0.0859443992376328 -8.84190940856934 NK cells -Sample_1_ACATACGAGAGAACAG-1 1.7825425863266 5.01163482666016 CD8+ Tem -Sample_1_ACATACGAGGCTAGAC-1 1.35231447219849 2.41255879402161 CD8+ Tem -Sample_1_ACATACGTCAACCAAC-1 4.16772651672363 -9.82475566864014 NK cells -Sample_1_ACATCAGAGGTCGGAT-1 1.66140508651733 -7.63435506820679 NK cells -Sample_1_ACATCAGGTCCGTTAA-1 3.62950134277344 -9.52772235870361 NK cells -Sample_1_ACATGGTGTCGCTTCT-1 4.87885332107544 6.55304479598999 CD8+ Tem -Sample_1_ACCAGTACACAACTGT-1 -1.2013772726059 6.50358581542969 CD8+ Tcm -Sample_1_ACCAGTATCCTCGCAT-1 4.86340761184692 5.87212419509888 CD4+ Tem -Sample_1_ACCAGTATCTGAAAGA-1 1.89495253562927 -7.85774993896484 NK cells -Sample_1_ACCGTAACAAGAAGAG-1 -1.87007737159729 8.63883876800537 CD8+ Tcm -Sample_1_ACCGTAACATTGAGCT-1 0.52234297990799 4.50984239578247 CD8+ Tcm -Sample_1_ACCGTAATCGCTTGTC-1 4.89007759094238 5.91274833679199 CD8+ Tem -Sample_1_ACCTTTAAGCGATATA-1 -1.86044549942017 7.94542264938354 CD8+ T-cells -Sample_1_ACGAGCCAGACCTAGG-1 -1.99892485141754 9.27174377441406 CD4+ T-cells -Sample_1_ACGAGCCGTCTAGTCA-1 -0.87776255607605 -9.37428665161133 NK cells -Sample_1_ACGATACAGAGACGAA-1 3.76665472984314 4.80968570709229 CD8+ Tem -Sample_1_ACGATACCAATACGCT-1 4.11595726013184 -9.97762012481689 NK cells -Sample_1_ACGATACGTCTGCCAG-1 -0.239020630717278 9.14091396331787 CD8+ T-cells -Sample_1_ACGATGTAGAGCTTCT-1 3.40364956855774 4.16323041915894 CD8+ Tem -Sample_1_ACGATGTCATTCACTT-1 3.12285161018372 5.89504671096802 CD4+ Tem -Sample_1_ACGATGTGTCATATCG-1 -0.21198607981205 4.21640062332153 CD8+ Tcm -Sample_1_ACGCAGCCAAGCGCTC-1 3.37458658218384 5.70337057113647 CD8+ Tcm -Sample_1_ACGCCGAAGGTGTTAA-1 3.40716099739075 -9.86660766601562 NK cells -Sample_1_ACGGAGAAGACAGAGA-1 -0.854538023471832 4.93078136444092 CD8+ Tcm -Sample_1_ACGGCCAGTTGATTCG-1 -1.43822741508484 1.23847079277039 CD8+ Tem -Sample_1_ACGTCAAAGGTAGCTG-1 2.3867359161377 -9.03950023651123 NK cells -Sample_1_ACGTCAATCGGAGCAA-1 3.88439106941223 -9.98717594146729 NK cells -Sample_1_ACGTCAATCGGCATCG-1 4.09003639221191 6.83379936218262 CD4+ Tem -Sample_1_ACTATCTAGCTACCTA-1 1.3778247833252 -9.50332736968994 NK cells -Sample_1_ACTGAACGTCAATGTC-1 -1.31208682060242 8.78950786590576 CD8+ T-cells -Sample_1_ACTGAACTCCCAACGG-1 -1.715008020401 9.34720039367676 CD8+ T-cells -Sample_1_ACTGCTCCATTAGCCA-1 3.59497141838074 -9.22525024414062 NK cells -Sample_1_ACTGTCCAGAAGAAGC-1 3.47580194473267 3.53710198402405 CD8+ Tem -Sample_1_ACTGTCCGTACCGCTG-1 3.10143852233887 4.90060234069824 CD8+ Tem -Sample_1_ACTTACTAGCCGGTAA-1 -0.796901047229767 6.40911626815796 CD4+ Tcm -Sample_1_ACTTACTGTACCGGCT-1 3.41064858436584 -10.4163045883179 NK cells -Sample_1_ACTTGTTCAGATGGCA-1 2.44819450378418 -7.75481367111206 NK cells -Sample_1_ACTTGTTTCACGATGT-1 1.68618011474609 8.50193023681641 CD8+ Tcm -Sample_1_ACTTTCACACAGTCGC-1 -1.58031845092773 8.89341640472412 CD8+ T-cells -Sample_1_ACTTTCAGTAAGGATT-1 3.63461065292358 3.61460614204407 CD8+ Tem -Sample_1_ACTTTCATCAGTTGAC-1 -2.96750545501709 8.35685729980469 CD4+ T-cells -Sample_1_AGAATAGTCAACACCA-1 0.611754596233368 -10.0023012161255 NK cells -Sample_1_AGAGCGAAGGTCATCT-1 2.75124311447144 7.61185932159424 CD8+ Tem -Sample_1_AGAGCGAGTACCGGCT-1 2.41421580314636 -9.17477893829346 NK cells -Sample_1_AGAGCTTAGGAGTTTA-1 0.892576396465302 -9.83121204376221 NK cells -Sample_1_AGAGCTTTCAAGAAGT-1 1.82870221138 -7.26447677612305 NK cells -Sample_1_AGAGTGGTCCAGTAGT-1 -2.29790854454041 8.93339920043945 CD8+ Tcm -Sample_1_AGAGTGGTCCTTGACC-1 -2.19172215461731 6.55649757385254 CD8+ T-cells -Sample_1_AGCAGCCAGTTCCACA-1 -1.50989127159119 6.82952260971069 CD4+ T-cells -Sample_1_AGCCTAACAGTCTTCC-1 3.98323559761047 -8.7066125869751 NK cells -Sample_1_AGCGTATAGTGCAAGC-1 4.71624374389648 6.30585432052612 CD8+ Tem -Sample_1_AGCGTATGTGACGGTA-1 3.66984796524048 -9.25009441375732 NK cells -Sample_1_AGCGTCGCAGCGAACA-1 2.36180734634399 7.81974792480469 CD8+ Tem -Sample_1_AGCTCCTTCAGAGCTT-1 0.290029168128967 -11.4322910308838 NK cells -Sample_1_AGCTCTCCATTTGCCC-1 2.37827014923096 -4.85275459289551 CD8+ Tcm -Sample_1_AGCTTGAAGACTAGAT-1 2.33547353744507 -8.53888416290283 NK cells -Sample_1_AGCTTGAGTCTCACCT-1 -2.96331191062927 7.83878087997437 CD4+ T-cells -Sample_1_AGGCCACCAAGCGAGT-1 4.79220294952393 -9.06209850311279 NK cells -Sample_1_AGGCCACCAAGTAATG-1 4.32549381256104 -9.20642566680908 NK cells -Sample_1_AGGCCACCACGGCTAC-1 0.608061075210571 2.32701349258423 CD8+ Tem -Sample_1_AGGCCACTCTCTTATG-1 1.36667680740356 -10.2177753448486 NK cells -Sample_1_AGGCCGTGTAGAGGAA-1 3.80969762802124 5.14175653457642 CD8+ Tem -Sample_1_AGGCCGTTCATCGATG-1 5.14268684387207 6.6541543006897 CD8+ Tem -Sample_1_AGGGATGGTCTCAACA-1 -0.559553146362305 0.06999322026968 NK cells -Sample_1_AGGGTGACAGTAAGAT-1 2.48358726501465 -9.95826053619385 NK cells -Sample_1_AGGTCATCACAGACTT-1 4.25397348403931 -9.62050151824951 NK cells -Sample_1_AGGTCATTCGAGAGCA-1 0.133194997906685 -8.35497570037842 NK cells -Sample_1_AGGTCCGCAGCTGTGC-1 2.11953234672546 -4.55000448226929 NK cells -Sample_1_AGTAGTCTCACTCCTG-1 1.67542850971222 4.74582529067993 CD8+ Tem -Sample_1_AGTCTTTAGCCTCGTG-1 -1.05278253555298 2.26723885536194 CD8+ Tem -Sample_1_AGTCTTTAGGATTCGG-1 -0.934557914733887 4.58750152587891 CD8+ Tcm -Sample_1_AGTGAGGAGGCTCTTA-1 1.2304470539093 -9.87077236175537 NK cells -Sample_1_AGTGAGGGTGCACCAC-1 2.18969774246216 8.21578788757324 CD8+ Tem -Sample_1_AGTGAGGTCTGATACG-1 0.658913910388947 3.1486120223999 CD8+ Tcm -Sample_1_AGTGTCATCCTTTCGG-1 -0.80131596326828 2.04837608337402 CD8+ Tem -Sample_1_AGTGTCATCTGGGCCA-1 -1.01643002033234 -8.09378528594971 NK cells -Sample_1_AGTGTCATCTTACCTA-1 -0.566655457019806 0.61968857049942 CD8+ Tem -Sample_1_AGTTGGTTCGTCCAGG-1 4.33685684204102 5.429358959198 CD8+ Tem -Sample_1_ATAACGCAGTGGCACA-1 2.5239634513855 -4.92396926879883 NK cells -Sample_1_ATAACGCCAGTCAGAG-1 0.371875822544098 2.07680678367615 CD8+ Tcm -Sample_1_ATAACGCGTAGCTTGT-1 -1.60940623283386 7.8127613067627 CD8+ T-cells -Sample_1_ATAAGAGGTGCCTGGT-1 3.53421878814697 -10.0646486282349 NK cells -Sample_1_ATAGACCTCTTTACGT-1 1.67540168762207 -9.05197238922119 NK cells -Sample_1_ATCACGAAGTGGAGTC-1 -0.478406876325607 3.94769144058228 CD8+ Tcm -Sample_1_ATCACGATCAGCAACT-1 -2.78579711914062 6.89767646789551 CD8+ T-cells -Sample_1_ATCATCTCAACACGCC-1 -2.87770104408264 6.81166648864746 CD4+ T-cells -Sample_1_ATCATGGAGGTGATAT-1 -0.571381688117981 -10.2016582489014 NK cells -Sample_1_ATCATGGCAGCCAGAA-1 -9.65121555328369 -2.74186873435974 naive B-cells -Sample_1_ATCCACCAGTAGATGT-1 -0.778923690319061 -9.53645420074463 NK cells -Sample_1_ATCCACCCATCGATGT-1 -9.58588218688965 -2.67464256286621 Memory B-cells -Sample_1_ATCCACCGTAACGACG-1 1.49840748310089 3.00287699699402 CD8+ Tem -Sample_1_ATCCGAAAGAAGATTC-1 2.42451906204224 -10.7047071456909 NK cells -Sample_1_ATCCGAACACTAGTAC-1 -0.443780660629272 -10.2637128829956 NK cells -Sample_1_ATCCGAAGTACACCGC-1 1.71249973773956 -8.08173179626465 NK cells -Sample_1_ATCGAGTCACAACTGT-1 3.53052830696106 6.44539260864258 CD8+ Tem -Sample_1_ATCTACTCACGAGGTA-1 0.171856760978699 -9.60459995269775 NK cells -Sample_1_ATCTACTCATGGGACA-1 0.487343281507492 6.31212997436523 CD4+ Tem -Sample_1_ATCTACTTCGGCCGAT-1 -1.27239167690277 8.23403739929199 CD8+ T-cells -Sample_1_ATCTGCCAGCGCTCCA-1 2.41461968421936 5.79222536087036 CD8+ Tem -Sample_1_ATCTGCCAGTGACATA-1 3.23415112495422 -8.87864112854004 NK cells -Sample_1_ATCTGCCAGTTGAGTA-1 -0.203844889998436 -9.76652526855469 NK cells -Sample_1_ATCTGCCCACTTAACG-1 1.18754458427429 5.20887327194214 CD8+ Tem -Sample_1_ATCTGCCCATCCCACT-1 1.2210282087326 -8.01252555847168 NK cells -Sample_1_ATCTGCCGTCTCTTTA-1 -0.52409040927887 2.92110419273376 CD8+ Tcm -Sample_1_ATCTGCCGTTAGTGGG-1 0.853027939796448 8.41493511199951 CD8+ Tem -Sample_1_ATCTGCCTCGCTTAGA-1 0.624929487705231 3.6301646232605 CD8+ Tcm -Sample_1_ATGAGGGGTACCGTAT-1 -1.31970632076263 0.115629270672798 CD8+ Tem -Sample_1_ATGCGATGTTCTGTTT-1 3.36940979957581 5.52611446380615 CD8+ Tem -Sample_1_ATGGGAGAGTTGTAGA-1 3.09861612319946 -6.92862510681152 NK cells -Sample_1_ATGTGTGAGCATCATC-1 1.22756969928741 -10.415002822876 NK cells -Sample_1_ATGTGTGAGGTCATCT-1 0.815921008586884 -9.03110218048096 NK cells -Sample_1_ATGTGTGGTGTATGGG-1 3.31167316436768 4.20487689971924 CD8+ Tcm -Sample_1_ATTACTCAGGTCATCT-1 -0.54111236333847 -10.6409482955933 NK cells -Sample_1_ATTACTCGTGGAAAGA-1 1.77862846851349 -4.24353885650635 CD8+ Tcm -Sample_1_ATTATCCAGTAGCGGT-1 -0.0804253444075584 -10.2319936752319 NK cells -Sample_1_ATTATCCCACCCATGG-1 4.83283758163452 6.19515705108643 CD8+ Tcm -Sample_1_ATTCTACCAAGCGATG-1 -0.445504128932953 3.66627717018127 CD8+ Tcm -Sample_1_ATTCTACCATTAACCG-1 1.09785544872284 5.87448215484619 CD8+ Tem -Sample_1_ATTCTACGTCTAGTCA-1 1.33598470687866 -7.4315447807312 NK cells -Sample_1_ATTCTACGTGGTAACG-1 -1.65370309352875 0.284121036529541 CD8+ Tem -Sample_1_ATTGGACCAGCGTTCG-1 -1.78101325035095 0.713341593742371 CD8+ Tem -Sample_1_ATTGGACCAGCTTCGG-1 1.49016416072845 2.39981245994568 CD8+ Tem -Sample_1_ATTGGACGTAGCGTGA-1 -2.39092779159546 8.75310325622559 CD4+ T-cells -Sample_1_ATTGGACGTCGCATCG-1 -0.568874716758728 -7.45785522460938 NK cells -Sample_1_ATTGGTGAGCCCGAAA-1 3.87436842918396 8.06886005401611 CD8+ Tcm -Sample_1_CAACCAATCCTTGGTC-1 1.85938584804535 -10.6938591003418 NK cells -Sample_1_CAACTAGCATGGTAGG-1 0.444642275571823 -10.9781732559204 NK cells -Sample_1_CAACTAGTCGCATGGC-1 3.67217469215393 4.44149017333984 NK cells -Sample_1_CAAGAAAAGACTAAGT-1 -0.461171239614487 -10.0358076095581 NK cells -Sample_1_CAAGAAAAGTTAGCGG-1 -0.913442373275757 -9.3472204208374 NK cells -Sample_1_CAAGAAATCTATGTGG-1 2.4080605506897 -7.9039249420166 NK cells -Sample_1_CAAGATCGTTACTGAC-1 -0.705993413925171 0.502585411071777 CD8+ Tem -Sample_1_CAAGATCGTTCAACCA-1 4.06773996353149 -9.06484794616699 NK cells -Sample_1_CAAGATCGTTCAGCGC-1 3.37582325935364 6.59067440032959 CD8+ Tem -Sample_1_CAAGATCGTTTAGGAA-1 -2.89604735374451 8.69244861602783 CD8+ T-cells -Sample_1_CAAGTTGGTAACGCGA-1 -2.18872356414795 6.14468860626221 CD8+ T-cells -Sample_1_CACAAACAGTTCGATC-1 3.33541393280029 4.73566913604736 CD8+ Tem -Sample_1_CACACAACAATCACAC-1 -1.42048728466034 8.60146808624268 CD8+ T-cells -Sample_1_CACACAAGTCGCGTGT-1 -0.36109921336174 6.46589708328247 CD8+ Tcm -Sample_1_CACACCTCAAACCTAC-1 -0.0752605050802231 -11.2059812545776 NK cells -Sample_1_CACACCTTCCCATTTA-1 3.69698071479797 -9.83880805969238 NK cells -Sample_1_CACACTCCAGTACACT-1 0.302799552679062 -9.39656639099121 NK cells -Sample_1_CACACTCCATGTAAGA-1 -1.15875506401062 9.82337951660156 CD4+ T-cells -Sample_1_CACACTCTCTAACCGA-1 1.15156519412994 1.41201436519623 CD8+ Tem -Sample_1_CACAGGCGTGGGTATG-1 1.74510419368744 -11.1257190704346 NK cells -Sample_1_CACAGTAAGTGACATA-1 -1.72241485118866 0.955516397953033 CD8+ Tem -Sample_1_CACATAGCAAGCCGCT-1 -2.08288741111755 9.1315860748291 CD4+ T-cells -Sample_1_CACATAGGTAGCAAAT-1 -0.27866131067276 -10.177417755127 NK cells -Sample_1_CACATAGTCGCGTTTC-1 -2.39640355110168 7.1630334854126 CD8+ T-cells -Sample_1_CACATTTCATTGCGGC-1 -0.480587512254715 7.12131977081299 CD8+ Tcm -Sample_1_CACATTTTCACGAAGG-1 4.37657451629639 -9.47565937042236 CD8+ Tem -Sample_1_CACATTTTCCGTCATC-1 -2.06394028663635 8.14588832855225 CD4+ T-cells -Sample_1_CACCACTCACTCGACG-1 -0.0998870208859444 2.10886740684509 CD8+ Tem -Sample_1_CACCACTTCATACGGT-1 1.23085379600525 -8.50186920166016 NK cells -Sample_1_CACCACTTCCGCAAGC-1 1.76651978492737 -9.63887405395508 NK cells -Sample_1_CACCACTTCTTGAGAC-1 1.33997654914856 -8.02445125579834 NK cells -Sample_1_CACCAGGCACCTCGTT-1 -1.77731072902679 0.249673694372177 CD8+ Tem -Sample_1_CACCAGGTCAAGGTAA-1 1.93451154232025 -5.20873880386353 NK cells -Sample_1_CACCTTGTCTCTAAGG-1 0.348949998617172 5.99846458435059 CD4+ Tcm -Sample_1_CAGAGAGCATCACGTA-1 0.0168837998062372 5.74600076675415 Tregs -Sample_1_CAGATCAAGATATGGT-1 4.78597164154053 5.06462812423706 CD8+ Tem -Sample_1_CAGCAGCGTCGCCATG-1 0.0234979223459959 8.53036022186279 CD4+ Tcm -Sample_1_CAGCAGCTCGGAATCT-1 -9.7459831237793 -2.83713102340698 naive B-cells -Sample_1_CAGCAGCTCTGTCCGT-1 5.17809438705444 6.07472658157349 CD8+ Tcm -Sample_1_CAGCCGAAGCGACGTA-1 1.78307318687439 -8.52631950378418 NK cells -Sample_1_CAGCGACAGTAGCGGT-1 1.97281169891357 -7.80335140228271 NK cells -Sample_1_CAGCTGGAGTGTACGG-1 2.36268353462219 -8.91238784790039 NK cells -Sample_1_CAGCTGGTCTTTCCTC-1 0.594456553459167 -10.2626962661743 NK cells -Sample_1_CAGTAACCATCTCGCT-1 -1.1311844587326 2.66928648948669 CD8+ Tcm -Sample_1_CAGTAACTCTGTCCGT-1 -0.565705716609955 9.11149883270264 CD8+ T-cells -Sample_1_CAGTCCTAGACAAGCC-1 1.2447783946991 -7.52887678146362 NK cells -Sample_1_CAGTCCTAGATCTGCT-1 -2.33919215202332 7.74161624908447 CD8+ T-cells -Sample_1_CAGTCCTAGTAGATGT-1 3.889319896698 4.13125944137573 CD8+ Tem -Sample_1_CAGTCCTCAGGCGATA-1 3.58737468719482 6.17286729812622 CD4+ Tem -Sample_1_CAGTCCTGTTCAGCGC-1 3.10988306999207 4.3418083190918 CD8+ Tem -Sample_1_CAGTCCTTCTCTTGAT-1 3.84278798103333 4.96447801589966 CD8+ Tcm -Sample_1_CATATGGTCTGTGCAA-1 2.00363111495972 -4.90690517425537 NK cells -Sample_1_CATATTCCAGTGACAG-1 -2.58675384521484 6.67126178741455 CD8+ Tcm -Sample_1_CATATTCGTAAATACG-1 2.40221738815308 8.27754211425781 CD8+ Tem -Sample_1_CATCAAGAGGTGTTAA-1 0.0630362555384636 5.19026231765747 CD8+ Tcm -Sample_1_CATCAAGCACGACGAA-1 5.24060583114624 6.38461589813232 CD8+ Tem -Sample_1_CATCAAGCAGGCAGTA-1 3.17114496231079 -9.54288673400879 NK cells -Sample_1_CATCAAGCATGTTGAC-1 0.755975067615509 2.47337126731873 CD8+ Tem -Sample_1_CATCAAGGTCTCCACT-1 3.95773458480835 -9.28275012969971 NK cells -Sample_1_CATCAGAAGAAACCAT-1 -0.232032909989357 5.63811492919922 CD8+ Tcm -Sample_1_CATCAGACACCCTATC-1 3.51261401176453 5.64078712463379 CD8+ Tcm -Sample_1_CATCAGAGTCCATGAT-1 0.627810120582581 -8.96159744262695 NK cells -Sample_1_CATCAGAGTCCGAAGA-1 -1.37576651573181 -0.0231322832405567 CD8+ Tem -Sample_1_CATCCACGTCCCTTGT-1 -0.18080335855484 5.19724941253662 CD8+ Tcm -Sample_1_CATCCACGTGCATCTA-1 -0.00507249962538481 6.67111539840698 CD4+ Tem -Sample_1_CATCCACTCTGAGGGA-1 2.91036915779114 -9.02821731567383 NK cells -Sample_1_CATCGAAAGGAATCGC-1 -1.04445242881775 6.89203262329102 CD4+ T-cells -Sample_1_CATCGAACAGGCTGAA-1 -2.75409817695618 7.36702346801758 CD8+ Tcm -Sample_1_CATCGAACAGGGAGAG-1 3.00789451599121 7.0022177696228 CD8+ Tem -Sample_1_CATCGGGCAAAGGTGC-1 0.0438354015350342 -11.3050632476807 NK cells -Sample_1_CATGACAAGTGAACGC-1 2.04186844825745 -5.21881484985352 NK cells -Sample_1_CATGACACACAGACTT-1 1.71415591239929 -10.8167772293091 NK cells -Sample_1_CATGACACACCGTTGG-1 3.3174159526825 3.91487193107605 CD8+ Tcm -Sample_1_CATGACACAGCTATTG-1 2.94020867347717 -9.61607265472412 NK cells -Sample_1_CATGACAGTCTCACCT-1 3.10072755813599 4.4870982170105 CD8+ Tem -Sample_1_CATGCCTCAATGAAAC-1 1.06381857395172 -9.70048427581787 NK cells -Sample_1_CATGGCGAGTGTGGCA-1 2.47490406036377 -7.07280588150024 NK cells -Sample_1_CATGGCGCACGAGGTA-1 1.9792947769165 -7.69011211395264 NK cells -Sample_1_CATGGCGGTACAGTGG-1 0.440909385681152 -8.57760238647461 NK cells -Sample_1_CATTATCAGGGTTCCC-1 2.5673565864563 -10.3409767150879 NK cells -Sample_1_CATTATCCACAGCCCA-1 -3.84395003318787 8.70332336425781 CD8+ Tcm -Sample_1_CATTATCGTGGTCTCG-1 2.44035959243774 6.45651531219482 CD8+ Tem -Sample_1_CCAATCCGTCTCTTAT-1 -0.76575756072998 9.56751537322998 CD8+ T-cells -Sample_1_CCAATCCTCTCATTCA-1 -2.16597080230713 8.92309093475342 CD4+ T-cells -Sample_1_CCACCTATCTTGCCGT-1 0.697722792625427 -11.2273750305176 NK cells -Sample_1_CCACGGACAATCACAC-1 3.53270649909973 -9.78309535980225 NK cells -Sample_1_CCACGGATCAGGCAAG-1 2.36914968490601 -4.821044921875 NK cells -Sample_1_CCACTACAGTGGACGT-1 3.45099496841431 5.1000828742981 CD8+ Tem -Sample_1_CCAGCGAGTTCGTTGA-1 4.47835874557495 7.14763879776001 CD8+ Tem -Sample_1_CCATTCGCAAGCCATT-1 -2.19050478935242 9.32952308654785 CD8+ Tcm -Sample_1_CCCAATCTCAACACCA-1 3.72384238243103 7.22496032714844 CD8+ Tcm -Sample_1_CCCTCCTAGTAGCCGA-1 2.67608094215393 -9.94626426696777 NK cells -Sample_1_CCCTCCTGTTGTACAC-1 -2.77268433570862 8.73142910003662 CD8+ T-cells -Sample_1_CCGGTAGAGTTACGGG-1 -1.94900166988373 9.13476848602295 CD8+ T-cells -Sample_1_CCGGTAGTCGCCTGAG-1 -0.346305727958679 -9.99585151672363 NK cells -Sample_1_CCGTACTCAGGGTATG-1 4.4346809387207 5.63301467895508 CD8+ Tcm -Sample_1_CCGTACTTCTACTATC-1 2.30927538871765 -10.162428855896 NK cells -Sample_1_CCGTGGAAGCTGAAAT-1 0.248432323336601 8.61422252655029 CD8+ Tem -Sample_1_CCGTTCAAGCCCGAAA-1 0.899591088294983 -9.11897277832031 NK cells -Sample_1_CCTAAAGAGCGATTCT-1 3.52225279808044 5.047682762146 CD8+ Tcm -Sample_1_CCTAAAGCAAGTAATG-1 3.43550872802734 6.08645820617676 CD8+ Tem -Sample_1_CCTACACAGCAAATCA-1 -9.55943775177002 -2.64964008331299 Monocytes -Sample_1_CCTACACTCTTTACGT-1 2.94011187553406 -10.7847766876221 NK cells -Sample_1_CCTACCACATCGGAAG-1 -0.994282484054565 0.110569573938847 CD8+ Tcm -Sample_1_CCTAGCTAGAGGTACC-1 -2.75922751426697 7.46610164642334 CD4+ T-cells -Sample_1_CCTATTATCAAACCAC-1 1.77717697620392 7.98904800415039 CD8+ Tem -Sample_1_CCTCTGAGTAAGAGGA-1 -0.769384860992432 0.120132997632027 CD8+ Tcm -Sample_1_CCTCTGAGTTATTCTC-1 3.06545925140381 6.632399559021 CD8+ Tem -Sample_1_CCTCTGATCAGCCTAA-1 0.73478627204895 -8.36841773986816 NK cells -Sample_1_CCTTACGAGCACCGCT-1 2.17968821525574 -9.68575382232666 NK cells -Sample_1_CCTTACGGTCGCATCG-1 1.81071054935455 -7.07152080535889 NK cells -Sample_1_CCTTCCCCAAACCTAC-1 -0.123919628560543 9.3926420211792 CD8+ Tcm -Sample_1_CCTTCCCTCACTGGGC-1 -2.03845858573914 7.19721555709839 CD4+ T-cells -Sample_1_CCTTCGAAGCGTTTAC-1 3.56893587112427 -8.37166690826416 NK cells -Sample_1_CCTTCGACAAGAAGAG-1 4.00477886199951 6.70125198364258 CD8+ Tem -Sample_1_CGAACATAGTCAAGCG-1 1.87806880474091 -6.78408050537109 NK cells -Sample_1_CGAACATCATCAGTCA-1 4.20589876174927 3.86020541191101 CD8+ Tem -Sample_1_CGAATGTGTCATTAGC-1 1.22296571731567 8.5341157913208 CD8+ Tcm -Sample_1_CGACCTTAGCACCGCT-1 -0.220777273178101 5.40536308288574 CD8+ Tcm -Sample_1_CGACCTTAGTTGTAGA-1 -9.59134387969971 -2.68039727210999 naive B-cells -Sample_1_CGACCTTTCAAGCCTA-1 -0.871612966060638 0.59259432554245 CD8+ Tem -Sample_1_CGACTTCTCATAGCAC-1 3.20290374755859 -8.6392068862915 NK cells -Sample_1_CGAGCACAGACGCAAC-1 1.51586127281189 -10.4823131561279 NK cells -Sample_1_CGAGCACAGCTCAACT-1 5.4262547492981 6.61784839630127 CD8+ Tem -Sample_1_CGAGCACAGGCGTACA-1 -3.0296037197113 7.72162580490112 CD8+ T-cells -Sample_1_CGAGCACTCTGCAAGT-1 -3.02756762504578 7.93671941757202 CD4+ T-cells -Sample_1_CGAGCCACATGCAACT-1 -2.39545130729675 6.50141954421997 CD8+ T-cells -Sample_1_CGAGCCATCGTGGTCG-1 0.118915848433971 4.0856032371521 CD8+ Tcm -Sample_1_CGATCGGAGACGCTTT-1 -0.942717790603638 1.26931869983673 CD8+ Tem -Sample_1_CGATCGGGTCCCTTGT-1 -0.311519205570221 1.38760602474213 CD8+ Tem -Sample_1_CGATGGCTCGGTCCGA-1 -0.703660845756531 9.6351432800293 CD8+ T-cells -Sample_1_CGATGTAAGGACATTA-1 2.69289517402649 5.67592811584473 CD8+ Tem -Sample_1_CGATTGAGTGGTACAG-1 2.4296669960022 -7.28416109085083 NK cells -Sample_1_CGCCAAGAGACCACGA-1 -1.55237197875977 0.290328741073608 CD8+ Tem -Sample_1_CGCCAAGGTGTCCTCT-1 2.64222478866577 -11.172399520874 NK cells -Sample_1_CGCGGTAAGGCATGGT-1 3.52520227432251 -8.27082443237305 NK cells -Sample_1_CGCGGTAAGTATCGAA-1 1.30341637134552 -10.4853525161743 NK cells -Sample_1_CGCGGTACACAGGCCT-1 -0.577741324901581 -9.55523109436035 NK cells -Sample_1_CGCGGTATCAACGCTA-1 1.42548716068268 -8.31450366973877 NK cells -Sample_1_CGCGGTATCTCTGCTG-1 -0.701200485229492 4.63832187652588 CD8+ Tem -Sample_1_CGCGTTTTCCAGATCA-1 1.53237640857697 2.18888473510742 CD8+ Tem -Sample_1_CGCTATCCACTGTCGG-1 -2.63296365737915 7.32019424438477 CD8+ T-cells -Sample_1_CGCTATCGTGCAGACA-1 1.72842288017273 -10.254602432251 NK cells -Sample_1_CGCTATCTCGCCATAA-1 1.02027058601379 -7.88271999359131 NK cells -Sample_1_CGCTATCTCTGTACGA-1 0.429194837808609 4.70363616943359 CD8+ Tcm -Sample_1_CGCTGGATCCTAGGGC-1 -0.287453681230545 7.05276489257812 CD8+ Tcm -Sample_1_CGCTTCACATTTCAGG-1 3.01135110855103 4.62495565414429 CD8+ Tem -Sample_1_CGGACACAGCAGCGTA-1 -1.65239548683167 9.84995555877686 CD8+ Tcm -Sample_1_CGGACACAGGTGCTTT-1 0.505939483642578 -8.15459156036377 NK cells -Sample_1_CGGACACTCCTCGCAT-1 -1.3574812412262 1.77687704563141 CD8+ Tem -Sample_1_CGGACGTTCAGGTTCA-1 3.45047450065613 6.77175569534302 CD8+ Tcm -Sample_1_CGGACTGTCTCTGAGA-1 -0.771688759326935 7.42810297012329 CD8+ Tcm -Sample_1_CGGAGCTCAAACTGTC-1 3.18879222869873 3.82512140274048 CD8+ Tem -Sample_1_CGGAGCTGTACCGTTA-1 4.37616443634033 7.13164615631104 CD8+ Tem -Sample_1_CGGAGTCTCAGGCCCA-1 0.488511353731155 -10.135516166687 NK cells -Sample_1_CGGCTAGCAATCTGCA-1 -0.0722795501351357 -11.1829032897949 NK cells -Sample_1_CGTAGCGCAAACAACA-1 3.24545168876648 -7.32401418685913 NK cells -Sample_1_CGTAGCGTCAGCACAT-1 2.57239961624146 -8.44242191314697 NK cells -Sample_1_CGTAGCGTCCTAGAAC-1 1.98351907730103 -4.74703550338745 NK cells -Sample_1_CGTAGGCGTCTGATTG-1 -0.411053359508514 7.43650817871094 CD8+ Tcm -Sample_1_CGTCAGGCAAGCTGTT-1 0.0623848550021648 -9.1264591217041 NK cells -Sample_1_CGTCAGGTCAAGGTAA-1 -2.64447140693665 8.86438369750977 CD4+ T-cells -Sample_1_CGTCCATAGCTATGCT-1 2.21137070655823 -8.25054550170898 NK cells -Sample_1_CGTCCATCACCCTATC-1 3.18663787841797 4.84319591522217 CD8+ Tem -Sample_1_CGTCTACCATAGGATA-1 -1.49236154556274 8.76383018493652 CD8+ T-cells -Sample_1_CGTGAGCCATTCTTAC-1 2.04542541503906 7.80729675292969 CD8+ Tcm -Sample_1_CGTGAGCTCAATCTCT-1 -9.68434810638428 -2.77521347999573 naive B-cells -Sample_1_CGTTGGGTCAGAGACG-1 4.27883386611938 7.04338455200195 CD8+ Tem -Sample_1_CGTTGGGTCCTTGCCA-1 -1.01970958709717 5.23676347732544 CD8+ Tcm -Sample_1_CTAACTTAGCCCAGCT-1 3.78999733924866 5.56672286987305 CD8+ Tem -Sample_1_CTAAGACAGTGTCTCA-1 0.692916095256805 -10.3941383361816 NK cells -Sample_1_CTAAGACCAGATCGGA-1 2.14137864112854 8.07774925231934 CD8+ Tem -Sample_1_CTAAGACGTCGGATCC-1 1.88571536540985 -4.62730932235718 NK cells -Sample_1_CTAAGACTCCACTGGG-1 1.96300554275513 -5.01746273040771 NK cells -Sample_1_CTAATGGCACAGACTT-1 -1.51943206787109 7.44185066223145 CD8+ T-cells -Sample_1_CTAATGGGTTCAGGCC-1 1.69961595535278 -7.671311378479 NK cells -Sample_1_CTACACCCAACGCACC-1 2.36763954162598 -8.64446449279785 NK cells -Sample_1_CTACACCGTTCAGCGC-1 2.47601747512817 -10.017373085022 NK cells -Sample_1_CTACACCTCGGTTCGG-1 2.50060033798218 4.72233295440674 CD8+ Tem -Sample_1_CTACATTCATGCCCGA-1 3.05085158348083 -8.61839389801025 NK cells -Sample_1_CTACATTGTTTGGCGC-1 3.50059175491333 -7.82734441757202 NK cells -Sample_1_CTACCCAAGGTGCACA-1 2.47169971466064 -5.09640884399414 NK cells -Sample_1_CTACCCACACGACTCG-1 2.2097053527832 -6.95829296112061 NK cells -Sample_1_CTACCCACAGCGATCC-1 0.106824487447739 -10.9473457336426 NK cells -Sample_1_CTACGTCAGAGCTGCA-1 -1.74871504306793 8.53816795349121 CD4+ T-cells -Sample_1_CTACGTCAGGCGATAC-1 3.16552686691284 7.09193658828735 CD8+ Tem -Sample_1_CTACGTCGTCTCAACA-1 1.79297733306885 -8.68103694915771 NK cells -Sample_1_CTACGTCTCGTCGTTC-1 0.707013785839081 -8.96208381652832 NK cells -Sample_1_CTAGAGTCAATCTACG-1 1.07578718662262 -9.35842895507812 NK cells -Sample_1_CTAGAGTCAGGAATCG-1 -0.696973741054535 0.933324337005615 CD8+ Tem -Sample_1_CTAGAGTTCAACTCTT-1 3.69802665710449 5.8221116065979 CD8+ Tem -Sample_1_CTAGTGACAGACGCTC-1 -1.2039532661438 6.70439147949219 CD8+ Tcm -Sample_1_CTAGTGACAGCGTAAG-1 -0.951570391654968 5.65003728866577 CD8+ Tcm -Sample_1_CTAGTGACATCTATGG-1 0.754350483417511 6.65543651580811 CD4+ Tem -Sample_1_CTAGTGATCCTTGACC-1 -1.53773200511932 9.29082012176514 CD8+ T-cells -Sample_1_CTCACACTCACATACG-1 2.93328738212585 4.76689195632935 CD8+ Tem -Sample_1_CTCAGAATCGAGGTAG-1 1.74549067020416 2.13115930557251 CD8+ Tem -Sample_1_CTCAGAATCGGAAATA-1 -1.24728047847748 2.17150449752808 CD8+ Tcm -Sample_1_CTCATTAAGTCGTTTG-1 -0.278563052415848 8.12511730194092 CD8+ Tcm -Sample_1_CTCCTAGGTCATACTG-1 1.60536992549896 -10.105263710022 NK cells -Sample_1_CTCCTAGTCGGCGCTA-1 -1.16230344772339 0.910468280315399 CD8+ Tem -Sample_1_CTCGAAAAGAACAATC-1 0.688374102115631 5.26966381072998 CD8+ Tcm -Sample_1_CTCGAAAAGACGACGT-1 -0.969482243061066 3.41217827796936 CD8+ Tem -Sample_1_CTCGAAACACAGCGTC-1 4.66794013977051 -8.9266939163208 NK cells -Sample_1_CTCGGGAGTTCGGCAC-1 2.80904102325439 6.28461122512817 CD8+ Tem -Sample_1_CTCGTCACACACTGCG-1 0.99674254655838 -9.7141695022583 NK cells -Sample_1_CTCGTCACACAGGAGT-1 0.605369091033936 -10.1221284866333 NK cells -Sample_1_CTCGTCAGTTCCACGG-1 4.20492076873779 4.16893815994263 CD8+ Tem -Sample_1_CTCTAATAGTCCGTAT-1 0.664677858352661 -11.162670135498 NK cells -Sample_1_CTCTAATAGTGGGTTG-1 4.37943887710571 5.05606937408447 CD8+ Tcm -Sample_1_CTCTACGAGGTGCACA-1 3.79614877700806 4.42203044891357 CD8+ Tem -Sample_1_CTCTACGTCGAACGGA-1 4.63490200042725 7.02498912811279 CD8+ Tem -Sample_1_CTCTGGTAGCGATATA-1 3.74753785133362 4.32856369018555 CD8+ Tem -Sample_1_CTCTGGTAGGGAACGG-1 -0.494250893592834 8.30830860137939 CD8+ T-cells -Sample_1_CTCTGGTAGGTCGGAT-1 2.90352034568787 6.94984722137451 CD8+ Tcm -Sample_1_CTCTGGTCACTGCCAG-1 3.11969470977783 4.14710187911987 CD8+ Tem -Sample_1_CTGAAACGTGCCTGTG-1 0.540169775485992 -8.17657661437988 NK cells -Sample_1_CTGAAGTCACGAAGCA-1 -2.25880408287048 7.93933582305908 CD8+ T-cells -Sample_1_CTGAAGTTCCGCAAGC-1 2.15600371360779 -8.90572452545166 NK cells -Sample_1_CTGAAGTTCGCAAACT-1 -2.75590109825134 8.06023216247559 CD4+ T-cells -Sample_1_CTGATAGAGCGTCAAG-1 -0.234809145331383 9.64826393127441 CD8+ T-cells -Sample_1_CTGATAGTCGAATCCA-1 1.14090037345886 8.470778465271 CD8+ Tem -Sample_1_CTGATCCTCATAACCG-1 4.99145793914795 6.03238916397095 CD8+ Tem -Sample_1_CTGATCCTCGCAAGCC-1 -2.84604144096375 8.18588733673096 CD8+ T-cells -Sample_1_CTGATCCTCTACTATC-1 -1.87423133850098 6.75371694564819 CD4+ T-cells -Sample_1_CTGCCTAAGAAGGTGA-1 0.431790769100189 -10.2718181610107 NK cells -Sample_1_CTGCCTAAGACAGAGA-1 0.690374672412872 2.89846467971802 CD8+ Tem -Sample_1_CTGCCTACAGACAGGT-1 4.11702585220337 -9.92671203613281 NK cells -Sample_1_CTGCTGTCAACGATCT-1 -0.0789527148008347 -7.78494644165039 NK cells -Sample_1_CTGGTCTCAACCGCCA-1 4.51300239562988 4.46926164627075 CD8+ Tem -Sample_1_CTGGTCTGTATCAGTC-1 0.0246476363390684 -9.97648525238037 NK cells -Sample_1_CTGGTCTGTTGACGTT-1 1.82693767547607 7.89642190933228 CD8+ Tem -Sample_1_CTGGTCTGTTGTCGCG-1 -1.99664735794067 8.44965553283691 CD8+ T-cells -Sample_1_CTGTGCTCACATGGGA-1 -0.913871049880981 9.0831413269043 CD8+ T-cells -Sample_1_CTGTGCTCACGGCCAT-1 2.77731490135193 5.03067302703857 CD8+ Tem -Sample_1_CTGTGCTCACTTCGAA-1 -0.474156647920609 -7.00108814239502 CD8+ Tem -Sample_1_CTGTGCTTCAGCGATT-1 3.83106756210327 6.7234935760498 CD8+ Tem -Sample_1_CTTAACTGTGGACGAT-1 -1.18547022342682 8.16516590118408 CD4+ T-cells -Sample_1_CTTAACTTCAACGGGA-1 2.7127513885498 -10.636570930481 NK cells -Sample_1_CTTACCGAGCGTCAAG-1 2.53008604049683 6.5587797164917 CD8+ Tem -Sample_1_CTTAGGAAGCCAACAG-1 -2.21248030662537 7.6800799369812 CD8+ T-cells -Sample_1_CTTCTCTAGTATCGAA-1 3.2545702457428 5.242271900177 CD8+ Tem -Sample_1_CTTCTCTAGTGCGATG-1 1.55109906196594 -10.6372385025024 NK cells -Sample_1_CTTTGCGTCGGGAGTA-1 3.32105731964111 7.79813146591187 CD8+ Tem -Sample_1_GAAACTCAGTACGTTC-1 -0.0590820275247097 3.00523972511292 CD8+ Tem -Sample_1_GAAACTCGTCTGCCAG-1 1.43477606773376 1.62395751476288 NK cells -Sample_1_GAAATGAAGAAGGGTA-1 3.6400351524353 4.82408857345581 CD8+ Tcm -Sample_1_GAAATGAGTGTTAAGA-1 3.89413261413574 6.3429069519043 CD8+ Tem -Sample_1_GAACATCTCCTTGGTC-1 0.147489830851555 9.08437347412109 CD8+ Tcm -Sample_1_GAACCTACAACACCCG-1 -2.72442746162415 8.52769947052002 CD8+ T-cells -Sample_1_GAACGGAAGCAAATCA-1 2.40917038917542 6.35535955429077 CD8+ Tcm -Sample_1_GAAGCAGGTTCAGGCC-1 2.13171696662903 -10.4592218399048 NK cells -Sample_1_GAAGCAGTCTGTGCAA-1 2.79256415367126 -10.4532032012939 NK cells -Sample_1_GAATAAGAGCGATTCT-1 2.80030536651611 4.93432283401489 CD8+ Tem -Sample_1_GAATAAGAGTTGAGAT-1 0.255801111459732 -9.0773811340332 NK cells -Sample_1_GAATAAGGTACCGTTA-1 -2.08134174346924 9.20035934448242 CD8+ T-cells -Sample_1_GAATAAGTCCACTCCA-1 0.482437074184418 -9.88090896606445 NK cells -Sample_1_GACACGCCACGACGAA-1 1.77269780635834 6.04607200622559 CD4+ Tem -Sample_1_GACACGCGTAACGTTC-1 -0.344223469495773 4.08104705810547 CD8+ Tcm -Sample_1_GACAGAGAGGCGTACA-1 1.77840542793274 -5.82710361480713 NK cells -Sample_1_GACAGAGTCGAGAACG-1 -1.19841289520264 0.437630861997604 CD8+ Tem -Sample_1_GACCAATGTGAGCGAT-1 -0.459125220775604 3.54582333564758 NK cells -Sample_1_GACCTGGCACTTCGAA-1 2.00395822525024 -9.84332847595215 NK cells -Sample_1_GACCTGGCAGTAAGAT-1 -0.690893054008484 -0.0484486930072308 NK cells -Sample_1_GACGGCTGTTGATTGC-1 0.115743860602379 -10.0460443496704 NK cells -Sample_1_GACGGCTGTTTGGGCC-1 3.63397407531738 8.15212535858154 CD8+ Tem -Sample_1_GACGGCTTCAACGCTA-1 3.45173978805542 5.30575752258301 CD8+ Tem -Sample_1_GACGTGCTCACCATAG-1 -1.38138711452484 0.540376663208008 CD8+ Tem -Sample_1_GACGTTAAGAATAGGG-1 1.24904870986938 -8.83970832824707 NK cells -Sample_1_GACGTTAAGTGTACGG-1 -1.23987770080566 7.93319177627563 CD8+ T-cells -Sample_1_GACTACAAGTACACCT-1 3.67229127883911 7.68147802352905 CD8+ Tem -Sample_1_GACTACATCGGAATCT-1 1.89055633544922 -8.93047523498535 NK cells -Sample_1_GACTGCGGTTCGGCAC-1 5.0992488861084 6.02694940567017 CD8+ Tem -Sample_1_GACTGCGTCAGTTGAC-1 -1.82335186004639 7.77199745178223 CD4+ T-cells -Sample_1_GACTGCGTCTGTACGA-1 -3.26714158058167 7.48848247528076 CD8+ T-cells -Sample_1_GAGCAGAAGTGCTGCC-1 -2.72569298744202 8.85517311096191 NK cells -Sample_1_GAGCAGAGTCTTGATG-1 -0.114556439220905 -8.72591876983643 NK cells -Sample_1_GAGGTGAGTCCTAGCG-1 -3.91759777069092 8.89718532562256 CD8+ Tcm -Sample_1_GAGGTGATCCCTAACC-1 4.48416471481323 -8.21122741699219 NK cells -Sample_1_GATCAGTAGAGTTGGC-1 -1.40649104118347 8.05350399017334 CD8+ T-cells -Sample_1_GATCAGTAGTAGCGGT-1 -2.33520460128784 8.5764684677124 CD8+ T-cells -Sample_1_GATCAGTCAAAGCGGT-1 -0.718833148479462 8.70720100402832 CD8+ T-cells -Sample_1_GATCAGTTCCGAGCCA-1 0.809561252593994 -9.30906963348389 NK cells -Sample_1_GATCAGTTCCTCGCAT-1 0.247267454862595 -8.882155418396 NK cells -Sample_1_GATCGATGTTCCCGAG-1 -1.44037353992462 7.87010145187378 CD8+ T-cells -Sample_1_GATCGATTCCACGCAG-1 0.848930478096008 -10.4926357269287 NK cells -Sample_1_GATCGCGAGACCTTTG-1 3.75076770782471 4.36834716796875 CD8+ Tem -Sample_1_GATCGCGCAAGAAAGG-1 2.47015929222107 -5.13344526290894 NK cells -Sample_1_GATCGCGGTCAACTGT-1 4.32910776138306 -8.34249305725098 NK cells -Sample_1_GATCGCGTCAAGGTAA-1 0.419142216444016 -7.20157623291016 NK cells -Sample_1_GATCGTAAGGTACTCT-1 -2.35361385345459 8.44811534881592 CD4+ T-cells -Sample_1_GATGAAAAGACACTAA-1 -2.16669344902039 8.51125144958496 CD8+ T-cells -Sample_1_GATGAAATCGTCTGAA-1 0.608438611030579 -9.77297592163086 NK cells -Sample_1_GATGAGGTCCTAGGGC-1 2.92352557182312 -7.74598598480225 NK cells -Sample_1_GATGCTAAGTCCCACG-1 -2.50823783874512 7.89695882797241 CD4+ T-cells -Sample_1_GATGCTACAAAGGAAG-1 3.3099536895752 4.10033941268921 CD8+ Tem -Sample_1_GATGCTAGTAGAGGAA-1 3.6833643913269 5.86702919006348 CD8+ Tem -Sample_1_GATTCAGAGACAGGCT-1 0.506023585796356 2.37659072875977 CD8+ Tem -Sample_1_GCAAACTCATCCCATC-1 0.195472240447998 2.00387382507324 CD8+ Tcm -Sample_1_GCAAACTGTGGACGAT-1 2.8027970790863 -7.55388450622559 NK cells -Sample_1_GCAATCAAGTAGGCCA-1 3.89770817756653 -9.63875389099121 NK cells -Sample_1_GCAATCAGTCTAGTCA-1 3.319176197052 -7.93057918548584 NK cells -Sample_1_GCAATCATCACCCTCA-1 1.55381631851196 -8.63716602325439 NK cells -Sample_1_GCAGTTACAACGATGG-1 0.915478527545929 -10.5911989212036 NK cells -Sample_1_GCAGTTAGTTCAGCGC-1 -0.50658243894577 9.50882816314697 CD8+ T-cells -Sample_1_GCATACATCCTGCCAT-1 4.18612575531006 4.04001998901367 CD8+ Tem -Sample_1_GCATACATCTTTAGGG-1 2.39410734176636 -10.1385555267334 NK cells -Sample_1_GCATGCGGTCCCTTGT-1 0.753268539905548 5.58596706390381 CD8+ Tcm -Sample_1_GCATGTAAGACGCACA-1 2.04623961448669 6.26838445663452 CD8+ Tem -Sample_1_GCATGTAAGAGTAATC-1 -1.86949074268341 1.27132964134216 CD8+ Tem -Sample_1_GCATGTACACCTCGTT-1 -0.607209146022797 -0.21784308552742 CD8+ Tem -Sample_1_GCCAAATAGTGGAGAA-1 5.28897666931152 5.73409652709961 CD8+ Tem -Sample_1_GCCTCTACAAACTGCT-1 1.99360835552216 2.94407296180725 CD8+ Tem -Sample_1_GCGACCACATGCCCGA-1 4.27168607711792 4.8252100944519 CD8+ Tcm -Sample_1_GCGAGAAAGCTACCGC-1 -1.8541671037674 8.79474830627441 CD8+ T-cells -Sample_1_GCGAGAACAAGGCTCC-1 2.24975895881653 6.80652856826782 CD8+ Tem -Sample_1_GCGAGAACACCAGTTA-1 0.461079746484756 -8.72281551361084 NK cells -Sample_1_GCGCAACAGGGTGTGT-1 -0.853554964065552 4.91273975372314 CD8+ Tcm -Sample_1_GCGCAACCACCCAGTG-1 1.3683660030365 -8.18411445617676 NK cells -Sample_1_GCGCAACTCCTATGTT-1 2.05239844322205 7.65978288650513 CD4+ Tem -Sample_1_GCGCAACTCTTGCATT-1 0.621173620223999 -7.6070671081543 NK cells -Sample_1_GCGCAGTTCCGTCATC-1 -0.41158390045166 -0.153892979025841 CD8+ Tem -Sample_1_GCGCCAACACCGTTGG-1 -3.94150376319885 8.99466133117676 CD8+ Tcm -Sample_1_GCGCGATTCCGTAGTA-1 2.8406445980072 -8.92532444000244 NK cells -Sample_1_GCGGGTTGTGCGCTTG-1 -0.924243748188019 5.06863498687744 CD8+ Tcm -Sample_1_GCTCCTAGTAGAAGGA-1 -0.274944663047791 0.737953662872314 CD8+ Tem -Sample_1_GCTCTGTCAAGTCTAC-1 -0.24533349275589 -9.85272216796875 NK cells -Sample_1_GCTGCAGAGTGGGATC-1 3.90335202217102 7.29629278182983 CD8+ Tem -Sample_1_GCTGCAGCACCACCAG-1 1.15303874015808 -10.8980417251587 NK cells -Sample_1_GCTGCAGGTCGATTGT-1 0.839805424213409 -10.0366201400757 NK cells -Sample_1_GCTGCAGTCCTTTACA-1 1.97093594074249 2.22766900062561 CD8+ Tem -Sample_1_GCTGCAGTCGGAGGTA-1 3.85833120346069 -8.80115032196045 NK cells -Sample_1_GCTGCGAAGGCTACGA-1 -0.551234364509583 -0.121521957218647 CD8+ Tem -Sample_1_GCTGCGAGTGTCAATC-1 -0.301801383495331 9.10567188262939 CD4+ T-cells -Sample_1_GCTGCGATCTGATACG-1 0.585711479187012 -9.06491184234619 NK cells -Sample_1_GCTGGGTCATATGCTG-1 -0.167133241891861 -10.2117414474487 NK cells -Sample_1_GCTTCCACACATCCGG-1 4.97530794143677 4.74355840682983 CD8+ Tem -Sample_1_GCTTCCACATCTATGG-1 -0.833450794219971 -9.52284812927246 NK cells -Sample_1_GCTTCCATCTCTGTCG-1 0.49830573797226 4.52252912521362 CD8+ Tcm -Sample_1_GCTTGAACAGTATAAG-1 4.07127618789673 6.22015476226807 CD8+ Tem -Sample_1_GCTTGAATCTAACCGA-1 0.0995322689414024 -8.19691753387451 NK cells -Sample_1_GGAAAGCTCCTTGCCA-1 1.77435982227325 -8.22579479217529 NK cells -Sample_1_GGAACTTCAGATGGCA-1 3.8548309803009 4.24844980239868 CD8+ Tem -Sample_1_GGAATAACAAATTGCC-1 4.2466139793396 -8.38707447052002 NK cells -Sample_1_GGAATAACAGTTCATG-1 -2.76382303237915 8.49629497528076 CD8+ T-cells -Sample_1_GGACATTCACTTCGAA-1 2.88918328285217 -9.60821151733398 NK cells -Sample_1_GGAGCAACAAGCGCTC-1 2.62432622909546 6.51960515975952 CD8+ Tem -Sample_1_GGAGCAACATCTCCCA-1 -0.0781831219792366 6.06057548522949 CD4+ Tem -Sample_1_GGAGCAATCGCATGGC-1 -0.415369153022766 -6.98866748809814 NK cells -Sample_1_GGAGCAATCGTAGATC-1 -0.213849201798439 -11.1357717514038 NK cells -Sample_1_GGATGTTAGTCCGTAT-1 -2.71616840362549 8.0947847366333 CD4+ T-cells -Sample_1_GGATTACCATTGAGCT-1 -0.00959466397762299 -9.83193206787109 NK cells -Sample_1_GGATTACTCACCAGGC-1 0.0195837784558535 5.12490224838257 CD8+ Tcm -Sample_1_GGCAATTAGAGGTAGA-1 3.55588412284851 4.14010095596313 CD8+ Tem -Sample_1_GGCAATTAGTGTTGAA-1 -1.62776207923889 1.25527679920197 CD8+ Tem -Sample_1_GGCAATTCAGATTGCT-1 -0.650635421276093 3.80666255950928 CD8+ Tem -Sample_1_GGCAATTGTCAAACTC-1 1.12907147407532 -9.06212615966797 NK cells -Sample_1_GGCAATTGTGATAAGT-1 4.46939086914062 4.07049417495728 CD8+ Tem -Sample_1_GGCCGATGTTTCCACC-1 2.21034383773804 -10.815357208252 NK cells -Sample_1_GGCGACTCATCCTAGA-1 1.00149989128113 -8.86007308959961 NK cells -Sample_1_GGCGACTCATCCTTGC-1 -0.086730919778347 6.56266164779663 CD4+ Tcm -Sample_1_GGCGTGTCACATGACT-1 1.4458087682724 -8.99271869659424 NK cells -Sample_1_GGGAATGAGATGGCGT-1 -1.99444055557251 9.0369234085083 CD4+ T-cells -Sample_1_GGGAATGGTCATCCCT-1 -1.42071139812469 8.02858638763428 CD8+ T-cells -Sample_1_GGGACCTTCTTGGGTA-1 -0.297968000173569 -0.169596880674362 CD8+ Tem -Sample_1_GGGAGATAGTGTTTGC-1 -0.943174183368683 8.66666221618652 CD8+ T-cells -Sample_1_GGGAGATCAGATGGCA-1 0.432471334934235 4.46840381622314 CD8+ Tcm -Sample_1_GGGAGATCATACCATG-1 -1.81196558475494 7.86424541473389 CD8+ T-cells -Sample_1_GGGAGATGTCAAACTC-1 4.35870504379272 -9.81123352050781 NK cells -Sample_1_GGGATGAGTCGTCTTC-1 3.31418943405151 6.1149525642395 CD8+ Tcm -Sample_1_GGGATGATCAGTTCGA-1 1.49927914142609 6.2656192779541 CD8+ Tem -Sample_1_GGGCACTAGCAGGTCA-1 3.70309233665466 -8.38417911529541 NK cells -Sample_1_GGGCACTCAAAGCAAT-1 -0.917432487010956 3.94810175895691 CD8+ Tem -Sample_1_GGGCACTTCCTAGGGC-1 -1.72339963912964 8.80133819580078 CD8+ T-cells -Sample_1_GGGCACTTCTATCGCC-1 0.794236779212952 -9.22216606140137 NK cells -Sample_1_GGGCATCCACGGCGTT-1 0.523612499237061 -9.20165252685547 NK cells -Sample_1_GGGCATCCATCACAAC-1 0.126509308815002 3.50232219696045 CD8+ Tcm -Sample_1_GGGCATCGTTCCTCCA-1 5.15055227279663 5.61600017547607 CD8+ Tcm -Sample_1_GGGCATCTCCGCATCT-1 2.44237637519836 5.54469442367554 CD8+ Tem -Sample_1_GGTATTGGTGAGGGTT-1 -1.89470827579498 6.66817665100098 CD8+ T-cells -Sample_1_GGTATTGGTTCCCGAG-1 0.467153906822205 3.69567680358887 CD8+ Tcm -Sample_1_GGTGAAGAGAGGTAGA-1 1.0671169757843 2.13026976585388 CD8+ Tcm -Sample_1_GGTGAAGTCCATGAGT-1 3.94793105125427 6.30688762664795 CD8+ Tem -Sample_1_GGTGAAGTCCTCATTA-1 2.28394198417664 -8.18015956878662 NK cells -Sample_1_GGTGCGTAGAGTACAT-1 1.31479394435883 -10.6336231231689 NK cells -Sample_1_GGTGCGTAGTACATGA-1 0.000618312740698457 5.58661413192749 Tregs -Sample_1_GGTGTTAGTAGTAGTA-1 -0.9777010679245 0.241243064403534 CD8+ Tem -Sample_1_GTAACGTAGGACAGCT-1 2.71862125396729 5.48501348495483 CD8+ Tem -Sample_1_GTAACGTTCACTTACT-1 2.46418857574463 4.88584899902344 CD8+ Tem -Sample_1_GTAACTGCATCGGTTA-1 3.43324613571167 -8.33129024505615 NK cells -Sample_1_GTAACTGTCCGAACGC-1 -0.600250899791718 -7.25049495697021 NK cells -Sample_1_GTACGTAAGACACTAA-1 -0.762470722198486 -6.92325448989868 CD8+ T-cells -Sample_1_GTACGTACATGCTGGC-1 2.95901250839233 5.1428108215332 CD8+ Tcm -Sample_1_GTACGTATCCTGTAGA-1 -3.03267621994019 8.2058572769165 CD4+ T-cells -Sample_1_GTACTTTGTGTCAATC-1 -0.377443730831146 8.26597881317139 CD8+ Tem -Sample_1_GTAGGCCAGCGCTCCA-1 2.83828735351562 7.73592042922974 CD8+ Tem -Sample_1_GTAGGCCGTAACGTTC-1 -0.085719883441925 -9.89418888092041 NK cells -Sample_1_GTATCTTTCCTATGTT-1 2.58372282981873 7.10137033462524 CD8+ Tcm -Sample_1_GTATTCTCAGCTGCTG-1 3.51512503623962 6.91034078598022 CD8+ Tem -Sample_1_GTATTCTGTCCCTACT-1 0.819347321987152 2.55777430534363 CD8+ Tcm -Sample_1_GTATTCTTCCAATGGT-1 3.17723178863525 5.15378618240356 CD8+ Tem -Sample_1_GTCACAAGTCCGAAGA-1 -1.10143232345581 8.11679267883301 CD8+ T-cells -Sample_1_GTCACAAGTTCCACAA-1 -0.00161242228932679 -8.44725322723389 NK cells -Sample_1_GTCACGGCACCGGAAA-1 -1.85790908336639 7.6867299079895 CD4+ T-cells -Sample_1_GTCACGGGTTGCCTCT-1 5.13865041732788 5.92956209182739 CD8+ Tcm -Sample_1_GTCACGGTCAAGGCTT-1 -1.69250202178955 1.36277711391449 CD8+ Tcm -Sample_1_GTCATTTAGCTTTGGT-1 0.913913488388062 -9.86781406402588 NK cells -Sample_1_GTCATTTTCAGTTTGG-1 3.2378990650177 6.07441854476929 CD8+ Tem -Sample_1_GTCCTCAAGTATCGAA-1 -1.88364386558533 9.17751693725586 CD8+ Tcm -Sample_1_GTCCTCAAGTGCCATT-1 4.55810070037842 5.94284296035767 CD8+ Tem -Sample_1_GTCGGGTGTCCTCTTG-1 3.48605370521545 7.67852401733398 CD8+ Tem -Sample_1_GTCGTAATCCATGAGT-1 0.712278187274933 3.15098786354065 CD8+ Tcm -Sample_1_GTCTCGTAGTTCGCAT-1 -2.62067914009094 8.04558563232422 CD4+ T-cells -Sample_1_GTCTTCGGTATAAACG-1 3.36765074729919 3.47876286506653 CD8+ Tem -Sample_1_GTGAAGGAGTACGACG-1 1.64230263233185 -9.28687381744385 NK cells -Sample_1_GTGAAGGCAATAACGA-1 1.18194985389709 -8.54368877410889 NK cells -Sample_1_GTGAAGGCATGTTGAC-1 3.65819811820984 7.12100124359131 CD8+ Tem -Sample_1_GTGAAGGGTCCTCTTG-1 1.01911449432373 4.56774282455444 CD8+ Tcm -Sample_1_GTGAAGGTCATGTAGC-1 1.88991320133209 -4.98144006729126 NK cells -Sample_1_GTGCAGCAGCGATTCT-1 1.97823536396027 8.21100234985352 CD8+ Tem -Sample_1_GTGCAGCAGGCTACGA-1 4.96904230117798 4.73781299591064 CD8+ Tem -Sample_1_GTGCAGCTCAAAGACA-1 -2.11408162117004 7.25128841400146 CD8+ T-cells -Sample_1_GTGCATACATCCGGGT-1 2.59401774406433 -10.0361862182617 NK cells -Sample_1_GTGCATATCACCGTAA-1 1.41646754741669 -7.00791072845459 NK cells -Sample_1_GTGCATATCATTATCC-1 2.34949779510498 -9.26599597930908 NK cells -Sample_1_GTGCGGTAGAGGTACC-1 3.4325692653656 7.27686643600464 CD8+ Tem -Sample_1_GTGCGGTCAAGCCGTC-1 -0.0261060874909163 -8.94536685943604 NK cells -Sample_1_GTGCGGTCACATTCGA-1 -2.58474707603455 7.86465454101562 CD8+ Tcm -Sample_1_GTGCTTCAGCCAACAG-1 2.04113745689392 -7.75461864471436 NK cells -Sample_1_GTGGGTCTCAAGAAGT-1 -0.143787667155266 0.184897065162659 CD8+ Tem -Sample_1_GTGTGCGTCGGTGTCG-1 1.89520585536957 7.89379787445068 CD8+ Tem -Sample_1_GTGTTAGCATCCTAGA-1 2.98713135719299 -9.54645729064941 NK cells -Sample_1_GTGTTAGGTAGATTAG-1 3.19328880310059 6.71580076217651 CD8+ Tem -Sample_1_GTTACAGCATTAGGCT-1 -2.07037782669067 8.11500644683838 CD4+ T-cells -Sample_1_GTTCATTCATGCATGT-1 3.05845189094543 -10.6843824386597 NK cells -Sample_1_GTTCATTTCCAAACAC-1 -1.46215522289276 7.51512670516968 CD8+ T-cells -Sample_1_GTTCTCGTCTAGCACA-1 1.64257752895355 -9.90656185150146 NK cells -Sample_1_GTTTCTAAGATTACCC-1 -0.856835722923279 4.50320482254028 CD8+ Tem -Sample_1_GTTTCTAAGGTGCAAC-1 -0.79082852602005 6.44924545288086 CD8+ T-cells -Sample_1_GTTTCTACATCACGAT-1 -1.2471718788147 8.7328052520752 CD8+ T-cells -Sample_1_TAAACCGAGTTCGATC-1 4.17689800262451 -8.61411666870117 NK cells -Sample_1_TAAACCGGTCTGCGGT-1 2.21265935897827 6.44423198699951 CD8+ Tem -Sample_1_TAAACCGGTTCGCGAC-1 4.26833820343018 5.61408281326294 CD8+ Tem -Sample_1_TAAGAGAGTACTTAGC-1 -0.231787458062172 -9.1062707901001 NK cells -Sample_1_TAAGAGATCCGAAGAG-1 -2.22437620162964 8.64959335327148 CD4+ T-cells -Sample_1_TAAGCGTAGGACTGGT-1 1.11238598823547 -7.42065334320068 NK cells -Sample_1_TAAGTGCCAGACAAAT-1 3.06007885932922 -10.8409214019775 NK cells -Sample_1_TAAGTGCTCGCCATAA-1 1.33711624145508 -8.89894008636475 NK cells -Sample_1_TACACGAGTCACTTCC-1 2.62036275863647 -10.6512508392334 NK cells -Sample_1_TACACGAGTTTCGCTC-1 -3.44797801971436 8.82529067993164 CD8+ T-cells -Sample_1_TACACGATCCCAGGTG-1 0.328533351421356 1.93988871574402 CD8+ Tem -Sample_1_TACCTATGTCCAACTA-1 1.79840457439423 8.07206153869629 CD8+ Tem -Sample_1_TACCTTAAGCTAACAA-1 2.00481867790222 -10.1567783355713 NK cells -Sample_1_TACCTTATCTGGCGTG-1 3.44766330718994 -7.34216642379761 NK cells -Sample_1_TACGGGCCATCCTAGA-1 -0.773902714252472 6.37834692001343 CD8+ Tcm -Sample_1_TACGGGCCATTACCTT-1 -0.873016655445099 3.12933731079102 CD8+ Tcm -Sample_1_TACGGTAAGGATGGAA-1 2.57178568840027 6.06211090087891 CD8+ Tem -Sample_1_TACTCATTCGGCCGAT-1 -1.07941901683807 1.96940577030182 CD8+ Tem -Sample_1_TACTCGCCAACTGCGC-1 0.12674917280674 -9.52519512176514 NK cells -Sample_1_TACTCGCTCCGAATGT-1 -1.46638655662537 9.37839794158936 CD8+ T-cells -Sample_1_TACTCGCTCGTAGGTT-1 -0.709179699420929 7.927161693573 CD8+ T-cells -Sample_1_TACTTACTCCGTTGTC-1 0.193615168333054 -11.0330762863159 NK cells -Sample_1_TACTTGTGTACTTAGC-1 0.547445058822632 -8.65194034576416 NK cells -Sample_1_TAGACCAAGAGAGCTC-1 -0.923305094242096 9.42425537109375 CD8+ T-cells -Sample_1_TAGACCACAGTCCTTC-1 -0.254191100597382 7.69011116027832 CD8+ Tcm -Sample_1_TAGCCGGCAATGTTGC-1 2.23692178726196 5.39970970153809 CD8+ Tem -Sample_1_TAGCCGGTCGAGAACG-1 0.670314967632294 2.79208374023438 CD8+ Tcm -Sample_1_TAGGCATCAGTCCTTC-1 -0.212992161512375 4.94781446456909 Tregs -Sample_1_TAGGCATGTTGTACAC-1 -1.56503427028656 9.72060108184814 CD8+ T-cells -Sample_1_TAGTGGTCAAGGGTCA-1 1.79028570652008 -9.33463764190674 NK cells -Sample_1_TAGTGGTTCAATACCG-1 -2.43932199478149 7.5512843132019 CD8+ T-cells -Sample_1_TAGTGGTTCCTAGTGA-1 4.62346315383911 6.04124069213867 CD8+ Tcm -Sample_1_TAGTTGGAGTAGGCCA-1 1.70250725746155 -9.2817325592041 NK cells -Sample_1_TATCAGGCAACTGCTA-1 1.90206789970398 -8.08635997772217 NK cells -Sample_1_TATCAGGGTCGGATCC-1 2.33634972572327 5.65392637252808 CD8+ Tcm -Sample_1_TATCTCAAGACTTGAA-1 2.89245080947876 -8.42257881164551 NK cells -Sample_1_TATCTCAAGGGAGTAA-1 1.15609526634216 -9.3127908706665 NK cells -Sample_1_TATCTCAAGGTCATCT-1 -0.0732176303863525 4.14719820022583 CD8+ Tcm -Sample_1_TATCTCAGTCTACCTC-1 0.861790955066681 3.72557926177979 CD8+ Tcm -Sample_1_TATCTCATCATGTAGC-1 4.74549865722656 5.55421447753906 CD8+ Tcm -Sample_1_TATGCCCCAAGCTGTT-1 4.57893753051758 5.42203712463379 CD8+ Tem -Sample_1_TATGCCCCAATCACAC-1 -1.75137376785278 8.93626308441162 CD8+ T-cells -Sample_1_TATTACCCAAACCCAT-1 -1.08634078502655 9.86554718017578 CD8+ Tcm -Sample_1_TATTACCCACGAAAGC-1 -3.17634963989258 7.99935531616211 CD8+ T-cells -Sample_1_TATTACCGTGATGTGG-1 -0.757175803184509 -10.6268272399902 NK cells -Sample_1_TCAACGACAATCTACG-1 0.729838252067566 -9.01301383972168 NK cells -Sample_1_TCAACGATCCTTTCGG-1 -0.119923010468483 1.73784840106964 CD8+ Tcm -Sample_1_TCAATCTGTGCAGACA-1 -0.817590773105621 0.849547564983368 CD8+ Tem -Sample_1_TCAATCTGTGCGATAG-1 3.12782597541809 6.89581727981567 CD8+ Tem -Sample_1_TCACAAGCAATCGAAA-1 3.11376762390137 -9.75539970397949 NK cells -Sample_1_TCACAAGGTCTCATCC-1 -0.939577758312225 -9.22567844390869 NK cells -Sample_1_TCACGAACATACTACG-1 -0.983627140522003 0.339897841215134 CD8+ Tem -Sample_1_TCACGAAGTAGAAAGG-1 -0.174675762653351 0.925788164138794 CD8+ Tem -Sample_1_TCACGAATCATCGATG-1 3.16532158851624 5.26398658752441 CD8+ Tem -Sample_1_TCACGAATCGTCACGG-1 3.10678124427795 5.26355648040771 CD8+ Tem -Sample_1_TCAGATGAGTTGAGTA-1 -0.851998031139374 6.84454441070557 CD8+ Tcm -Sample_1_TCAGCAAGTGCTTCTC-1 4.46320247650146 6.54095649719238 CD8+ Tem -Sample_1_TCAGCTCAGACAGGCT-1 0.473435878753662 3.07445621490479 CD8+ Tem -Sample_1_TCAGCTCGTCGAGTTT-1 3.46569585800171 7.70187616348267 CD8+ Tem -Sample_1_TCAGGATTCATTTGGG-1 3.57357001304626 5.51153659820557 CD8+ Tem -Sample_1_TCAGGTAAGTCCGGTC-1 -1.08828246593475 3.27308392524719 CD8+ Tem -Sample_1_TCAGGTACAGGATTGG-1 -1.3678594827652 1.09354650974274 CD8+ Tem -Sample_1_TCAGGTAGTCCGTCAG-1 1.78806042671204 -6.79833126068115 NK cells -Sample_1_TCAGGTATCGAGGTAG-1 2.78911685943604 -9.50050926208496 NK cells -Sample_1_TCAGGTATCGCCTGTT-1 0.186782777309418 5.83754014968872 CD8+ Tcm -Sample_1_TCATTACCAGTATAAG-1 3.80675768852234 -9.31516075134277 NK cells -Sample_1_TCATTTGAGGCAATTA-1 3.26984238624573 7.00719356536865 CD8+ Tem -Sample_1_TCATTTGAGTGGTAGC-1 -1.83511877059937 0.763606548309326 CD8+ Tem -Sample_1_TCATTTGGTGTTGAGG-1 -0.40348955988884 0.527018845081329 NK cells -Sample_1_TCCACACAGTACACCT-1 -2.07267808914185 7.43482255935669 CD8+ Tcm -Sample_1_TCCACACCAGTTTACG-1 1.34894216060638 -10.5973358154297 NK cells -Sample_1_TCCCGATAGACAGGCT-1 -1.52165865898132 8.51216793060303 CD4+ T-cells -Sample_1_TCCCGATCAAGTTCTG-1 1.08454620838165 -8.11517524719238 NK cells -Sample_1_TCCCGATTCGTAGATC-1 -0.574307441711426 6.86248254776001 CD8+ Tcm -Sample_1_TCGAGGCGTAGAAGGA-1 -1.67881655693054 5.5960693359375 CD8+ Tcm -Sample_1_TCGAGGCGTTTACTCT-1 2.77362060546875 -7.10477542877197 NK cells -Sample_1_TCGAGGCTCCTCAACC-1 3.32093286514282 4.53740787506104 CD8+ Tem -Sample_1_TCGAGGCTCGTTGACA-1 2.26038861274719 -9.09016132354736 NK cells -Sample_1_TCGCGAGGTCAGTGGA-1 0.826019704341888 2.77624607086182 CD8+ Tem -Sample_1_TCGGGACCAGACGTAG-1 2.47859764099121 7.33025217056274 CD8+ Tem -Sample_1_TCGGGACGTGATAAGT-1 0.549019038677216 -8.88973331451416 NK cells -Sample_1_TCGGGACTCACCCGAG-1 -1.4236661195755 7.81136608123779 CD8+ T-cells -Sample_1_TCGGGACTCCGAAGAG-1 1.04633367061615 -9.42446517944336 NK cells -Sample_1_TCGGTAACACAGGCCT-1 2.70883774757385 5.68682098388672 CD8+ Tcm -Sample_1_TCGGTAACATTAACCG-1 2.62944936752319 -7.42343044281006 NK cells -Sample_1_TCGTACCAGATCTGCT-1 2.68780517578125 4.87401533126831 CD8+ Tcm -Sample_1_TCGTACCGTTTGACAC-1 0.988836824893951 -8.22246360778809 NK cells -Sample_1_TCGTACCTCAAACCGT-1 2.91508603096008 -6.93784999847412 NK cells -Sample_1_TCGTACCTCCCGACTT-1 3.98005962371826 4.68684387207031 CD8+ Tem -Sample_1_TCGTAGACATCTATGG-1 2.33761858940125 -9.18652248382568 NK cells -Sample_1_TCTATTGAGGCGACAT-1 3.19004487991333 7.47237157821655 CD8+ Tcm -Sample_1_TCTATTGGTAGCTCCG-1 2.08007287979126 8.28045749664307 CD8+ Tem -Sample_1_TCTCATAAGACTTTCG-1 -0.0148756196722388 0.413755983114243 CD8+ Tem -Sample_1_TCTCTAATCCGTAGTA-1 0.356642127037048 3.3492259979248 CD8+ Tem -Sample_1_TCTGAGAAGAGTGAGA-1 0.167540058493614 -10.5540132522583 NK cells -Sample_1_TCTGAGACACCACGTG-1 -0.394235789775848 0.657951951026917 CD8+ Tem -Sample_1_TCTGAGACAGATCTGT-1 2.25200581550598 4.95615243911743 CD8+ Tem -Sample_1_TCTGGAAAGTACGTTC-1 2.3894190788269 7.75733852386475 CD8+ Tem -Sample_1_TCTTCGGAGGGATGGG-1 3.40541625022888 7.54967021942139 CD8+ Tcm -Sample_1_TCTTTCCAGACTGGGT-1 2.91451907157898 -8.84024524688721 NK cells -Sample_1_TCTTTCCGTGCTTCTC-1 4.45303058624268 5.40031242370605 CD8+ Tem -Sample_1_TGAAAGATCCCTAATT-1 0.408669084310532 -11.5035591125488 NK cells -Sample_1_TGAAAGATCGTTGCCT-1 -1.74003314971924 0.677280843257904 CD8+ Tem -Sample_1_TGACAACTCCTACAGA-1 4.10102844238281 -9.4396858215332 NK cells -Sample_1_TGACTTTCAATGGATA-1 0.185648918151855 2.33132433891296 CD8+ Tem -Sample_1_TGACTTTTCTAGCACA-1 4.6494517326355 6.21598196029663 CD8+ Tem -Sample_1_TGAGAGGAGCAATCTC-1 3.26755475997925 -10.1487407684326 NK cells -Sample_1_TGAGCATGTCAAAGCG-1 -0.318619072437286 3.96842837333679 CD8+ Tcm -Sample_1_TGAGCCGGTGCCTTGG-1 0.380860716104507 -11.3137044906616 NK cells -Sample_1_TGAGGGAAGCATGGCA-1 -2.01061582565308 7.04821109771729 CD8+ T-cells -Sample_1_TGAGGGACATGGGAAC-1 -0.402147501707077 4.38231039047241 CD8+ Tem -Sample_1_TGAGGGATCACATGCA-1 3.75300097465515 5.03439044952393 CD8+ Tem -Sample_1_TGATTTCTCCTTCAAT-1 0.907620906829834 -8.50947666168213 NK cells -Sample_1_TGCACCTAGGGCACTA-1 1.94379246234894 -9.66688251495361 NK cells -Sample_1_TGCCCATCAGTAAGCG-1 2.71397686004639 4.52637004852295 CD8+ Tcm -Sample_1_TGCCCTAGTTCAACCA-1 0.544191718101501 -8.1515588760376 NK cells -Sample_1_TGCCCTATCAGGCAAG-1 -0.381988912820816 7.94350624084473 CD8+ Tcm -Sample_1_TGCGGGTAGACCTAGG-1 3.11841130256653 5.89002752304077 CD8+ Tem -Sample_1_TGCGGGTGTTATGTGC-1 1.86625289916992 -4.57099962234497 NK cells -Sample_1_TGCGTGGGTAATTGGA-1 2.65090322494507 -7.9413366317749 NK cells -Sample_1_TGCTACCAGCTCCCAG-1 -0.164288207888603 9.45863723754883 CD8+ T-cells -Sample_1_TGCTGCTGTCCAACTA-1 -0.170738756656647 5.44306707382202 CD8+ Tcm -Sample_1_TGGACGCAGGAGCGAG-1 -1.28355073928833 5.41598510742188 CD8+ Tcm -Sample_1_TGGACGCGTAACGCGA-1 3.76633501052856 7.44051599502563 CD8+ Tem -Sample_1_TGGCGCAAGTACGCGA-1 3.62111139297485 6.60442638397217 CD8+ Tcm -Sample_1_TGGCGCAGTTCCCGAG-1 0.417125910520554 -8.17689323425293 NK cells -Sample_1_TGGCGCATCCTACAGA-1 3.98059034347534 3.94790768623352 CD8+ Tem -Sample_1_TGGGAAGAGGAACTGC-1 -2.67890954017639 8.92380428314209 CD8+ T-cells -Sample_1_TGGGAAGTCTATCCTA-1 -1.16108965873718 -8.17358207702637 NK cells -Sample_1_TGGGCGTCAGTTAACC-1 2.37601184844971 7.94367790222168 CD8+ Tcm -Sample_1_TGGTTAGAGCGCCTTG-1 1.78255271911621 -9.43976020812988 NK cells -Sample_1_TGGTTAGAGGAATTAC-1 0.202621400356293 3.16776537895203 CD8+ Tem -Sample_1_TGGTTAGGTCAATACC-1 2.95137333869934 -7.68622827529907 NK cells -Sample_1_TGGTTCCTCCACTCCA-1 -0.133145228028297 5.59178066253662 CD8+ Tcm -Sample_1_TGTATTCAGATGTCGG-1 0.559718906879425 4.71241235733032 CD8+ Tcm -Sample_1_TGTATTCCAGCGTCCA-1 2.47919487953186 -9.31047630310059 NK cells -Sample_1_TGTATTCTCTGATACG-1 2.65503406524658 -9.29549503326416 NK cells -Sample_1_TGTCCCAAGAGACTAT-1 4.23468255996704 -9.66808795928955 NK cells -Sample_1_TGTCCCATCTTGTACT-1 2.05299663543701 -10.1142024993896 NK cells -Sample_1_TGTGGTACATGCAACT-1 -1.71205174922943 9.08261585235596 CD4+ T-cells -Sample_1_TGTGGTAGTGCATCTA-1 -1.38025176525116 0.385601788759232 CD8+ Tem -Sample_1_TGTGGTATCCCGACTT-1 3.94791197776794 4.88269948959351 CD8+ Tem -Sample_1_TGTGTTTAGGCCCTCA-1 4.42033195495605 -8.75133609771729 NK cells -Sample_1_TGTGTTTGTTGTGGAG-1 0.861023962497711 2.71898102760315 CD8+ Tem -Sample_1_TGTGTTTTCATGCTCC-1 4.20694208145142 6.62960815429688 CD8+ Tem -Sample_1_TGTTCCGAGCCACCTG-1 2.02483177185059 -7.66323566436768 NK cells -Sample_1_TGTTCCGCAGTGACAG-1 2.15780735015869 -4.7347297668457 CD8+ Tcm -Sample_1_TTAGGACAGGGTCGAT-1 -1.73990225791931 1.03959047794342 CD8+ Tem -Sample_1_TTAGGACGTGGTACAG-1 2.86032891273499 6.22198581695557 CD8+ Tcm -Sample_1_TTAGGCACATAAAGGT-1 3.91695690155029 7.83230876922607 CD8+ Tcm -Sample_1_TTAGTTCTCACTTACT-1 2.65172982215881 4.42386531829834 CD8+ Tem -Sample_1_TTAGTTCTCTTGAGAC-1 4.26221132278442 5.4747896194458 CD8+ Tem -Sample_1_TTATGCTTCACGAAGG-1 -0.320900499820709 9.24231719970703 CD8+ T-cells -Sample_1_TTCGGTCAGCCAGAAC-1 2.49619698524475 -11.1582651138306 NK cells -Sample_1_TTCGGTCAGTTCCACA-1 -9.78505706787109 -2.88191485404968 naive B-cells -Sample_1_TTCTACACATTACCTT-1 -0.912493407726288 0.903590738773346 CD8+ Tem -Sample_1_TTCTACATCGCAAGCC-1 3.59308815002441 4.6816234588623 CD8+ Tem -Sample_1_TTCTCAACACACTGCG-1 -0.525051534175873 4.81193590164185 CD8+ Tem -Sample_1_TTCTCCTGTTCCCGAG-1 2.76335906982422 -11.0394191741943 NK cells -Sample_1_TTCTCCTTCCGAATGT-1 3.08159613609314 5.49576997756958 CD8+ Tem -Sample_1_TTCTTAGGTTTAGGAA-1 1.8193222284317 2.29203128814697 CD8+ Tem -Sample_1_TTGAACGAGCCATCGC-1 1.70358169078827 -8.67632293701172 NK cells -Sample_1_TTGAACGGTAGCTCCG-1 1.77607333660126 3.53138470649719 CD8+ Tem -Sample_1_TTGAACGGTCTCAACA-1 0.216464161872864 0.14102204144001 CD8+ Tem -Sample_1_TTGAACGTCAGTTCGA-1 -0.885632991790771 8.57219505310059 CD8+ T-cells -Sample_1_TTGACTTTCCCTAACC-1 3.38709950447083 -9.91372966766357 NK cells -Sample_1_TTGCCGTCACCTTGTC-1 4.22200012207031 -8.53142833709717 NK cells -Sample_1_TTGCCGTTCTTGCAAG-1 2.00515246391296 5.09844970703125 CD8+ T-cells -Sample_1_TTGCGTCCAGCCTTGG-1 1.36465454101562 3.23980307579041 CD8+ Tem -Sample_1_TTGCGTCTCACGGTTA-1 -0.768053710460663 7.19588947296143 CD8+ Tcm -Sample_1_TTGCGTCTCCTATTCA-1 4.39566516876221 6.37975931167603 CD8+ Tem -Sample_1_TTGCGTCTCTTCTGGC-1 -1.27155423164368 5.38261890411377 CD8+ Tcm -Sample_1_TTGGAACGTCCATCCT-1 -0.50926810503006 -7.16424179077148 NK cells -Sample_1_TTGGCAAAGAGATGAG-1 3.38818669319153 -8.84610462188721 NK cells -Sample_1_TTGGCAAGTATCACCA-1 4.30581283569336 5.23382425308228 CD8+ Tem -Sample_1_TTGTAGGAGTCGTACT-1 4.05017566680908 4.9457688331604 CD8+ Tcm -Sample_1_TTTATGCAGCCACTAT-1 2.11655640602112 6.38171672821045 CD8+ Tcm -Sample_1_TTTATGCCAGTTAACC-1 4.27194786071777 6.75956535339355 CD8+ Tem -Sample_1_TTTATGCGTACGACCC-1 4.51594161987305 -8.65060520172119 NK cells -Sample_1_TTTGCGCAGGCTACGA-1 3.73736119270325 -10.3495903015137 NK cells -Sample_1_TTTGCGCTCACTATTC-1 4.00344753265381 5.35648918151855 CD8+ Tem -Sample_1_TTTGGTTTCTGTTTGT-1 -0.620654821395874 -0.052264254540205 CD8+ Tem -Sample_1_TTTGTCACAATTGCTG-1 2.45908951759338 2.93657040596008 CD8+ Tem -Sample_1_TTTGTCAGTAGGAGTC-1 -9.50566387176514 -2.59451222419739 Class-switched memory B-cells -Sample_1_TTTGTCATCCTCATTA-1 0.563555896282196 -11.2930526733398 NK cells -Sample_2_AAACCTGTCTGCTGTC-1 -0.490078926086426 0.549113750457764 NK cells -Sample_2_AAACGGGGTTTGCATG-1 -3.64654541015625 8.48738956451416 CD8+ Tcm -Sample_2_AAACGGGTCCTTTACA-1 4.32082033157349 5.82150983810425 CD8+ Tem -Sample_2_AAAGTAGCAAACAACA-1 -2.330397605896 7.41575479507446 CD8+ T-cells -Sample_2_AAAGTAGCATAGAAAC-1 -2.8954746723175 7.01710414886475 CD8+ T-cells -Sample_2_AAATGCCCATGATCCA-1 2.59175968170166 6.68973636627197 CD8+ Tem -Sample_2_AAATGCCGTTAGTGGG-1 -1.24871790409088 7.59334897994995 CD4+ T-cells -Sample_2_AAATGCCTCAGTTAGC-1 2.50271654129028 6.59390735626221 CD8+ Tem -Sample_2_AACACGTGTCGCTTCT-1 2.89007759094238 6.81137943267822 CD8+ Tem -Sample_2_AACCATGGTCTCACCT-1 -0.881959915161133 4.75376081466675 CD8+ Tcm -Sample_2_AACTCAGGTACCGTAT-1 1.61758422851562 -10.2320880889893 NK cells -Sample_2_AACTCAGGTCCATCCT-1 -0.492989033460617 -8.98588466644287 NK cells -Sample_2_AACTCAGGTCGCTTCT-1 3.44364976882935 7.31388521194458 CD8+ Tcm -Sample_2_AACTCAGTCCTTTCTC-1 1.84780240058899 -10.3979635238647 NK cells -Sample_2_AACTCTTCAAGTAATG-1 2.95634984970093 -7.24510526657104 NK cells -Sample_2_AACTCTTCACACATGT-1 -0.258985668420792 2.83517622947693 CD8+ Tem -Sample_2_AACTCTTGTGCCTGTG-1 -3.85883975028992 8.86901473999023 CD8+ T-cells -Sample_2_AACTCTTTCAAACAAG-1 2.84009861946106 -10.5207099914551 NK cells -Sample_2_AACTCTTTCCTCGCAT-1 -0.801875591278076 0.99469929933548 NK cells -Sample_2_AACTGGTGTGCGGTAA-1 2.94190096855164 6.61568784713745 CD8+ Tem -Sample_2_AACTTTCGTAGCGATG-1 2.36633157730103 -11.2723989486694 NK cells -Sample_2_AAGACCTAGATGTCGG-1 4.63610363006592 6.65094566345215 CD8+ Tem -Sample_2_AAGCCGCAGCTGAACG-1 2.95281982421875 -7.06207132339478 NK cells -Sample_2_AAGCCGCGTTCGCGAC-1 1.90649282932281 -4.3629994392395 CD8+ Tcm -Sample_2_AAGGAGCCAATACGCT-1 2.27291035652161 8.33000564575195 CD8+ Tcm -Sample_2_AAGGCAGAGAGGTAGA-1 -0.313212305307388 7.13910865783691 CD4+ Tem -Sample_2_AAGGCAGAGGAATCGC-1 -2.3313319683075 6.38703918457031 CD8+ Tcm -Sample_2_AAGGTTCGTCATATGC-1 -2.39826774597168 7.92004346847534 CD8+ T-cells -Sample_2_AAGGTTCTCAGTGCAT-1 2.75081443786621 6.81925868988037 CD8+ Tem -Sample_2_AAGTCTGAGAGGTAGA-1 2.21746158599854 -4.61439085006714 NK cells -Sample_2_AATCCAGCAAGCCCAC-1 1.52866888046265 -10.3515777587891 NK cells -Sample_2_AATCCAGGTAAGCACG-1 -0.652664244174957 1.24966681003571 NK cells -Sample_2_AATCCAGTCACAACGT-1 2.70746731758118 4.70324420928955 CD8+ Tem -Sample_2_AATCGGTCATCACAAC-1 4.1713695526123 5.05643939971924 CD8+ Tem -Sample_2_ACACCCTCAGCTGTGC-1 3.25817537307739 -9.19936370849609 NK cells -Sample_2_ACACCCTCATACCATG-1 1.85876524448395 -7.0362982749939 NK cells -Sample_2_ACACCCTGTCACAAGG-1 3.81666612625122 -8.16307163238525 NK cells -Sample_2_ACACTGAAGCTGAACG-1 -1.80179393291473 6.01932048797607 CD8+ T-cells -Sample_2_ACACTGATCTTACCTA-1 1.49532723426819 -9.90207004547119 NK cells -Sample_2_ACAGCCGAGCTACCGC-1 -0.299345016479492 -10.9248352050781 NK cells -Sample_2_ACAGCTAAGGCAGTCA-1 0.0687426030635834 -8.87082958221436 NK cells -Sample_2_ACATACGAGAGAACAG-1 2.02515482902527 5.26141357421875 CD8+ Tcm -Sample_2_ACATACGAGGCTAGAC-1 1.31729650497437 2.30223202705383 CD8+ Tem -Sample_2_ACATACGTCAACCAAC-1 4.1568078994751 -9.93806457519531 NK cells -Sample_2_ACATCAGAGGTCGGAT-1 1.74978625774384 -7.4006929397583 NK cells -Sample_2_ACATCAGGTCCGTTAA-1 3.64074635505676 -9.10654163360596 NK cells -Sample_2_ACATGGTGTCGCTTCT-1 4.60535192489624 6.80487251281738 CD8+ Tem -Sample_2_ACCAGTACACAACTGT-1 -0.900677025318146 6.28268575668335 CD8+ Tcm -Sample_2_ACCAGTATCCTCGCAT-1 4.63266372680664 6.16399812698364 CD8+ Tcm -Sample_2_ACCAGTATCTGAAAGA-1 1.85046684741974 -7.70588684082031 NK cells -Sample_2_ACCGTAACAAGAAGAG-1 -1.25323462486267 8.55003833770752 CD8+ Tcm -Sample_2_ACCGTAACATTGAGCT-1 0.523276209831238 4.48776054382324 CD8+ Tcm -Sample_2_ACCGTAATCGCTTGTC-1 4.93847465515137 6.10134792327881 CD8+ Tem -Sample_2_ACCTTTAAGCGATATA-1 -1.60482251644135 7.89704322814941 CD8+ T-cells -Sample_2_ACGAGCCAGACCTAGG-1 -1.7933337688446 9.19381523132324 CD8+ T-cells -Sample_2_ACGAGCCGTCTAGTCA-1 -1.04017198085785 -9.5222864151001 NK cells -Sample_2_ACGATACAGAGACGAA-1 3.57528924942017 5.17307281494141 CD8+ Tem -Sample_2_ACGATACCAATACGCT-1 4.17659568786621 -9.83602905273438 NK cells -Sample_2_ACGATACGTCTGCCAG-1 -0.0625262856483459 9.14486885070801 CD8+ T-cells -Sample_2_ACGATGTAGAGCTTCT-1 3.00420713424683 4.93508291244507 CD8+ Tem -Sample_2_ACGATGTCATTCACTT-1 3.05492854118347 6.09268951416016 CD8+ Tem -Sample_2_ACGATGTGTCATATCG-1 -0.184129402041435 4.12423133850098 CD8+ Tcm -Sample_2_ACGCAGCCAAGCGCTC-1 3.84883570671082 6.03940296173096 CD8+ Tem -Sample_2_ACGCCGAAGGTGTTAA-1 2.98343276977539 -9.8183012008667 NK cells -Sample_2_ACGGAGAAGACAGAGA-1 -1.15667343139648 5.1014256477356 CD8+ Tcm -Sample_2_ACGGCCAGTTGATTCG-1 -1.20367753505707 1.22011935710907 CD8+ Tem -Sample_2_ACGTCAAAGGTAGCTG-1 2.15204977989197 -9.13882446289062 NK cells -Sample_2_ACGTCAATCGGAGCAA-1 3.88373327255249 -9.93347835540771 NK cells -Sample_2_ACGTCAATCGGCATCG-1 3.54095673561096 6.88327932357788 CD8+ Tem -Sample_2_ACTATCTAGCTACCTA-1 1.17048394680023 -9.69287014007568 NK cells -Sample_2_ACTGAACGTCAATGTC-1 -1.05541348457336 8.75903034210205 CD8+ T-cells -Sample_2_ACTGAACTCCCAACGG-1 -1.58664488792419 9.07959938049316 CD8+ T-cells -Sample_2_ACTGCTCCATTAGCCA-1 3.75638747215271 -9.08351135253906 NK cells -Sample_2_ACTGTCCAGAAGAAGC-1 3.23553967475891 3.52504372596741 CD8+ Tem -Sample_2_ACTGTCCGTACCGCTG-1 3.05354285240173 5.32069110870361 CD8+ Tem -Sample_2_ACTTACTAGCCGGTAA-1 -0.647111356258392 6.39336824417114 CD4+ Tcm -Sample_2_ACTTACTGTACCGGCT-1 3.15500378608704 -10.5710678100586 NK cells -Sample_2_ACTTGTTCAGATGGCA-1 3.12691426277161 -8.43655776977539 NK cells -Sample_2_ACTTGTTTCACGATGT-1 1.8674328327179 8.36542320251465 CD8+ Tcm -Sample_2_ACTTTCACACAGTCGC-1 -1.37780356407166 9.32563877105713 CD8+ T-cells -Sample_2_ACTTTCAGTAAGGATT-1 3.40812635421753 3.73725628852844 CD8+ Tem -Sample_2_ACTTTCATCAGTTGAC-1 -3.07826089859009 7.76824903488159 CD4+ T-cells -Sample_2_AGAATAGTCAACACCA-1 0.513394296169281 -9.85814666748047 NK cells -Sample_2_AGAGCGAAGGTCATCT-1 2.38384199142456 7.48771047592163 CD8+ Tem -Sample_2_AGAGCGAGTACCGGCT-1 2.55985856056213 -9.12986660003662 NK cells -Sample_2_AGAGCTTAGGAGTTTA-1 0.757569968700409 -9.38083553314209 NK cells -Sample_2_AGAGCTTTCAAGAAGT-1 2.1169228553772 -7.37352514266968 NK cells -Sample_2_AGAGTGGTCCAGTAGT-1 -2.06863594055176 9.15886116027832 CD8+ Tcm -Sample_2_AGAGTGGTCCTTGACC-1 -2.16603922843933 6.62261199951172 CD4+ T-cells -Sample_2_AGCAGCCAGTTCCACA-1 -1.39186799526215 6.81519460678101 CD4+ T-cells -Sample_2_AGCCTAACAGTCTTCC-1 4.19441938400269 -8.50279903411865 NK cells -Sample_2_AGCGTATAGTGCAAGC-1 4.76222133636475 6.38078451156616 CD8+ Tem -Sample_2_AGCGTATGTGACGGTA-1 3.50631380081177 -8.90280151367188 NK cells -Sample_2_AGCGTCGCAGCGAACA-1 2.0508885383606 7.84936046600342 CD8+ Tcm -Sample_2_AGCTCCTTCAGAGCTT-1 0.265067994594574 -11.4641981124878 NK cells -Sample_2_AGCTCTCCATTTGCCC-1 2.25950384140015 -4.79206371307373 NK cells -Sample_2_AGCTTGAAGACTAGAT-1 2.37022662162781 -8.43184757232666 NK cells -Sample_2_AGCTTGAGTCTCACCT-1 -3.15956044197083 8.38435173034668 CD4+ T-cells -Sample_2_AGGCCACCAAGCGAGT-1 4.69372797012329 -8.89098834991455 NK cells -Sample_2_AGGCCACCAAGTAATG-1 4.4335298538208 -9.27685546875 NK cells -Sample_2_AGGCCACCACGGCTAC-1 0.602977216243744 2.32114458084106 CD8+ Tem -Sample_2_AGGCCACTCTCTTATG-1 1.45615410804749 -10.2027721405029 NK cells -Sample_2_AGGCCGTGTAGAGGAA-1 2.72038602828979 5.77423143386841 CD8+ Tcm -Sample_2_AGGCCGTTCATCGATG-1 5.02051305770874 6.81888341903687 CD8+ Tem -Sample_2_AGGGATGGTCTCAACA-1 -0.449371218681335 -0.02501873485744 CD8+ Tem -Sample_2_AGGGTGACAGTAAGAT-1 2.6294264793396 -9.95604705810547 NK cells -Sample_2_AGGTCATCACAGACTT-1 4.32317161560059 -9.59361457824707 NK cells -Sample_2_AGGTCATTCGAGAGCA-1 0.130787417292595 -8.65794658660889 NK cells -Sample_2_AGGTCCGCAGCTGTGC-1 2.16333436965942 -4.60969543457031 NK cells -Sample_2_AGTAGTCTCACTCCTG-1 1.30030786991119 4.48664236068726 CD8+ Tem -Sample_2_AGTCTTTAGCCTCGTG-1 -1.12294936180115 2.37903666496277 CD8+ Tcm -Sample_2_AGTCTTTAGGATTCGG-1 -1.00004613399506 4.97402715682983 CD8+ Tcm -Sample_2_AGTGAGGAGGCTCTTA-1 1.1366765499115 -9.53948593139648 NK cells -Sample_2_AGTGAGGGTGCACCAC-1 2.06195974349976 8.08732986450195 CD8+ Tem -Sample_2_AGTGAGGTCTGATACG-1 0.80098021030426 3.20799517631531 CD8+ Tcm -Sample_2_AGTGTCATCCTTTCGG-1 -0.828728377819061 2.52179622650146 CD8+ Tcm -Sample_2_AGTGTCATCTGGGCCA-1 -1.13049411773682 -8.10165977478027 NK cells -Sample_2_AGTGTCATCTTACCTA-1 -0.507210373878479 0.763081014156342 CD8+ Tem -Sample_2_AGTTGGTTCGTCCAGG-1 4.27675294876099 5.66985416412354 CD4+ Tem -Sample_2_ATAACGCAGTGGCACA-1 2.4228208065033 -4.83216571807861 NK cells -Sample_2_ATAACGCCAGTCAGAG-1 0.230245217680931 1.97227597236633 CD8+ Tcm -Sample_2_ATAACGCGTAGCTTGT-1 -2.02874135971069 8.3585786819458 CD8+ T-cells -Sample_2_ATAAGAGGTGCCTGGT-1 3.53997111320496 -9.68237590789795 NK cells -Sample_2_ATAGACCTCTTTACGT-1 3.13179802894592 -9.79790878295898 NK cells -Sample_2_ATCACGAAGTGGAGTC-1 -0.268428832292557 3.73414397239685 CD8+ Tem -Sample_2_ATCACGATCAGCAACT-1 -2.93929743766785 7.16215181350708 CD4+ T-cells -Sample_2_ATCATCTCAACACGCC-1 -2.7608847618103 6.73698854446411 CD8+ T-cells -Sample_2_ATCATGGAGGTGATAT-1 -0.58057051897049 -10.2264251708984 NK cells -Sample_2_ATCATGGCAGCCAGAA-1 -9.70353317260742 -2.79446578025818 naive B-cells -Sample_2_ATCCACCAGTAGATGT-1 -0.844080448150635 -9.38298416137695 NK cells -Sample_2_ATCCACCCATCGATGT-1 -9.6027193069458 -2.69187784194946 Memory B-cells -Sample_2_ATCCACCGTAACGACG-1 1.6488618850708 3.19818305969238 CD8+ Tem -Sample_2_ATCCGAAAGAAGATTC-1 1.16246068477631 -10.9371213912964 NK cells -Sample_2_ATCCGAACACTAGTAC-1 -0.298514664173126 -10.2400493621826 NK cells -Sample_2_ATCCGAAGTACACCGC-1 1.47890138626099 -8.03507423400879 NK cells -Sample_2_ATCGAGTCACAACTGT-1 3.62979173660278 6.66318655014038 CD8+ Tcm -Sample_2_ATCTACTCACGAGGTA-1 0.35524770617485 -9.78182792663574 NK cells -Sample_2_ATCTACTCATGGGACA-1 0.289614409208298 6.48829364776611 CD4+ Tcm -Sample_2_ATCTACTTCGGCCGAT-1 -1.16056311130524 8.30403614044189 CD8+ Tcm -Sample_2_ATCTGCCAGCGCTCCA-1 2.50581169128418 6.12142324447632 CD8+ Tcm -Sample_2_ATCTGCCAGTGACATA-1 3.3936619758606 -8.75055027008057 NK cells -Sample_2_ATCTGCCAGTTGAGTA-1 -0.54170298576355 -9.81553554534912 NK cells -Sample_2_ATCTGCCCACTTAACG-1 2.13407731056213 5.65158319473267 CD8+ Tcm -Sample_2_ATCTGCCCATCCCACT-1 1.33990383148193 -7.68851804733276 NK cells -Sample_2_ATCTGCCGTCTCTTTA-1 -0.649528920650482 2.95045685768127 CD8+ Tcm -Sample_2_ATCTGCCGTTAGTGGG-1 1.18494427204132 8.26185035705566 CD8+ Tcm -Sample_2_ATCTGCCTCGCTTAGA-1 0.416380047798157 4.09205341339111 CD8+ Tcm -Sample_2_ATGAGGGGTACCGTAT-1 -1.38384568691254 0.138669610023499 CD8+ Tem -Sample_2_ATGCGATGTTCTGTTT-1 3.50140786170959 5.31016731262207 CD8+ Tem -Sample_2_ATGGGAGAGTTGTAGA-1 3.06533694267273 -7.0898585319519 NK cells -Sample_2_ATGTGTGAGCATCATC-1 1.23980057239532 -10.3855648040771 NK cells -Sample_2_ATGTGTGAGGTCATCT-1 0.677724361419678 -8.79709243774414 NK cells -Sample_2_ATGTGTGGTGTATGGG-1 3.46640658378601 5.14710903167725 CD8+ Tem -Sample_2_ATTACTCAGGTCATCT-1 -0.416295945644379 -10.8151025772095 NK cells -Sample_2_ATTACTCGTGGAAAGA-1 1.73413217067719 -4.04146242141724 CD8+ Tcm -Sample_2_ATTATCCAGTAGCGGT-1 0.121281199157238 -10.4840250015259 NK cells -Sample_2_ATTATCCCACCCATGG-1 5.0090708732605 6.09070730209351 CD8+ Tcm -Sample_2_ATTCTACCAAGCGATG-1 -0.306306272745132 3.73010683059692 CD8+ Tem -Sample_2_ATTCTACCATTAACCG-1 0.931414842605591 5.88514566421509 CD8+ Tem -Sample_2_ATTCTACGTCTAGTCA-1 0.955424070358276 -7.98334217071533 NK cells -Sample_2_ATTCTACGTGGTAACG-1 -1.66443777084351 0.242027014493942 CD8+ Tem -Sample_2_ATTGGACCAGCGTTCG-1 -1.63098180294037 0.83291083574295 CD8+ Tcm -Sample_2_ATTGGACCAGCTTCGG-1 1.51930618286133 2.5130558013916 CD8+ Tem -Sample_2_ATTGGACGTAGCGTGA-1 -2.35958409309387 8.86671257019043 CD8+ T-cells -Sample_2_ATTGGACGTCGCATCG-1 -0.466469705104828 -7.53118848800659 NK cells -Sample_2_ATTGGTGAGCCCGAAA-1 3.87886953353882 7.92998504638672 CD8+ Tcm -Sample_2_CAACCAATCCTTGGTC-1 1.78140270709991 -10.9876022338867 NK cells -Sample_2_CAACTAGCATGGTAGG-1 0.523173570632935 -11.2812299728394 NK cells -Sample_2_CAACTAGTCGCATGGC-1 3.78208899497986 4.38339757919312 CD8+ Tem -Sample_2_CAAGAAAAGACTAAGT-1 -0.182187020778656 -9.82359313964844 NK cells -Sample_2_CAAGAAAAGTTAGCGG-1 -0.92163747549057 -9.37548828125 NK cells -Sample_2_CAAGAAATCTATGTGG-1 2.23352384567261 -8.80153369903564 NK cells -Sample_2_CAAGATCGTTACTGAC-1 -0.626469492912292 0.431938767433167 CD8+ Tem -Sample_2_CAAGATCGTTCAACCA-1 3.90511727333069 -8.92542839050293 NK cells -Sample_2_CAAGATCGTTCAGCGC-1 3.08817005157471 7.45178985595703 CD8+ Tem -Sample_2_CAAGATCGTTTAGGAA-1 -2.4654266834259 9.04079532623291 CD8+ T-cells -Sample_2_CAAGTTGGTAACGCGA-1 -2.1410870552063 6.00674247741699 CD8+ Tcm -Sample_2_CACAAACAGTTCGATC-1 3.46169996261597 4.51105213165283 CD8+ Tem -Sample_2_CACACAACAATCACAC-1 -0.959655046463013 8.5671443939209 CD8+ T-cells -Sample_2_CACACAAGTCGCGTGT-1 -0.399586260318756 6.32531642913818 CD8+ Tem -Sample_2_CACACCTCAAACCTAC-1 -0.102731004357338 -11.2875709533691 NK cells -Sample_2_CACACCTTCCCATTTA-1 3.37370896339417 -9.82248687744141 NK cells -Sample_2_CACACTCCAGTACACT-1 -0.0381368137896061 -10.5873222351074 NK cells -Sample_2_CACACTCCATGTAAGA-1 -0.911188900470734 9.77788162231445 CD8+ T-cells -Sample_2_CACACTCTCTAACCGA-1 1.23326289653778 1.44244313240051 CD8+ Tem -Sample_2_CACAGGCGTGGGTATG-1 1.8047137260437 -11.1056575775146 NK cells -Sample_2_CACAGTAAGTGACATA-1 -1.67017483711243 0.933240592479706 CD8+ Tem -Sample_2_CACATAGCAAGCCGCT-1 -2.40538668632507 8.72878837585449 CD4+ T-cells -Sample_2_CACATAGGTAGCAAAT-1 -0.120711632072926 -10.6269474029541 NK cells -Sample_2_CACATAGTCGCGTTTC-1 -2.36183404922485 7.04131889343262 CD8+ T-cells -Sample_2_CACATTTCATTGCGGC-1 -0.322303056716919 7.45219469070435 CD8+ Tcm -Sample_2_CACATTTTCCGTCATC-1 -1.74980771541595 8.02418231964111 CD4+ T-cells -Sample_2_CACCACTCACTCGACG-1 0.116703920066357 1.80954802036285 CD8+ Tem -Sample_2_CACCACTTCATACGGT-1 1.01820063591003 -8.42933368682861 NK cells -Sample_2_CACCACTTCCGCAAGC-1 1.96131932735443 -9.52595615386963 NK cells -Sample_2_CACCACTTCTTGAGAC-1 1.38165414333344 -8.03112411499023 NK cells -Sample_2_CACCAGGCACCTCGTT-1 -1.76517975330353 0.34677591919899 CD8+ Tem -Sample_2_CACCAGGTCAAGGTAA-1 1.85722279548645 -5.20342445373535 NK cells -Sample_2_CACCTTGTCTCTAAGG-1 0.392473191022873 6.09053182601929 CD4+ Tcm -Sample_2_CAGAGAGCATCACGTA-1 -0.0161797590553761 5.82370042800903 CD4+ Tem -Sample_2_CAGATCAAGATATGGT-1 4.78344917297363 5.24658536911011 CD8+ Tem -Sample_2_CAGCAGCGTCGCCATG-1 0.388050585985184 8.674147605896 CD8+ Tcm -Sample_2_CAGCAGCTCGGAATCT-1 -9.71193981170654 -2.80500936508179 Memory B-cells -Sample_2_CAGCAGCTCTGTCCGT-1 4.25414705276489 6.67632484436035 CD8+ Tem -Sample_2_CAGCCGAAGCGACGTA-1 1.77174460887909 -8.17306518554688 NK cells -Sample_2_CAGCGACAGTAGCGGT-1 1.94938325881958 -7.45058870315552 NK cells -Sample_2_CAGCTGGAGTGTACGG-1 2.83210229873657 -9.15941524505615 NK cells -Sample_2_CAGCTGGTCTTTCCTC-1 0.813422977924347 -10.1450634002686 NK cells -Sample_2_CAGTAACCATCTCGCT-1 -1.10551929473877 2.63267159461975 CD8+ Tem -Sample_2_CAGTAACTCTGTCCGT-1 -0.453981071710587 9.04695606231689 CD8+ Tcm -Sample_2_CAGTCCTAGACAAGCC-1 1.22171974182129 -7.55849075317383 NK cells -Sample_2_CAGTCCTAGATCTGCT-1 -2.43294644355774 8.37426471710205 CD8+ T-cells -Sample_2_CAGTCCTAGTAGATGT-1 4.30033540725708 4.6929178237915 CD8+ Tcm -Sample_2_CAGTCCTCAGGCGATA-1 3.48290491104126 6.6887354850769 CD4+ Tem -Sample_2_CAGTCCTGTTCAGCGC-1 3.30033564567566 4.32956504821777 CD8+ Tem -Sample_2_CAGTCCTTCTCTTGAT-1 3.40116262435913 4.92864513397217 CD8+ Tem -Sample_2_CATATGGTCTGTGCAA-1 2.10639381408691 -4.86203098297119 NK cells -Sample_2_CATATTCCAGTGACAG-1 -2.57765293121338 6.73637247085571 CD8+ Tcm -Sample_2_CATATTCGTAAATACG-1 2.4977343082428 8.13886165618896 CD8+ Tem -Sample_2_CATCAAGAGGTGTTAA-1 0.0492724478244781 5.4917631149292 CD8+ Tcm -Sample_2_CATCAAGCACGACGAA-1 5.21133041381836 6.63512754440308 CD8+ Tem -Sample_2_CATCAAGCAGGCAGTA-1 3.6913890838623 -9.57955932617188 NK cells -Sample_2_CATCAAGCATGTTGAC-1 0.917635023593903 2.62550806999207 CD8+ Tem -Sample_2_CATCAAGGTCTCCACT-1 3.85607004165649 -9.25901508331299 NK cells -Sample_2_CATCAGAAGAAACCAT-1 -0.123688906431198 5.47343921661377 CD8+ Tcm -Sample_2_CATCAGACACCCTATC-1 3.18976974487305 5.59975624084473 CD8+ Tcm -Sample_2_CATCAGAGTCCATGAT-1 0.528202414512634 -8.88026428222656 NK cells -Sample_2_CATCAGAGTCCGAAGA-1 -1.18055868148804 -0.112405799329281 CD8+ Tem -Sample_2_CATCCACGTCCCTTGT-1 -0.163310363888741 5.15359449386597 CD8+ Tcm -Sample_2_CATCCACGTGCATCTA-1 0.182706698775291 6.56506109237671 CD8+ Tcm -Sample_2_CATCCACTCTGAGGGA-1 2.65286135673523 -8.9774055480957 NK cells -Sample_2_CATCGAAAGGAATCGC-1 -1.2217024564743 7.03407144546509 CD8+ Tcm -Sample_2_CATCGAACAGGCTGAA-1 -2.79355835914612 7.46945524215698 CD8+ T-cells -Sample_2_CATCGAACAGGGAGAG-1 2.56699299812317 7.38778305053711 CD8+ Tem -Sample_2_CATCGGGCAAAGGTGC-1 -0.0260573625564575 -11.3374099731445 NK cells -Sample_2_CATGACAAGTGAACGC-1 1.77930700778961 -4.45737648010254 CD8+ Tem -Sample_2_CATGACACACAGACTT-1 1.63493812084198 -10.5269365310669 NK cells -Sample_2_CATGACACACCGTTGG-1 2.98568391799927 3.88332796096802 CD8+ Tem -Sample_2_CATGACACAGCTATTG-1 3.24907469749451 -9.18910121917725 NK cells -Sample_2_CATGACAGTCTCACCT-1 2.9936306476593 4.48337125778198 CD8+ Tem -Sample_2_CATGCCTCAATGAAAC-1 1.06246817111969 -9.78233623504639 NK cells -Sample_2_CATGGCGAGTGTGGCA-1 2.67200350761414 -6.86736392974854 NK cells -Sample_2_CATGGCGCACGAGGTA-1 2.29739046096802 -8.17120361328125 NK cells -Sample_2_CATGGCGGTACAGTGG-1 0.220493778586388 -8.48588466644287 NK cells -Sample_2_CATTATCAGGGTTCCC-1 2.48880004882812 -10.6197299957275 NK cells -Sample_2_CATTATCCACAGCCCA-1 -3.66858148574829 8.57568454742432 CD8+ Tcm -Sample_2_CATTATCGTGGTCTCG-1 2.43172335624695 6.05743312835693 CD8+ Tem -Sample_2_CCAATCCGTCTCTTAT-1 -0.754400670528412 9.67450046539307 CD8+ T-cells -Sample_2_CCAATCCTCTCATTCA-1 -1.41102147102356 8.86275768280029 CD8+ T-cells -Sample_2_CCACCTATCTTGCCGT-1 0.767095506191254 -11.1441736221313 NK cells -Sample_2_CCACGGACAATCACAC-1 3.48531818389893 -9.85309314727783 NK cells -Sample_2_CCACGGATCAGGCAAG-1 2.30445122718811 -4.8685474395752 NK cells -Sample_2_CCACTACAGTGGACGT-1 3.81238508224487 5.97278690338135 CD8+ Tem -Sample_2_CCAGCGAGTTCGTTGA-1 4.53498363494873 7.09426498413086 CD8+ Tem -Sample_2_CCATTCGCAAGCCATT-1 -1.40313637256622 9.00105762481689 CD8+ Tcm -Sample_2_CCCAATCTCAACACCA-1 3.69979310035706 7.36099433898926 CD8+ Tem -Sample_2_CCCTCCTAGTAGCCGA-1 2.54321932792664 -9.99652671813965 NK cells -Sample_2_CCCTCCTGTTGTACAC-1 -2.07157206535339 9.06001091003418 CD8+ T-cells -Sample_2_CCGGTAGAGTTACGGG-1 -2.02895927429199 9.03836059570312 CD8+ T-cells -Sample_2_CCGGTAGTCGCCTGAG-1 -0.30379256606102 -10.1836833953857 NK cells -Sample_2_CCGTACTCAGGGTATG-1 4.34455728530884 5.8697304725647 CD8+ Tcm -Sample_2_CCGTACTTCTACTATC-1 2.19050550460815 -10.2162351608276 NK cells -Sample_2_CCGTGGAAGCTGAAAT-1 1.62085175514221 6.99724054336548 CD8+ Tem -Sample_2_CCGTTCAAGCCCGAAA-1 1.03903639316559 -9.09964752197266 NK cells -Sample_2_CCTAAAGAGCGATTCT-1 3.45481276512146 5.17640256881714 CD8+ Tem -Sample_2_CCTAAAGCAAGTAATG-1 3.51051259040833 6.29619789123535 CD8+ Tem -Sample_2_CCTACACAGCAAATCA-1 -9.64228248596191 -2.73223996162415 CD8+ Tcm -Sample_2_CCTACACTCTTTACGT-1 3.09435629844666 -10.79185962677 NK cells -Sample_2_CCTACCACATCGGAAG-1 -1.07175958156586 0.0709773823618889 CD8+ Tcm -Sample_2_CCTAGCTAGAGGTACC-1 -1.77269721031189 8.33713626861572 CD4+ T-cells -Sample_2_CCTATTATCAAACCAC-1 1.88781785964966 8.09424304962158 CD8+ Tem -Sample_2_CCTCTGAGTAAGAGGA-1 -0.690892338752747 0.102271623909473 CD8+ Tem -Sample_2_CCTCTGAGTTATTCTC-1 2.49461460113525 7.51391506195068 CD8+ Tcm -Sample_2_CCTCTGATCAGCCTAA-1 0.619292616844177 -8.10032272338867 NK cells -Sample_2_CCTTACGAGCACCGCT-1 2.12799453735352 -9.46491146087646 NK cells -Sample_2_CCTTACGGTCGCATCG-1 1.91421008110046 -6.49885606765747 NK cells -Sample_2_CCTTCCCCAAACCTAC-1 -0.547998428344727 9.4435396194458 CD8+ T-cells -Sample_2_CCTTCCCTCACTGGGC-1 -2.1534583568573 7.05650329589844 CD8+ T-cells -Sample_2_CCTTCGAAGCGTTTAC-1 3.63080143928528 -8.35389518737793 NK cells -Sample_2_CCTTCGACAAGAAGAG-1 3.60039114952087 6.39253044128418 CD8+ Tem -Sample_2_CGAACATAGTCAAGCG-1 1.84329652786255 -6.73609781265259 NK cells -Sample_2_CGAACATCATCAGTCA-1 3.92343926429749 3.84774327278137 CD8+ Tem -Sample_2_CGAATGTGTCATTAGC-1 1.35752034187317 8.44132900238037 CD8+ Tem -Sample_2_CGACCTTAGCACCGCT-1 -0.193068310618401 5.49457693099976 CD8+ Tcm -Sample_2_CGACCTTAGTTGTAGA-1 -9.54714298248291 -2.63613820075989 Memory B-cells -Sample_2_CGACCTTTCAAGCCTA-1 -1.17123031616211 0.670337319374084 CD8+ Tcm -Sample_2_CGACTTCTCATAGCAC-1 3.12348318099976 -9.34591293334961 NK cells -Sample_2_CGAGCACAGACGCAAC-1 1.50253617763519 -10.2598571777344 NK cells -Sample_2_CGAGCACAGCTCAACT-1 5.34832239151001 6.55733633041382 CD8+ Tem -Sample_2_CGAGCACAGGCGTACA-1 -2.92095398902893 7.74431657791138 CD8+ T-cells -Sample_2_CGAGCACTCTGCAAGT-1 -2.81320667266846 7.77249622344971 CD8+ T-cells -Sample_2_CGAGCCACATGCAACT-1 -2.31915378570557 6.79165506362915 CD8+ T-cells -Sample_2_CGAGCCATCGTGGTCG-1 0.129045769572258 4.00837182998657 CD8+ Tcm -Sample_2_CGATCGGAGACGCTTT-1 -1.43082666397095 1.38790929317474 CD8+ Tcm -Sample_2_CGATCGGGTCCCTTGT-1 -0.497782498598099 1.18018436431885 CD8+ Tem -Sample_2_CGATGGCTCGGTCCGA-1 -0.595341742038727 9.70358943939209 CD8+ T-cells -Sample_2_CGATGTAAGGACATTA-1 2.262770652771 5.61354732513428 CD8+ Tem -Sample_2_CGATTGAGTGGTACAG-1 2.21263766288757 -7.1452522277832 NK cells -Sample_2_CGCCAAGAGACCACGA-1 -1.61572372913361 0.336304038763046 CD8+ Tem -Sample_2_CGCCAAGGTGTCCTCT-1 2.62102651596069 -11.1984996795654 NK cells -Sample_2_CGCGGTAAGGCATGGT-1 3.77258563041687 -8.61324214935303 NK cells -Sample_2_CGCGGTAAGTATCGAA-1 1.21839737892151 -10.4119844436646 NK cells -Sample_2_CGCGGTACACAGGCCT-1 -0.429587543010712 -9.71237850189209 NK cells -Sample_2_CGCGGTATCAACGCTA-1 1.34285998344421 -8.11648654937744 NK cells -Sample_2_CGCGGTATCTCTGCTG-1 -0.76593291759491 4.63291263580322 CD8+ Tem -Sample_2_CGCGTTTTCCAGATCA-1 1.55738854408264 2.28002500534058 CD8+ Tem -Sample_2_CGCTATCCACTGTCGG-1 -2.62551498413086 7.39648914337158 CD8+ T-cells -Sample_2_CGCTATCGTGCAGACA-1 1.52604925632477 -10.1507329940796 NK cells -Sample_2_CGCTATCTCGCCATAA-1 0.70492821931839 -7.9971399307251 NK cells -Sample_2_CGCTATCTCTGTACGA-1 0.696970283985138 4.67271137237549 CD8+ Tcm -Sample_2_CGCTGGATCCTAGGGC-1 -0.643948197364807 6.99590015411377 CD8+ Tcm -Sample_2_CGCTTCACATTTCAGG-1 3.2002637386322 4.86870288848877 CD8+ Tem -Sample_2_CGGACACAGCAGCGTA-1 -1.49595606327057 9.7742977142334 CD8+ Tcm -Sample_2_CGGACACAGGTGCTTT-1 0.325844705104828 -9.01515579223633 NK cells -Sample_2_CGGACACTCCTCGCAT-1 -1.136066198349 2.17664122581482 CD8+ Tem -Sample_2_CGGACGTTCAGGTTCA-1 2.91334700584412 6.23187828063965 CD8+ Tcm -Sample_2_CGGACTGTCTCTGAGA-1 -0.537001669406891 7.70639705657959 CD8+ Tcm -Sample_2_CGGAGCTCAAACTGTC-1 2.95291590690613 4.40727710723877 CD8+ T-cells -Sample_2_CGGAGCTGTACCGTTA-1 3.74634099006653 7.87159252166748 CD8+ Tem -Sample_2_CGGAGTCTCAGGCCCA-1 0.5074103474617 -10.1908931732178 NK cells -Sample_2_CGGCTAGCAATCTGCA-1 2.49887895584106 -7.14664554595947 NK cells -Sample_2_CGTAGCGCAAACAACA-1 3.30574679374695 -7.20064544677734 NK cells -Sample_2_CGTAGCGTCAGCACAT-1 2.6238067150116 -7.98223114013672 NK cells -Sample_2_CGTAGCGTCCTAGAAC-1 1.92870914936066 -4.61911725997925 NK cells -Sample_2_CGTAGGCGTCTGATTG-1 -0.339592933654785 7.75080966949463 CD8+ Tcm -Sample_2_CGTCAGGCAAGCTGTT-1 -0.0619976334273815 -9.20181846618652 NK cells -Sample_2_CGTCAGGTCAAGGTAA-1 -1.95469057559967 9.40045738220215 CD8+ T-cells -Sample_2_CGTCCATAGCTATGCT-1 2.18891310691833 -8.02189064025879 NK cells -Sample_2_CGTCCATCACCCTATC-1 3.72082686424255 5.04044342041016 CD8+ Tem -Sample_2_CGTCTACCATAGGATA-1 -1.37086188793182 9.56240749359131 CD8+ T-cells -Sample_2_CGTGAGCCATTCTTAC-1 1.96705269813538 8.01556968688965 CD8+ Tcm -Sample_2_CGTGAGCTCAATCTCT-1 -9.72149085998535 -2.81126403808594 naive B-cells -Sample_2_CGTTGGGTCAGAGACG-1 4.13262844085693 7.19426298141479 CD8+ Tem -Sample_2_CGTTGGGTCCTTGCCA-1 -1.01343011856079 5.69344091415405 CD8+ Tcm -Sample_2_CTAACTTAGCCCAGCT-1 3.52886343002319 5.49601984024048 CD8+ Tem -Sample_2_CTAAGACAGTGTCTCA-1 0.75559413433075 -10.5450859069824 NK cells -Sample_2_CTAAGACCAGATCGGA-1 2.67302942276001 7.93223142623901 CD8+ Tem -Sample_2_CTAAGACGTCGGATCC-1 1.77666974067688 -4.63154602050781 NK cells -Sample_2_CTAAGACTCCACTGGG-1 1.9324631690979 -5.01174306869507 NK cells -Sample_2_CTAATGGCACAGACTT-1 -1.30354022979736 7.69536733627319 CD8+ T-cells -Sample_2_CTAATGGGTTCAGGCC-1 1.93240475654602 -7.53331232070923 NK cells -Sample_2_CTACACCCAACGCACC-1 2.3537609577179 -8.66712951660156 NK cells -Sample_2_CTACACCGTTCAGCGC-1 3.58492398262024 -9.8335485458374 NK cells -Sample_2_CTACACCTCGGTTCGG-1 2.41098499298096 4.83375978469849 CD8+ Tem -Sample_2_CTACATTCATGCCCGA-1 2.68095064163208 -8.3710823059082 NK cells -Sample_2_CTACATTGTTTGGCGC-1 3.46601009368896 -7.99448347091675 NK cells -Sample_2_CTACCCAAGGTGCACA-1 2.44010090827942 -5.0979380607605 NK cells -Sample_2_CTACCCACAAGGTGTG-1 -1.78617238998413 8.09042930603027 CD8+ T-cells -Sample_2_CTACCCACACGACTCG-1 2.24034452438354 -7.04807043075562 NK cells -Sample_2_CTACCCACAGCGATCC-1 -0.0666037127375603 -10.9951772689819 NK cells -Sample_2_CTACCCATCCAAATGC-1 -0.615976870059967 -7.24832201004028 NK cells -Sample_2_CTACGTCAGAGCTGCA-1 -1.02769064903259 8.77914142608643 CD8+ T-cells -Sample_2_CTACGTCAGGCGATAC-1 2.86343550682068 7.27869176864624 CD8+ Tem -Sample_2_CTACGTCGTCTCAACA-1 2.16005802154541 -8.97551822662354 NK cells -Sample_2_CTACGTCTCGTCGTTC-1 0.717475891113281 -8.5037260055542 NK cells -Sample_2_CTAGAGTCAATCTACG-1 1.05786633491516 -9.72928905487061 NK cells -Sample_2_CTAGAGTCAGGAATCG-1 -0.591778039932251 0.673186957836151 CD8+ Tem -Sample_2_CTAGAGTTCAACTCTT-1 3.38687801361084 6.02981662750244 CD8+ Tem -Sample_2_CTAGTGACAGACGCTC-1 -1.47266352176666 6.9931583404541 CD8+ Tcm -Sample_2_CTAGTGACAGCGTAAG-1 -0.976550459861755 5.75491189956665 CD8+ Tcm -Sample_2_CTAGTGACATCTATGG-1 0.654601752758026 6.67149543762207 CD4+ Tcm -Sample_2_CTAGTGATCCTTGACC-1 -0.941399753093719 9.19360828399658 CD8+ Tcm -Sample_2_CTCACACTCACATACG-1 2.70308685302734 5.28052139282227 CD8+ Tem -Sample_2_CTCAGAATCGGAAATA-1 -1.13821828365326 2.00149893760681 CD8+ Tcm -Sample_2_CTCATTAAGTCGTTTG-1 0.0688078477978706 8.20681571960449 CD8+ Tcm -Sample_2_CTCCTAGGTCATACTG-1 2.04771852493286 -7.1206202507019 NK cells -Sample_2_CTCCTAGTCGGCGCTA-1 -1.16898572444916 0.936104297637939 CD8+ Tem -Sample_2_CTCGAAAAGAACAATC-1 0.532846987247467 5.54428720474243 CD8+ Tcm -Sample_2_CTCGAAAAGACGACGT-1 -0.874139249324799 3.31508922576904 CD8+ Tcm -Sample_2_CTCGAAACACAGCGTC-1 4.70609092712402 -9.0043249130249 NK cells -Sample_2_CTCGGGAGTTCGGCAC-1 1.80933499336243 6.53144311904907 CD8+ Tem -Sample_2_CTCGTCACACACTGCG-1 1.17533349990845 -9.53723812103271 NK cells -Sample_2_CTCGTCACACAGGAGT-1 0.701548278331757 -10.0405750274658 NK cells -Sample_2_CTCGTCAGTTCCACGG-1 4.4127836227417 4.45223236083984 CD8+ Tem -Sample_2_CTCTAATAGTCCGTAT-1 0.95601612329483 -11.2520427703857 NK cells -Sample_2_CTCTAATAGTGGGTTG-1 4.19468450546265 5.14185237884521 CD8+ Tem -Sample_2_CTCTACGAGGTGCACA-1 3.81541299819946 4.64831781387329 CD8+ Tem -Sample_2_CTCTACGTCGAACGGA-1 4.68153715133667 6.86170291900635 CD8+ Tem -Sample_2_CTCTGGTAGCGATATA-1 3.41120910644531 5.13582420349121 CD8+ Tem -Sample_2_CTCTGGTAGGGAACGG-1 -0.278810024261475 8.31703948974609 CD8+ Tcm -Sample_2_CTCTGGTAGGTCGGAT-1 3.02982354164124 7.55151605606079 CD8+ Tcm -Sample_2_CTCTGGTCACTGCCAG-1 3.35514616966248 3.86028575897217 CD8+ Tem -Sample_2_CTGAAACGTGCCTGTG-1 -0.13188011944294 -8.83624649047852 NK cells -Sample_2_CTGAAGTCACGAAGCA-1 -2.30964469909668 8.38949966430664 CD4+ T-cells -Sample_2_CTGAAGTTCCGCAAGC-1 2.23647546768188 -8.76334667205811 NK cells -Sample_2_CTGAAGTTCGCAAACT-1 -2.35367131233215 8.27389335632324 CD4+ T-cells -Sample_2_CTGATAGAGCGTCAAG-1 -0.207430496811867 9.69259357452393 CD8+ T-cells -Sample_2_CTGATCCTCATAACCG-1 4.76245307922363 6.04568719863892 CD8+ Tem -Sample_2_CTGATCCTCGCAAGCC-1 -2.42928433418274 8.29139518737793 CD8+ T-cells -Sample_2_CTGATCCTCTACTATC-1 -1.49048697948456 6.71158456802368 CD8+ T-cells -Sample_2_CTGCCTAAGAAGGTGA-1 0.220979258418083 -10.6752214431763 NK cells -Sample_2_CTGCCTAAGACAGAGA-1 0.838299453258514 2.79089736938477 CD8+ Tem -Sample_2_CTGCCTACAGACAGGT-1 4.10219526290894 -9.91577339172363 NK cells -Sample_2_CTGCTGTCAACGATCT-1 -0.0589476674795151 -7.82927417755127 NK cells -Sample_2_CTGGTCTCAACCGCCA-1 4.87493991851807 4.86474514007568 CD8+ Tem -Sample_2_CTGGTCTGTATCAGTC-1 -0.0585139207541943 -10.0522594451904 NK cells -Sample_2_CTGGTCTGTTGACGTT-1 2.06621980667114 7.77075910568237 CD8+ Tcm -Sample_2_CTGGTCTGTTGTCGCG-1 -1.71637654304504 8.71114349365234 CD8+ T-cells -Sample_2_CTGTGCTCACATGGGA-1 -0.482556700706482 9.39114856719971 CD8+ T-cells -Sample_2_CTGTGCTCACGGCCAT-1 3.16288828849792 5.20122289657593 CD8+ Tcm -Sample_2_CTGTGCTTCAGCGATT-1 3.72534990310669 6.72742986679077 CD8+ Tem -Sample_2_CTTAACTGTGGACGAT-1 -1.09564471244812 8.63945865631104 CD8+ Tcm -Sample_2_CTTACCGAGCGTCAAG-1 2.61703109741211 6.55156087875366 CD8+ Tem -Sample_2_CTTAGGAAGCCAACAG-1 -1.58060324192047 8.31639957427979 CD8+ T-cells -Sample_2_CTTCTCTAGTATCGAA-1 2.79227042198181 5.34777164459229 CD8+ Tem -Sample_2_CTTCTCTAGTGCGATG-1 1.48864996433258 -10.5585298538208 NK cells -Sample_2_CTTTGCGTCGGGAGTA-1 3.67751693725586 7.53460025787354 CD8+ Tem -Sample_2_GAAACTCAGTACGTTC-1 -0.0470688603818417 3.0260272026062 CD8+ Tem -Sample_2_GAAACTCGTCTGCCAG-1 1.41713547706604 1.63406538963318 NK cells -Sample_2_GAAATGAAGAAGGGTA-1 3.68726634979248 4.7767972946167 CD8+ Tem -Sample_2_GAAATGAGTGTTAAGA-1 3.86777877807617 6.83382415771484 CD8+ Tem -Sample_2_GAACATCTCCTTGGTC-1 0.303660601377487 8.9306058883667 CD8+ Tcm -Sample_2_GAACCTACAACACCCG-1 -1.58270299434662 8.75334548950195 CD4+ T-cells -Sample_2_GAACGGAAGCAAATCA-1 2.52289223670959 6.52110815048218 CD8+ Tcm -Sample_2_GAAGCAGGTTCAGGCC-1 2.20913100242615 -10.5493307113647 NK cells -Sample_2_GAAGCAGTCTGTGCAA-1 2.66300821304321 -11.0251178741455 NK cells -Sample_2_GAATAAGAGCGATTCT-1 2.38001608848572 5.55337285995483 CD8+ Tem -Sample_2_GAATAAGAGTTGAGAT-1 0.329577773809433 -9.29755306243896 NK cells -Sample_2_GAATAAGGTACCGTTA-1 -0.661956489086151 9.64391708374023 CD8+ T-cells -Sample_2_GAATAAGTCCACTCCA-1 0.392118364572525 -10.3640604019165 NK cells -Sample_2_GACACGCCACGACGAA-1 1.57430946826935 6.30031871795654 CD4+ Tem -Sample_2_GACACGCGTAACGTTC-1 -0.289780288934708 4.34227132797241 CD8+ Tcm -Sample_2_GACAGAGAGGCGTACA-1 1.80434787273407 -6.00570249557495 NK cells -Sample_2_GACAGAGTCGAGAACG-1 -1.27216899394989 0.478481978178024 CD8+ Tcm -Sample_2_GACCAATGTGAGCGAT-1 -0.446041494607925 3.66822671890259 CD8+ Tem -Sample_2_GACCTGGCACTTCGAA-1 2.1007833480835 -9.66742134094238 NK cells -Sample_2_GACCTGGCAGTAAGAT-1 -0.798046827316284 0.0232799798250198 CD8+ Tem -Sample_2_GACGGCTGTTGATTGC-1 -0.0369717329740524 -10.1397943496704 NK cells -Sample_2_GACGGCTGTTTGGGCC-1 3.49552464485168 8.18905067443848 CD8+ Tem -Sample_2_GACGGCTTCAACGCTA-1 3.13623714447021 5.66573047637939 CD8+ Tem -Sample_2_GACGTGCTCACCATAG-1 -1.16980254650116 0.251904189586639 CD8+ Tem -Sample_2_GACGTTAAGAATAGGG-1 1.14140188694 -8.85609149932861 NK cells -Sample_2_GACGTTAAGTGTACGG-1 -1.22859585285187 7.80867338180542 CD8+ T-cells -Sample_2_GACTACAAGTACACCT-1 3.78286933898926 7.79269695281982 CD8+ Tem -Sample_2_GACTACATCGGAATCT-1 1.88375401496887 -8.96599292755127 NK cells -Sample_2_GACTGCGGTTCGGCAC-1 5.10640668869019 5.99217557907104 CD8+ Tem -Sample_2_GACTGCGTCAGTTGAC-1 -1.63415563106537 8.1964168548584 CD4+ T-cells -Sample_2_GACTGCGTCTGTACGA-1 -2.64307260513306 8.40739154815674 CD8+ T-cells -Sample_2_GAGCAGAAGTGCTGCC-1 -2.55199003219604 8.84134006500244 NK cells -Sample_2_GAGCAGAGTCTTGATG-1 -0.275412797927856 -8.9815502166748 NK cells -Sample_2_GAGGTGAGTCCTAGCG-1 -3.94951558113098 8.89658069610596 CD8+ Tcm -Sample_2_GAGGTGATCCCTAACC-1 4.56758737564087 -7.99584007263184 CD8+ Tem -Sample_2_GATCAGTAGAGTTGGC-1 -1.61097073554993 8.34739780426025 CD8+ T-cells -Sample_2_GATCAGTAGTAGCGGT-1 -2.56796622276306 8.20053672790527 CD8+ T-cells -Sample_2_GATCAGTCAAAGCGGT-1 -0.728795945644379 8.57220458984375 CD8+ T-cells -Sample_2_GATCAGTTCCGAGCCA-1 0.703264772891998 -9.33327579498291 NK cells -Sample_2_GATCAGTTCCTCGCAT-1 -0.0764984488487244 -9.26644325256348 NK cells -Sample_2_GATCGATGTTCCCGAG-1 -1.44836151599884 7.96166038513184 CD8+ T-cells -Sample_2_GATCGCGAGACCTTTG-1 3.6006932258606 4.40932941436768 CD8+ Tem -Sample_2_GATCGCGCAAGAAAGG-1 2.40417647361755 -5.18708276748657 NK cells -Sample_2_GATCGCGGTCAACTGT-1 4.34507608413696 -8.33303642272949 NK cells -Sample_2_GATCGCGTCAAGGTAA-1 0.324859470129013 -7.14410257339478 NK cells -Sample_2_GATCGTAAGGTACTCT-1 -1.12324368953705 9.0920295715332 CD8+ T-cells -Sample_2_GATGAAAAGACACTAA-1 -2.09026455879211 8.66267395019531 CD8+ T-cells -Sample_2_GATGAAATCGTCTGAA-1 0.753444790840149 -10.0609703063965 NK cells -Sample_2_GATGAGGTCCTAGGGC-1 2.74209833145142 -7.49758052825928 NK cells -Sample_2_GATGCTAAGTCCCACG-1 -2.07711338996887 8.22933387756348 CD8+ T-cells -Sample_2_GATGCTACAAAGGAAG-1 3.42569279670715 4.30734157562256 CD8+ Tem -Sample_2_GATGCTAGTAGAGGAA-1 3.47193551063538 5.51085090637207 CD8+ Tem -Sample_2_GATTCAGAGACAGGCT-1 0.557960450649261 2.42450594902039 CD8+ Tem -Sample_2_GCAAACTCATCCCATC-1 0.127689123153687 1.80011403560638 CD8+ Tcm -Sample_2_GCAAACTGTGGACGAT-1 2.76135182380676 -7.56620359420776 NK cells -Sample_2_GCAATCAAGTAGGCCA-1 3.80027103424072 -9.60830497741699 NK cells -Sample_2_GCAATCAGTCTAGTCA-1 3.20101308822632 -7.67079734802246 NK cells -Sample_2_GCAATCATCACCCTCA-1 1.31010639667511 -8.7073802947998 NK cells -Sample_2_GCAGTTACAACGATGG-1 0.992745876312256 -10.5525493621826 NK cells -Sample_2_GCAGTTAGTTCAGCGC-1 -0.845603585243225 9.57477474212646 CD8+ T-cells -Sample_2_GCATACATCCTGCCAT-1 4.47138452529907 4.27621221542358 CD8+ Tem -Sample_2_GCATACATCTTTAGGG-1 2.60876774787903 -10.2735280990601 NK cells -Sample_2_GCATGCGGTCCCTTGT-1 0.634178280830383 5.52774381637573 CD4+ Tem -Sample_2_GCATGTAAGACGCACA-1 1.77095925807953 6.54305076599121 CD8+ Tem -Sample_2_GCATGTAAGAGTAATC-1 -1.79028022289276 1.33167827129364 CD8+ Tem -Sample_2_GCATGTACACCTCGTT-1 -0.642697811126709 -0.0606019273400307 NK cells -Sample_2_GCCAAATAGTGGAGAA-1 5.28751611709595 5.51163339614868 CD8+ Tem -Sample_2_GCCTCTACAAACTGCT-1 2.01838421821594 2.96047949790955 CD8+ Tem -Sample_2_GCGACCACATGCCCGA-1 4.58801031112671 4.8243522644043 CD8+ Tem -Sample_2_GCGAGAAAGCTACCGC-1 -1.86373209953308 8.97793674468994 CD8+ T-cells -Sample_2_GCGAGAACAAGGCTCC-1 2.09097218513489 6.96303749084473 CD8+ Tem -Sample_2_GCGAGAACACCAGTTA-1 0.734833955764771 -8.76655769348145 NK cells -Sample_2_GCGCAACAGGGTGTGT-1 -0.724202871322632 4.89528703689575 CD8+ Tcm -Sample_2_GCGCAACCACCCAGTG-1 1.32647740840912 -8.2830982208252 NK cells -Sample_2_GCGCAACTCCTATGTT-1 2.13918423652649 7.75530195236206 CD8+ Tem -Sample_2_GCGCAACTCTTGCATT-1 0.811457753181458 -7.41915321350098 NK cells -Sample_2_GCGCAGTTCCGTCATC-1 -0.410527855157852 -0.15557087957859 CD8+ Tem -Sample_2_GCGCCAACACCGTTGG-1 -3.93152570724487 8.96311378479004 CD8+ Tcm -Sample_2_GCGCGATTCCGTAGTA-1 2.87845993041992 -8.61576557159424 NK cells -Sample_2_GCGGGTTGTGCGCTTG-1 -1.04879868030548 5.33108425140381 CD8+ Tcm -Sample_2_GCTCCTAGTAGAAGGA-1 -0.240868091583252 0.754394233226776 CD8+ Tem -Sample_2_GCTCTGTCAAGTCTAC-1 0.0139041189104319 -9.63117504119873 NK cells -Sample_2_GCTGCAGAGTGGGATC-1 3.24108123779297 7.60893535614014 CD8+ Tcm -Sample_2_GCTGCAGCACCACCAG-1 1.09569346904755 -10.8765726089478 NK cells -Sample_2_GCTGCAGGTCGATTGT-1 0.630888223648071 -10.0789928436279 NK cells -Sample_2_GCTGCAGTCCTTTACA-1 1.9308944940567 2.29233169555664 CD8+ Tem -Sample_2_GCTGCAGTCGGAGGTA-1 4.01273107528687 -8.30885219573975 NK cells -Sample_2_GCTGCGAAGGCTACGA-1 -0.455357402563095 -0.0756356865167618 CD8+ Tem -Sample_2_GCTGCGAGTGTCAATC-1 -1.3524934053421 9.04055309295654 CD8+ Tcm -Sample_2_GCTGCGATCTGATACG-1 1.2120156288147 -8.5221700668335 NK cells -Sample_2_GCTGGGTCATATGCTG-1 -0.319922834634781 -9.51258182525635 NK cells -Sample_2_GCTTCCACACATCCGG-1 4.68958759307861 4.600013256073 CD8+ Tem -Sample_2_GCTTCCACATCTATGG-1 -0.674934148788452 -9.52346992492676 NK cells -Sample_2_GCTTCCATCTCTGTCG-1 0.502156376838684 4.26655960083008 CD8+ Tcm -Sample_2_GCTTGAACAGTATAAG-1 3.90642142295837 6.49509572982788 CD8+ Tcm -Sample_2_GCTTGAATCTAACCGA-1 0.165855452418327 -8.31894683837891 NK cells -Sample_2_GGAAAGCTCCTTGCCA-1 1.69597268104553 -8.24169445037842 NK cells -Sample_2_GGAACTTCAGATGGCA-1 4.12051916122437 4.91739416122437 CD4+ Tem -Sample_2_GGAATAACAAATTGCC-1 4.38854074478149 -8.47641372680664 NK cells -Sample_2_GGAATAACAGTTCATG-1 -2.27713060379028 9.05123043060303 CD8+ T-cells -Sample_2_GGACATTCACTTCGAA-1 2.73819208145142 -9.6597375869751 NK cells -Sample_2_GGAGCAACAAGCGCTC-1 2.35614085197449 6.38037872314453 CD8+ Tem -Sample_2_GGAGCAACATCTCCCA-1 -0.0190206374973059 6.22568559646606 CD4+ Tem -Sample_2_GGAGCAATCGCATGGC-1 -0.49411529302597 -7.05848169326782 NK cells -Sample_2_GGAGCAATCGTAGATC-1 -0.257333904504776 -11.098464012146 NK cells -Sample_2_GGATGTTAGTCCGTAT-1 -2.54227590560913 7.52298450469971 CD8+ T-cells -Sample_2_GGATTACCATTGAGCT-1 0.313053160905838 -9.60498237609863 NK cells -Sample_2_GGATTACTCACCAGGC-1 -0.139906793832779 5.21865463256836 CD8+ Tcm -Sample_2_GGCAATTAGAGGTAGA-1 3.6366171836853 4.40870189666748 CD8+ Tem -Sample_2_GGCAATTAGTGTTGAA-1 -1.60160446166992 1.36376941204071 CD8+ Tcm -Sample_2_GGCAATTCAGATTGCT-1 -0.430101990699768 3.6316819190979 CD8+ Tcm -Sample_2_GGCAATTGTCAAACTC-1 1.12596523761749 -8.58235740661621 NK cells -Sample_2_GGCAATTGTGATAAGT-1 4.76277256011963 4.38891935348511 CD8+ Tem -Sample_2_GGCCGATGTTTCCACC-1 2.78472232818604 -10.4785709381104 NK cells -Sample_2_GGCGACTCATCCTAGA-1 0.802654206752777 -9.03677368164062 NK cells -Sample_2_GGCGACTCATCCTTGC-1 -0.134599044919014 6.67172861099243 CD4+ Tcm -Sample_2_GGCGTGTCACATGACT-1 1.35419487953186 -8.93182468414307 NK cells -Sample_2_GGGAATGAGATGGCGT-1 -1.77495944499969 9.24043464660645 CD8+ T-cells -Sample_2_GGGAATGGTCATCCCT-1 -1.31121337413788 7.84960889816284 CD4+ T-cells -Sample_2_GGGACCTTCTTGGGTA-1 -0.245622664690018 -0.248764559626579 CD8+ Tem -Sample_2_GGGAGATAGTGTTTGC-1 -0.603672981262207 8.72039222717285 CD8+ T-cells -Sample_2_GGGAGATCAGATGGCA-1 0.288437187671661 4.33993577957153 CD8+ Tcm -Sample_2_GGGAGATCATACCATG-1 -1.68566536903381 8.01160049438477 CD8+ Tcm -Sample_2_GGGAGATGTCAAACTC-1 4.45819807052612 -9.67481803894043 NK cells -Sample_2_GGGATGAGTCGTCTTC-1 3.16674709320068 6.18274354934692 CD8+ Tem -Sample_2_GGGATGATCAGTTCGA-1 1.56116890907288 6.39014387130737 CD4+ Tem -Sample_2_GGGCACTAGCAGGTCA-1 3.61108350753784 -8.46444702148438 NK cells -Sample_2_GGGCACTCAAAGCAAT-1 -0.940939664840698 3.90363097190857 CD8+ Tem -Sample_2_GGGCACTTCCTAGGGC-1 -1.7984334230423 7.94485807418823 CD4+ T-cells -Sample_2_GGGCACTTCTATCGCC-1 0.908063471317291 -9.57259464263916 NK cells -Sample_2_GGGCATCCACGGCGTT-1 0.0772609561681747 -9.08860969543457 NK cells -Sample_2_GGGCATCCATCACAAC-1 0.105370059609413 3.53539514541626 CD8+ Tcm -Sample_2_GGGCATCGTTCCTCCA-1 5.09278631210327 5.5049204826355 CD8+ Tem -Sample_2_GGGCATCTCCGCATCT-1 2.21695756912231 5.51585245132446 CD8+ Tem -Sample_2_GGTATTGGTGAGGGTT-1 -2.28631472587585 6.9326343536377 CD8+ T-cells -Sample_2_GGTATTGGTTCCCGAG-1 0.174898311495781 3.95622086524963 CD8+ Tcm -Sample_2_GGTGAAGAGAGGTAGA-1 0.96459025144577 2.09068655967712 CD8+ Tem -Sample_2_GGTGAAGTCCATGAGT-1 3.62265682220459 6.30330801010132 CD8+ Tem -Sample_2_GGTGAAGTCCTCATTA-1 2.31561636924744 -8.1111011505127 NK cells -Sample_2_GGTGCGTAGAGTACAT-1 1.30013704299927 -10.6613492965698 NK cells -Sample_2_GGTGCGTAGTACATGA-1 0.00674083223566413 5.63631010055542 CD8+ Tcm -Sample_2_GGTGTTAGTAGTAGTA-1 -0.841310143470764 0.291478186845779 CD8+ Tem -Sample_2_GTAACGTAGGACAGCT-1 2.13543128967285 6.18163537979126 CD8+ Tem -Sample_2_GTAACGTTCACTTACT-1 2.26708507537842 4.88862133026123 CD8+ Tem -Sample_2_GTAACTGCATCGGTTA-1 3.72936224937439 -9.24002838134766 NK cells -Sample_2_GTAACTGTCCGAACGC-1 -0.591503441333771 -7.26344013214111 NK cells -Sample_2_GTACGTAAGACACTAA-1 -0.802965998649597 -6.88883113861084 CD8+ Tem -Sample_2_GTACGTACATGCTGGC-1 2.94774031639099 5.97824478149414 CD8+ Tem -Sample_2_GTACGTATCCTGTAGA-1 -2.65919256210327 8.68787574768066 CD4+ T-cells -Sample_2_GTACTTTGTGTCAATC-1 -0.0365527719259262 8.50883769989014 CD8+ Tcm -Sample_2_GTAGGCCAGCGCTCCA-1 2.70624542236328 7.81864976882935 CD8+ Tcm -Sample_2_GTAGGCCGTAACGTTC-1 0.0443353764712811 -9.60340023040771 NK cells -Sample_2_GTATCTTTCCTATGTT-1 2.87174987792969 6.97519302368164 CD8+ Tem -Sample_2_GTATTCTCAGCTGCTG-1 3.47861528396606 6.56786918640137 CD8+ Tem -Sample_2_GTATTCTGTCCCTACT-1 0.765854597091675 2.53114652633667 CD8+ Tcm -Sample_2_GTATTCTTCCAATGGT-1 2.85680294036865 5.74490976333618 CD8+ Tem -Sample_2_GTCACAAGTCCGAAGA-1 -1.45028686523438 8.37578868865967 CD8+ T-cells -Sample_2_GTCACAAGTTCCACAA-1 -0.168279573321342 -8.10830783843994 NK cells -Sample_2_GTCACGGCACCGGAAA-1 -1.98417901992798 7.97107744216919 CD8+ T-cells -Sample_2_GTCACGGGTTGCCTCT-1 4.90126323699951 5.80808115005493 CD8+ Tcm -Sample_2_GTCACGGTCAAGGCTT-1 -1.73285377025604 1.31158447265625 CD8+ Tcm -Sample_2_GTCATTTAGCTTTGGT-1 1.18699812889099 -9.87060928344727 NK cells -Sample_2_GTCATTTTCAGTTTGG-1 3.1621241569519 6.34158229827881 CD8+ Tem -Sample_2_GTCCTCAAGTATCGAA-1 -1.42456471920013 9.12302875518799 CD8+ Tcm -Sample_2_GTCCTCAAGTGCCATT-1 4.46475553512573 5.98818349838257 CD8+ Tem -Sample_2_GTCGGGTGTCCTCTTG-1 3.58841300010681 7.65212202072144 CD8+ Tem -Sample_2_GTCGTAATCCATGAGT-1 0.678011059761047 3.62503743171692 CD8+ Tcm -Sample_2_GTCTCGTAGTTCGCAT-1 -2.46576762199402 8.27730369567871 CD4+ T-cells -Sample_2_GTCTTCGGTATAAACG-1 3.30279564857483 3.45396447181702 CD8+ Tem -Sample_2_GTGAAGGAGTACGACG-1 1.42178654670715 -9.41478061676025 NK cells -Sample_2_GTGAAGGCAATAACGA-1 1.49739336967468 -8.57739353179932 NK cells -Sample_2_GTGAAGGCATGTTGAC-1 3.9612991809845 7.29014682769775 CD8+ Tem -Sample_2_GTGAAGGGTCCTCTTG-1 0.346495002508163 4.75266218185425 CD8+ Tcm -Sample_2_GTGAAGGTCATGTAGC-1 1.81842517852783 -5.05217123031616 NK cells -Sample_2_GTGCAGCAGCGATTCT-1 1.71381461620331 8.48898029327393 CD8+ Tem -Sample_2_GTGCAGCAGGCTACGA-1 4.81126356124878 4.41991853713989 CD8+ Tem -Sample_2_GTGCAGCTCAAAGACA-1 -2.69243359565735 7.36370849609375 CD8+ T-cells -Sample_2_GTGCATACATCCGGGT-1 2.54055881500244 -9.76883602142334 NK cells -Sample_2_GTGCATATCACCGTAA-1 1.56337988376617 -7.05689907073975 NK cells -Sample_2_GTGCATATCATTATCC-1 2.30328178405762 -9.21470165252686 NK cells -Sample_2_GTGCGGTAGAGGTACC-1 3.43727016448975 7.61666774749756 CD8+ Tem -Sample_2_GTGCGGTCAAGCCGTC-1 0.356183260679245 -8.5522632598877 NK cells -Sample_2_GTGCGGTCACATTCGA-1 -2.40909481048584 7.73382234573364 CD8+ Tcm -Sample_2_GTGCTTCAGCCAACAG-1 2.13452196121216 -7.71556043624878 NK cells -Sample_2_GTGGGTCTCAAGAAGT-1 0.161444291472435 1.39933753013611 NK cells -Sample_2_GTGTTAGCATCCTAGA-1 3.0701892375946 -9.34256267547607 NK cells -Sample_2_GTGTTAGGTAGATTAG-1 2.93646121025085 5.89251327514648 CD8+ Tem -Sample_2_GTTACAGCATTAGGCT-1 -2.01941561698914 8.2223653793335 CD8+ Tcm -Sample_2_GTTCATTCATGCATGT-1 3.17137241363525 -10.7611799240112 NK cells -Sample_2_GTTCATTTCCAAACAC-1 -1.02676200866699 7.79853343963623 CD8+ T-cells -Sample_2_GTTCTCGTCTAGCACA-1 1.33371365070343 -9.78653144836426 NK cells -Sample_2_GTTTCTAAGATTACCC-1 -0.828245282173157 4.50760984420776 CD8+ Tem -Sample_2_GTTTCTAAGGTGCAAC-1 -1.04392397403717 5.96352005004883 CD8+ T-cells -Sample_2_GTTTCTACATCACGAT-1 -1.61111128330231 8.68826103210449 CD8+ T-cells -Sample_2_TAAACCGAGTTCGATC-1 4.24266576766968 -8.34085273742676 NK cells -Sample_2_TAAACCGGTCTGCGGT-1 2.20468544960022 6.19665002822876 CD8+ Tem -Sample_2_TAAACCGGTTCGCGAC-1 4.4120945930481 5.55164051055908 CD8+ Tcm -Sample_2_TAAGAGAGTACTTAGC-1 -0.0202916115522385 -9.08162021636963 NK cells -Sample_2_TAAGAGATCCGAAGAG-1 -1.73159539699554 9.28734493255615 CD8+ Tcm -Sample_2_TAAGCGTAGGACTGGT-1 1.01854777336121 -7.44385480880737 NK cells -Sample_2_TAAGTGCCAGACAAAT-1 3.16579866409302 -10.8557100296021 NK cells -Sample_2_TAAGTGCTCGCCATAA-1 1.17174112796783 -8.98999881744385 NK cells -Sample_2_TACACGAGTCACTTCC-1 2.68450808525085 -10.5971698760986 NK cells -Sample_2_TACACGAGTTTCGCTC-1 -3.67823910713196 8.80016708374023 CD8+ T-cells -Sample_2_TACACGATCCCAGGTG-1 0.486207693815231 2.20995116233826 CD8+ Tem -Sample_2_TACCTATGTCCAACTA-1 1.89273202419281 7.97818326950073 CD8+ Tem -Sample_2_TACCTTAAGCTAACAA-1 1.91757607460022 -10.0196628570557 NK cells -Sample_2_TACCTTATCTGGCGTG-1 3.39237856864929 -7.36124467849731 NK cells -Sample_2_TACGGGCCATCCTAGA-1 -0.941759824752808 6.41630840301514 CD8+ Tcm -Sample_2_TACGGGCCATTACCTT-1 -0.715539991855621 3.24874901771545 CD8+ Tcm -Sample_2_TACGGTAAGGATGGAA-1 2.4907169342041 6.81452178955078 CD8+ Tem -Sample_2_TACTCATTCGGCCGAT-1 -1.03990757465363 2.19286823272705 CD8+ Tem -Sample_2_TACTCGCCAACTGCGC-1 0.0640943571925163 -9.37134552001953 NK cells -Sample_2_TACTCGCTCCGAATGT-1 -1.23900187015533 9.40444374084473 CD8+ T-cells -Sample_2_TACTCGCTCGTAGGTT-1 -1.37959682941437 8.6184549331665 CD8+ T-cells -Sample_2_TACTTACTCCGTTGTC-1 -0.378066837787628 -10.5021018981934 NK cells -Sample_2_TACTTGTGTACTTAGC-1 0.601878225803375 -8.72785568237305 NK cells -Sample_2_TAGCCGGCAATGTTGC-1 2.08283996582031 5.39203262329102 CD8+ Tem -Sample_2_TAGCCGGTCGAGAACG-1 0.710172712802887 2.69971704483032 CD8+ Tcm -Sample_2_TAGGCATCAGTCCTTC-1 -0.199499294161797 4.88635444641113 Tregs -Sample_2_TAGGCATGTTGTACAC-1 -1.75997734069824 9.69057559967041 CD8+ T-cells -Sample_2_TAGTGGTCAAGGGTCA-1 1.8580836057663 -9.30741691589355 NK cells -Sample_2_TAGTGGTTCAATACCG-1 -2.42201852798462 7.58682680130005 CD8+ T-cells -Sample_2_TAGTGGTTCCTAGTGA-1 4.32615947723389 5.82360410690308 CD8+ Tcm -Sample_2_TAGTTGGAGTAGGCCA-1 2.10703587532043 -9.4180212020874 NK cells -Sample_2_TATCAGGCAACTGCTA-1 3.01266264915466 -7.66838932037354 NK cells -Sample_2_TATCAGGGTCGGATCC-1 2.10829472541809 5.77377796173096 CD8+ Tcm -Sample_2_TATCTCAAGACTTGAA-1 3.22497272491455 -8.35658359527588 NK cells -Sample_2_TATCTCAAGGGAGTAA-1 1.71579372882843 -9.6931209564209 NK cells -Sample_2_TATCTCAAGGTCATCT-1 0.20000909268856 4.17796468734741 CD8+ Tcm -Sample_2_TATCTCAGTCTACCTC-1 0.622728645801544 3.93293619155884 CD8+ Tcm -Sample_2_TATCTCATCATGTAGC-1 3.52879238128662 4.94586372375488 CD8+ Tem -Sample_2_TATGCCCCAAGCTGTT-1 4.35267782211304 6.29163122177124 CD8+ Tcm -Sample_2_TATGCCCCAATCACAC-1 -1.34835290908813 9.08397388458252 CD8+ T-cells -Sample_2_TATTACCCAAACCCAT-1 -1.20105111598969 9.64632320404053 CD8+ Tcm -Sample_2_TATTACCCACGAAAGC-1 -3.1008563041687 7.87512397766113 CD8+ T-cells -Sample_2_TATTACCGTGATGTGG-1 -0.726540684700012 -10.5975551605225 NK cells -Sample_2_TCAACGACAATCTACG-1 1.01282525062561 -8.72939014434814 NK cells -Sample_2_TCAACGATCCTTTCGG-1 -0.147374913096428 1.84160447120667 CD8+ Tem -Sample_2_TCAATCTGTGCAGACA-1 -0.750780582427979 0.921518206596375 CD8+ Tcm -Sample_2_TCAATCTGTGCGATAG-1 3.269047498703 7.31080436706543 CD8+ Tem -Sample_2_TCACAAGCAATCGAAA-1 3.19095134735107 -9.82450675964355 NK cells -Sample_2_TCACAAGGTCTCATCC-1 -0.998427093029022 -9.17161273956299 NK cells -Sample_2_TCACGAACATACTACG-1 -1.02685046195984 0.457566678524017 NK cells -Sample_2_TCACGAAGTAGAAAGG-1 -0.144474372267723 0.938789904117584 CD8+ Tcm -Sample_2_TCACGAATCATCGATG-1 2.66990828514099 5.40595579147339 CD8+ Tem -Sample_2_TCACGAATCGTCACGG-1 2.91738343238831 5.60171556472778 CD8+ Tcm -Sample_2_TCAGATGAGTTGAGTA-1 -0.855517029762268 7.07564783096313 CD8+ Tcm -Sample_2_TCAGCAAGTGCTTCTC-1 4.59517002105713 6.50900983810425 CD8+ Tcm -Sample_2_TCAGCTCAGACAGGCT-1 0.636192619800568 2.95896220207214 CD8+ Tem -Sample_2_TCAGCTCGTCGAGTTT-1 2.41833209991455 7.68106079101562 CD8+ Tcm -Sample_2_TCAGGATTCATTTGGG-1 3.02907824516296 6.09552049636841 CD8+ Tem -Sample_2_TCAGGTAAGTCCGGTC-1 -1.08664703369141 3.37329173088074 CD8+ Tcm -Sample_2_TCAGGTACAGGATTGG-1 -1.38645303249359 1.06985294818878 CD8+ Tem -Sample_2_TCAGGTAGTCCGTCAG-1 1.85578954219818 -6.78238821029663 NK cells -Sample_2_TCAGGTATCGAGGTAG-1 3.06301879882812 -9.57776165008545 NK cells -Sample_2_TCAGGTATCGCCTGTT-1 0.290255010128021 5.88131189346313 CD8+ Tcm -Sample_2_TCATTACCAGTATAAG-1 3.57920432090759 -9.31604099273682 NK cells -Sample_2_TCATTTGAGGCAATTA-1 3.15743279457092 6.95983982086182 CD8+ Tcm -Sample_2_TCATTTGAGTGGTAGC-1 -1.79879903793335 0.698711395263672 CD8+ Tem -Sample_2_TCATTTGGTGTTGAGG-1 -0.236364439129829 0.490620046854019 CD8+ Tem -Sample_2_TCCACACAGTACACCT-1 -1.88263213634491 7.53612089157104 CD8+ T-cells -Sample_2_TCCACACCAGTTTACG-1 1.18819570541382 -10.5198602676392 NK cells -Sample_2_TCCCGATAGACAGGCT-1 -1.82697546482086 8.10618495941162 CD8+ Tcm -Sample_2_TCCCGATCAAGTTCTG-1 1.32605218887329 -8.09475040435791 NK cells -Sample_2_TCCCGATTCGTAGATC-1 -0.34793484210968 6.82689762115479 CD8+ Tcm -Sample_2_TCGAGGCGTAGAAGGA-1 -1.93426334857941 5.83449268341064 CD8+ Tcm -Sample_2_TCGAGGCGTTTACTCT-1 -0.422722637653351 -7.51220035552979 CD8+ Tem -Sample_2_TCGAGGCTCCTCAACC-1 3.26434779167175 4.55835199356079 CD8+ Tem -Sample_2_TCGAGGCTCGTTGACA-1 1.96573960781097 -9.00004577636719 NK cells -Sample_2_TCGCGAGGTCAGTGGA-1 0.799896538257599 2.99936676025391 CD8+ Tem -Sample_2_TCGGGACCAGACGTAG-1 2.47797441482544 7.50278520584106 CD8+ Tem -Sample_2_TCGGGACGTGATAAGT-1 0.536046981811523 -8.67595291137695 NK cells -Sample_2_TCGGGACTCACCCGAG-1 -1.34791612625122 8.15101051330566 CD8+ T-cells -Sample_2_TCGGGACTCCGAAGAG-1 0.801810204982758 -9.76134395599365 NK cells -Sample_2_TCGGTAACACAGGCCT-1 2.70240354537964 5.71730661392212 CD8+ Tem -Sample_2_TCGGTAACATTAACCG-1 2.72554087638855 -7.28230905532837 NK cells -Sample_2_TCGTACCAGATCTGCT-1 2.74314451217651 4.74351167678833 CD8+ Tem -Sample_2_TCGTACCGTTTGACAC-1 1.24084913730621 -8.58538913726807 NK cells -Sample_2_TCGTACCTCAAACCGT-1 2.77638173103333 -6.88912534713745 NK cells -Sample_2_TCGTACCTCCCGACTT-1 3.91711211204529 4.83328151702881 CD8+ Tcm -Sample_2_TCGTAGACATCTATGG-1 2.32776689529419 -9.08304691314697 NK cells -Sample_2_TCTATTGAGGCGACAT-1 3.0478048324585 7.45929002761841 CD8+ Tcm -Sample_2_TCTATTGGTAGCTCCG-1 1.9036318063736 8.401930809021 CD8+ Tem -Sample_2_TCTCATAAGACTTTCG-1 -0.26843649148941 0.440858572721481 CD8+ Tem -Sample_2_TCTCTAATCCGTAGTA-1 0.106110207736492 3.64707207679749 CD8+ Tem -Sample_2_TCTGAGAAGAGTGAGA-1 0.0986613184213638 -10.573037147522 NK cells -Sample_2_TCTGAGACACCACGTG-1 -0.347568064928055 0.62399435043335 CD8+ Tem -Sample_2_TCTGAGACAGATCTGT-1 2.18160247802734 4.7471137046814 CD8+ Tem -Sample_2_TCTGGAAAGTACGTTC-1 2.5413863658905 7.87342262268066 CD8+ Tem -Sample_2_TCTTCGGAGGGATGGG-1 3.16142439842224 7.69505977630615 CD8+ Tcm -Sample_2_TCTTTCCAGACTGGGT-1 3.11197710037231 -9.09385108947754 NK cells -Sample_2_TCTTTCCGTGCTTCTC-1 3.96654582023621 5.12148332595825 CD8+ Tem -Sample_2_TGAAAGATCCCTAATT-1 0.413892805576324 -11.5104751586914 NK cells -Sample_2_TGAAAGATCGTTGCCT-1 -1.82062709331512 0.679070472717285 CD8+ Tcm -Sample_2_TGACAACTCCTACAGA-1 4.11157417297363 -9.47665309906006 NK cells -Sample_2_TGACTTTCAATGGATA-1 0.133321732282639 2.16348314285278 CD8+ Tcm -Sample_2_TGACTTTTCTAGCACA-1 3.91469502449036 5.64563322067261 CD8+ Tcm -Sample_2_TGAGAGGAGCAATCTC-1 3.32446098327637 -10.1729011535645 NK cells -Sample_2_TGAGCATGTCAAAGCG-1 -0.320151478052139 3.88992023468018 CD8+ Tcm -Sample_2_TGAGCCGGTGCCTTGG-1 0.392462402582169 -11.3297214508057 NK cells -Sample_2_TGAGGGAAGCATGGCA-1 -2.30605149269104 6.66576671600342 CD8+ T-cells -Sample_2_TGAGGGACATGGGAAC-1 -0.40620556473732 4.1514139175415 CD8+ Tem -Sample_2_TGAGGGATCACATGCA-1 3.93912601470947 5.13544416427612 CD8+ Tem -Sample_2_TGATTTCTCCTTCAAT-1 1.37932920455933 -8.09883308410645 NK cells -Sample_2_TGCACCTAGGGCACTA-1 1.89535689353943 -9.5971097946167 NK cells -Sample_2_TGCCCATCAGTAAGCG-1 2.47450041770935 4.81238031387329 CD8+ Tcm -Sample_2_TGCCCTAGTTCAACCA-1 0.430548876523972 -8.16486167907715 NK cells -Sample_2_TGCCCTATCAGGCAAG-1 -0.954828262329102 7.93054342269897 CD8+ Tcm -Sample_2_TGCGGGTAGACCTAGG-1 3.00772261619568 6.36842393875122 CD8+ Tem -Sample_2_TGCGGGTGTTATGTGC-1 1.81350755691528 -4.58076953887939 NK cells -Sample_2_TGCGTGGGTAATTGGA-1 2.59625315666199 -7.9819130897522 NK cells -Sample_2_TGCTACCAGCTCCCAG-1 -0.290158778429031 9.40306758880615 CD8+ Tcm -Sample_2_TGCTGCTGTCCAACTA-1 -0.206542328000069 5.74718332290649 CD8+ Tcm -Sample_2_TGGACGCAGGAGCGAG-1 -1.30097639560699 5.44703245162964 CD8+ Tcm -Sample_2_TGGACGCGTAACGCGA-1 3.69309878349304 7.7619194984436 CD8+ Tem -Sample_2_TGGCGCAAGTACGCGA-1 3.52826690673828 6.42247486114502 CD8+ Tcm -Sample_2_TGGCGCAGTTCCCGAG-1 0.596459746360779 -8.30012321472168 NK cells -Sample_2_TGGCGCATCCTACAGA-1 3.81291365623474 3.70639801025391 CD8+ Tem -Sample_2_TGGGAAGAGGAACTGC-1 -2.51700830459595 9.10831165313721 CD8+ T-cells -Sample_2_TGGGAAGTCTATCCTA-1 -1.09713327884674 -8.13563346862793 NK cells -Sample_2_TGGGCGTCAGTTAACC-1 2.53604459762573 7.96760654449463 CD8+ Tem -Sample_2_TGGGCGTGTGTTGGGA-1 3.93395209312439 6.29234218597412 CD8+ Tem -Sample_2_TGGTTAGAGCGCCTTG-1 1.68449985980988 -8.73291206359863 NK cells -Sample_2_TGGTTAGAGGAATTAC-1 0.226238220930099 3.15258741378784 CD8+ Tem -Sample_2_TGGTTAGGTCAATACC-1 1.79595255851746 -7.26984024047852 NK cells -Sample_2_TGGTTCCTCCACTCCA-1 -0.247537881135941 5.4781322479248 CD8+ Tcm -Sample_2_TGTATTCAGATGTCGG-1 0.655124366283417 5.0499529838562 CD8+ Tcm -Sample_2_TGTATTCCAGCGTCCA-1 2.73580384254456 -9.10747909545898 NK cells -Sample_2_TGTATTCTCTGATACG-1 2.79089879989624 -9.41042900085449 NK cells -Sample_2_TGTCCCAAGAGACTAT-1 4.28743362426758 -9.61738967895508 NK cells -Sample_2_TGTCCCATCTTGTACT-1 2.23207139968872 -9.7170524597168 NK cells -Sample_2_TGTGGTACATGCAACT-1 -1.52169525623322 8.8923168182373 CD8+ T-cells -Sample_2_TGTGGTAGTGCATCTA-1 -1.4170435667038 0.371904283761978 NK cells -Sample_2_TGTGGTATCCCGACTT-1 4.06054067611694 4.92706680297852 CD8+ Tem -Sample_2_TGTGTTTAGGCCCTCA-1 4.41744661331177 -8.75786590576172 NK cells -Sample_2_TGTGTTTGTTGTGGAG-1 0.886349081993103 2.75564980506897 CD8+ Tem -Sample_2_TGTGTTTTCATGCTCC-1 3.99766516685486 7.14530324935913 CD8+ Tcm -Sample_2_TGTTCCGAGCCACCTG-1 2.03159761428833 -7.48870182037354 NK cells -Sample_2_TGTTCCGCAGTGACAG-1 2.15023994445801 -4.81290817260742 NK cells -Sample_2_TTAGGACAGGGTCGAT-1 -1.65826022624969 1.2582323551178 CD8+ Tem -Sample_2_TTAGGACGTGGTACAG-1 2.78120112419128 6.07779550552368 CD8+ T-cells -Sample_2_TTAGGCACATAAAGGT-1 3.82346057891846 7.81411695480347 CD8+ Tcm -Sample_2_TTAGTTCTCACTTACT-1 2.80376887321472 5.07882785797119 CD8+ T-cells -Sample_2_TTAGTTCTCTTGAGAC-1 4.08466577529907 5.40344619750977 CD8+ Tem -Sample_2_TTATGCTTCACGAAGG-1 -0.663998782634735 9.07790470123291 CD8+ T-cells -Sample_2_TTCGGTCAGCCAGAAC-1 2.79841041564941 -11.091046333313 NK cells -Sample_2_TTCGGTCAGTTCCACA-1 -9.72986316680908 -2.86799764633179 naive B-cells -Sample_2_TTCTACACATTACCTT-1 -0.911657810211182 0.86281955242157 NK cells -Sample_2_TTCTACATCGCAAGCC-1 3.24749040603638 4.84483575820923 CD8+ Tem -Sample_2_TTCTCAACACACTGCG-1 -0.522373557090759 5.0238676071167 CD8+ Tem -Sample_2_TTCTCCTGTTCCCGAG-1 2.91026926040649 -10.9879932403564 NK cells -Sample_2_TTCTCCTTCCGAATGT-1 2.64249014854431 6.47498655319214 CD8+ Tcm -Sample_2_TTCTTAGGTTTAGGAA-1 1.80916798114777 2.36553573608398 CD8+ Tem -Sample_2_TTGAACGAGCCATCGC-1 1.69159650802612 -8.73082828521729 NK cells -Sample_2_TTGAACGGTAGCTCCG-1 2.7043342590332 3.82222485542297 CD8+ Tcm -Sample_2_TTGAACGGTCTCAACA-1 0.170126393437386 0.0854008719325066 NK cells -Sample_2_TTGAACGTCAGTTCGA-1 -0.970185399055481 8.64316844940186 CD8+ T-cells -Sample_2_TTGCCGTCACCTTGTC-1 4.17194795608521 -8.82023811340332 NK cells -Sample_2_TTGCCGTTCTTGCAAG-1 1.83742129802704 5.71507740020752 CD8+ Tcm -Sample_2_TTGCGTCCAGCCTTGG-1 1.74079310894012 3.53607535362244 CD8+ Tem -Sample_2_TTGCGTCTCACGGTTA-1 -1.0442887544632 6.89792537689209 CD8+ Tcm -Sample_2_TTGCGTCTCCTATTCA-1 4.28723382949829 6.82443141937256 CD8+ Tem -Sample_2_TTGCGTCTCTTCTGGC-1 -1.35015404224396 5.51687955856323 CD8+ Tem -Sample_2_TTGGCAAAGAGATGAG-1 3.7937273979187 -8.87756443023682 NK cells -Sample_2_TTGGCAAGTATCACCA-1 4.17854595184326 5.105149269104 CD8+ Tcm -Sample_2_TTGTAGGAGTCGTACT-1 4.14154291152954 6.30688381195068 CD8+ Tem -Sample_2_TTTATGCAGCCACTAT-1 2.19650101661682 6.07358264923096 CD8+ Tcm -Sample_2_TTTATGCCAGTTAACC-1 4.13388824462891 7.52402925491333 CD8+ Tem -Sample_2_TTTGCGCAGGCTACGA-1 3.66919803619385 -10.4027309417725 NK cells -Sample_2_TTTGCGCTCACTATTC-1 3.86760520935059 5.34841060638428 CD8+ Tem -Sample_2_TTTGGTTTCTGTTTGT-1 -0.489038825035095 -0.0497650764882565 NK cells -Sample_2_TTTGTCACAATTGCTG-1 2.46331763267517 2.93512606620789 CD8+ Tem -Sample_2_TTTGTCAGTAGGAGTC-1 -9.53912162780762 -2.62771511077881 Class-switched memory B-cells -Sample_2_TTTGTCATCCTCATTA-1 0.460877120494843 -11.0803117752075 NK cells -Sample_3_AAACCTGTCTGCTGTC-1 -0.396727502346039 0.387721180915833 CD8+ Tem -Sample_3_AAACGGGGTTTGCATG-1 -3.83718252182007 8.58635711669922 CD8+ Tcm -Sample_3_AAACGGGTCCTTTACA-1 4.34322357177734 5.61384153366089 CD8+ Tem -Sample_3_AAAGTAGCAAACAACA-1 -2.49119210243225 7.1802225112915 CD8+ T-cells -Sample_3_AAAGTAGCATAGAAAC-1 -2.82258176803589 6.88276767730713 CD8+ T-cells -Sample_3_AAATGCCCATGATCCA-1 2.92815351486206 6.47556447982788 CD8+ Tem -Sample_3_AAATGCCGTTAGTGGG-1 -1.58650505542755 7.27634811401367 CD8+ Tcm -Sample_3_AAATGCCTCAGTTAGC-1 2.498379945755 6.19723606109619 CD8+ Tem -Sample_3_AACACGTGTCGCTTCT-1 3.1306459903717 6.62193632125854 CD8+ Tem -Sample_3_AACCATGGTCTCACCT-1 -0.990986227989197 4.80664730072021 CD8+ Tcm -Sample_3_AACTCAGGTACCGTAT-1 1.60424482822418 -10.1467189788818 NK cells -Sample_3_AACTCAGGTCCATCCT-1 -0.408074021339417 -9.02249717712402 NK cells -Sample_3_AACTCAGGTCGCTTCT-1 3.51643991470337 7.16677331924438 CD8+ Tcm -Sample_3_AACTCAGTCCTTTCTC-1 1.11915254592896 -10.035961151123 NK cells -Sample_3_AACTCTTCACACATGT-1 -0.353020548820496 2.88753056526184 CD8+ Tem -Sample_3_AACTCTTGTGCCTGTG-1 -3.80397248268127 8.86623191833496 CD4+ T-cells -Sample_3_AACTCTTTCAAACAAG-1 2.90502262115479 -10.5730800628662 NK cells -Sample_3_AACTCTTTCCTCGCAT-1 -0.596313118934631 0.693148195743561 NK cells -Sample_3_AACTGGTGTGCGGTAA-1 3.03787159919739 6.39949607849121 CD8+ Tem -Sample_3_AACTTTCGTAGCGATG-1 2.31609535217285 -11.2041492462158 NK cells -Sample_3_AAGACCTAGATGTCGG-1 4.37214136123657 6.16730690002441 CD4+ Tem -Sample_3_AAGCCGCAGCTGAACG-1 2.85771131515503 -7.11361169815063 NK cells -Sample_3_AAGCCGCGTTCGCGAC-1 1.77475368976593 -4.3726978302002 NK cells -Sample_3_AAGGAGCCAATACGCT-1 2.27152299880981 8.27713012695312 CD8+ Tem -Sample_3_AAGGCAGAGAGGTAGA-1 -0.115325920283794 6.82826995849609 CD4+ Tcm -Sample_3_AAGGCAGAGGAATCGC-1 -2.09306001663208 6.01981401443481 CD4+ Tcm -Sample_3_AAGGTTCGTCATATGC-1 -2.74299478530884 7.44491863250732 CD8+ T-cells -Sample_3_AAGGTTCTCAGTGCAT-1 2.86806869506836 6.82628774642944 CD8+ Tem -Sample_3_AATCCAGCAAGCCCAC-1 1.47138679027557 -10.3342056274414 NK cells -Sample_3_AATCCAGGTAAGCACG-1 -0.467231422662735 1.04238557815552 NK cells -Sample_3_AATCCAGTCACAACGT-1 3.08766865730286 4.49657440185547 CD8+ Tem -Sample_3_AATCGGTCATCACAAC-1 4.18903589248657 5.03767585754395 CD8+ Tcm -Sample_3_ACACCCTCAGCTGTGC-1 3.20319080352783 -9.2072639465332 NK cells -Sample_3_ACACCCTCATACCATG-1 1.43962001800537 -7.34156942367554 NK cells -Sample_3_ACACTGAAGCTGAACG-1 -1.79644191265106 6.05264282226562 CD4+ T-cells -Sample_3_ACACTGATCTTACCTA-1 1.27592265605927 -9.93198490142822 NK cells -Sample_3_ACAGCCGAGCTACCGC-1 -0.363775879144669 -10.9093866348267 NK cells -Sample_3_ACAGCTAAGGCAGTCA-1 0.247418686747551 -9.07986259460449 NK cells -Sample_3_ACATACGAGAGAACAG-1 2.31117558479309 4.68346214294434 CD4+ Tem -Sample_3_ACATACGAGGCTAGAC-1 1.36954128742218 2.36726665496826 CD8+ Tem -Sample_3_ACATACGTCAACCAAC-1 4.19567441940308 -9.96702289581299 NK cells -Sample_3_ACATCAGAGGTCGGAT-1 1.63427793979645 -7.89524793624878 NK cells -Sample_3_ACATCAGGTCCGTTAA-1 3.42331194877625 -9.46917915344238 NK cells -Sample_3_ACATGGTGTCGCTTCT-1 4.8958592414856 6.52756357192993 CD8+ Tem -Sample_3_ACCAGTACACAACTGT-1 -0.959009647369385 6.33157157897949 CD8+ Tcm -Sample_3_ACCAGTATCCTCGCAT-1 4.76656293869019 6.09208631515503 CD4+ Tem -Sample_3_ACCAGTATCTGAAAGA-1 1.78755044937134 -7.95785188674927 NK cells -Sample_3_ACCGTAACAAGAAGAG-1 -1.90218698978424 8.56812763214111 CD4+ T-cells -Sample_3_ACCGTAACATTGAGCT-1 0.491711765527725 4.42844724655151 CD8+ Tcm -Sample_3_ACCGTAATCGCTTGTC-1 4.91420936584473 5.81713914871216 CD8+ Tem -Sample_3_ACCTTTAAGCGATATA-1 -2.0538227558136 7.76728296279907 CD8+ T-cells -Sample_3_ACGAGCCAGACCTAGG-1 -1.86618566513062 9.17316436767578 CD4+ T-cells -Sample_3_ACGAGCCGTCTAGTCA-1 -0.877739310264587 -9.48667335510254 NK cells -Sample_3_ACGATACAGAGACGAA-1 3.90209722518921 4.76808071136475 CD8+ Tem -Sample_3_ACGATACCAATACGCT-1 4.20173645019531 -10.0311260223389 NK cells -Sample_3_ACGATACGTCTGCCAG-1 -0.271163105964661 9.10234355926514 CD8+ T-cells -Sample_3_ACGATGTAGAGCTTCT-1 3.33252143859863 3.85839819908142 CD8+ Tem -Sample_3_ACGATGTCATTCACTT-1 3.31918668746948 5.66721820831299 CD4+ Tem -Sample_3_ACGATGTGTCATATCG-1 -0.0877501145005226 4.12014484405518 CD8+ Tcm -Sample_3_ACGCAGCCAAGCGCTC-1 3.70644879341125 5.83691930770874 CD8+ Tem -Sample_3_ACGCCGAAGGTGTTAA-1 3.23652791976929 -9.91287994384766 NK cells -Sample_3_ACGGAGAAGACAGAGA-1 -0.942581117153168 5.08545684814453 CD8+ Tcm -Sample_3_ACGGCCAGTTGATTCG-1 -1.43860626220703 1.24132108688354 CD8+ Tem -Sample_3_ACGTCAAAGGTAGCTG-1 2.24929547309875 -9.02688980102539 NK cells -Sample_3_ACGTCAATCGGAGCAA-1 3.85897278785706 -9.85359764099121 NK cells -Sample_3_ACGTCAATCGGCATCG-1 3.825026512146 7.09366989135742 CD8+ Tem -Sample_3_ACTATCTAGCTACCTA-1 1.46860766410828 -9.61578750610352 NK cells -Sample_3_ACTGAACGTCAATGTC-1 -0.927719235420227 8.45430088043213 CD8+ T-cells -Sample_3_ACTGAACTCCCAACGG-1 -1.98489236831665 8.68005847930908 CD8+ T-cells -Sample_3_ACTGCTCCATTAGCCA-1 3.59808611869812 -8.7819652557373 NK cells -Sample_3_ACTGTCCAGAAGAAGC-1 3.40765261650085 3.53222632408142 CD8+ Tem -Sample_3_ACTGTCCGTACCGCTG-1 3.06533145904541 5.22821521759033 CD8+ Tem -Sample_3_ACTTACTAGCCGGTAA-1 -0.906911849975586 6.39353132247925 CD4+ Tcm -Sample_3_ACTTACTGTACCGGCT-1 3.31644511222839 -10.3320016860962 NK cells -Sample_3_ACTTGTTCAGATGGCA-1 3.1761462688446 -8.44988918304443 NK cells -Sample_3_ACTTGTTTCACGATGT-1 1.82787525653839 8.43759250640869 CD8+ Tcm -Sample_3_ACTTTCACACAGTCGC-1 -1.79360401630402 8.95797443389893 CD8+ T-cells -Sample_3_ACTTTCAGTAAGGATT-1 3.54147958755493 3.55152058601379 CD8+ Tem -Sample_3_ACTTTCATCAGTTGAC-1 -3.17644476890564 7.40374279022217 CD4+ T-cells -Sample_3_AGAATAGTCAACACCA-1 0.392506182193756 -9.71506690979004 NK cells -Sample_3_AGAGCGAAGGTCATCT-1 2.75693893432617 7.17498111724854 CD8+ Tcm -Sample_3_AGAGCGAGTACCGGCT-1 2.5072934627533 -8.99595069885254 NK cells -Sample_3_AGAGCTTAGGAGTTTA-1 1.02433598041534 -9.96888542175293 NK cells -Sample_3_AGAGCTTTCAAGAAGT-1 1.96407246589661 -7.41730260848999 NK cells -Sample_3_AGAGTGGTCCAGTAGT-1 -1.94333338737488 8.08909797668457 CD8+ Tcm -Sample_3_AGAGTGGTCCTTGACC-1 -2.25890016555786 6.59494066238403 CD8+ T-cells -Sample_3_AGCAGCCAGTTCCACA-1 -1.55633556842804 6.77548742294312 CD4+ T-cells -Sample_3_AGCCTAACAGTCTTCC-1 4.29767560958862 -8.62322807312012 NK cells -Sample_3_AGCGTATAGTGCAAGC-1 4.75724792480469 6.28850173950195 CD8+ Tem -Sample_3_AGCGTATGTGACGGTA-1 3.4590756893158 -8.95581436157227 NK cells -Sample_3_AGCGTCGCAGCGAACA-1 2.84823393821716 7.77069330215454 CD8+ Tem -Sample_3_AGCTCCTTCAGAGCTT-1 0.321358174085617 -11.3885087966919 NK cells -Sample_3_AGCTCTCCATTTGCCC-1 2.44213700294495 -4.89895915985107 NK cells -Sample_3_AGCTTGAAGACTAGAT-1 2.04340934753418 -8.77906894683838 NK cells -Sample_3_AGCTTGAGTCTCACCT-1 -2.9917995929718 7.68621444702148 CD4+ T-cells -Sample_3_AGGCCACCAAGCGAGT-1 4.75440502166748 -8.95725727081299 NK cells -Sample_3_AGGCCACCAAGTAATG-1 4.33867454528809 -9.3503885269165 NK cells -Sample_3_AGGCCACCACGGCTAC-1 0.692148447036743 2.48933982849121 CD8+ Tcm -Sample_3_AGGCCACTCTCTTATG-1 1.55386888980865 -10.1784467697144 NK cells -Sample_3_AGGCCGTGTAGAGGAA-1 2.35371875762939 7.12572956085205 CD8+ Tem -Sample_3_AGGCCGTTCATCGATG-1 5.17563009262085 6.5079026222229 CD8+ Tem -Sample_3_AGGGATGGTCTCAACA-1 -0.557101845741272 0.000981950666755438 CD8+ Tcm -Sample_3_AGGGTGACAGTAAGAT-1 2.42304444313049 -10.0403137207031 NK cells -Sample_3_AGGTCATCACAGACTT-1 4.12796306610107 -9.71707916259766 NK cells -Sample_3_AGGTCATTCGAGAGCA-1 0.281883507966995 -8.8474178314209 NK cells -Sample_3_AGGTCCGCAGCTGTGC-1 2.15878820419312 -4.63976955413818 NK cells -Sample_3_AGTAGTCCATGTCTCC-1 -0.952153503894806 0.466458380222321 CD8+ Tem -Sample_3_AGTAGTCTCACTCCTG-1 1.12436473369598 4.61751317977905 CD8+ Tem -Sample_3_AGTCTTTAGCCTCGTG-1 -1.03326427936554 2.17112874984741 CD8+ Tcm -Sample_3_AGTCTTTAGGATTCGG-1 0.433452248573303 6.23020648956299 CD8+ Tcm -Sample_3_AGTGAGGAGGCTCTTA-1 1.41923403739929 -9.63059139251709 NK cells -Sample_3_AGTGAGGGTGCACCAC-1 2.18801426887512 8.10723400115967 CD8+ Tem -Sample_3_AGTGAGGTCTGATACG-1 0.641865670681 3.3295533657074 CD8+ Tcm -Sample_3_AGTGTCATCCTTTCGG-1 -0.879697144031525 2.61135959625244 CD8+ Tem -Sample_3_AGTGTCATCTGGGCCA-1 -1.07919895648956 -8.08193302154541 NK cells -Sample_3_AGTGTCATCTTACCTA-1 -0.523324847221375 0.556499242782593 CD8+ Tem -Sample_3_AGTTGGTTCGTCCAGG-1 4.3337025642395 5.50274419784546 CD4+ Tem -Sample_3_ATAACGCCAGTCAGAG-1 0.267599552869797 1.97525382041931 CD8+ Tcm -Sample_3_ATAACGCGTAGCTTGT-1 -2.19165349006653 7.94540214538574 CD8+ T-cells -Sample_3_ATAAGAGGTGCCTGGT-1 3.88685607910156 -9.45316410064697 NK cells -Sample_3_ATAGACCTCTTTACGT-1 2.35137796401978 -9.08162498474121 NK cells -Sample_3_ATCACGAAGTGGAGTC-1 -0.253079146146774 3.85497951507568 CD8+ Tcm -Sample_3_ATCACGATCAGCAACT-1 -2.90073919296265 6.94681215286255 CD8+ T-cells -Sample_3_ATCATCTCAACACGCC-1 -2.76819610595703 6.78422117233276 CD4+ T-cells -Sample_3_ATCATGGAGGTGATAT-1 -0.631397187709808 -10.2288970947266 NK cells -Sample_3_ATCATGGCAGCCAGAA-1 -9.78701114654541 -2.87741994857788 naive B-cells -Sample_3_ATCCACCAGTAGATGT-1 -0.832554996013641 -9.55251884460449 NK cells -Sample_3_ATCCACCCATCGATGT-1 -9.59557247161865 -2.68442964553833 Memory B-cells -Sample_3_ATCCACCGTAACGACG-1 1.49786484241486 3.0508017539978 CD8+ Tem -Sample_3_ATCCGAACACTAGTAC-1 -0.311287313699722 -10.2675170898438 NK cells -Sample_3_ATCCGAAGTACACCGC-1 1.64129877090454 -8.19408512115479 NK cells -Sample_3_ATCGAGTCACAACTGT-1 3.61128902435303 6.43721055984497 CD8+ Tcm -Sample_3_ATCTACTCACGAGGTA-1 0.265942722558975 -9.38722133636475 NK cells -Sample_3_ATCTACTCATGGGACA-1 0.168922677636147 6.2771143913269 CD8+ Tcm -Sample_3_ATCTACTTCGGCCGAT-1 -1.17216503620148 8.1036205291748 CD4+ Tcm -Sample_3_ATCTGCCAGCGCTCCA-1 2.5389769077301 5.99887466430664 CD8+ Tcm -Sample_3_ATCTGCCAGTGACATA-1 3.21087288856506 -8.87726879119873 NK cells -Sample_3_ATCTGCCAGTTGAGTA-1 -0.314184010028839 -9.78339862823486 NK cells -Sample_3_ATCTGCCCACTTAACG-1 1.04748630523682 5.30796718597412 CD8+ Tem -Sample_3_ATCTGCCCATCCCACT-1 1.46473073959351 -7.88158321380615 NK cells -Sample_3_ATCTGCCGTCTCTTTA-1 -0.522031188011169 3.0314359664917 CD8+ Tem -Sample_3_ATCTGCCGTTAGTGGG-1 0.694676697254181 8.461501121521 CD8+ Tem -Sample_3_ATCTGCCTCGCTTAGA-1 0.524760007858276 3.87449336051941 CD8+ Tcm -Sample_3_ATGAGGGGTACCGTAT-1 -1.50852835178375 0.15420638024807 CD8+ Tem -Sample_3_ATGCGATGTTCTGTTT-1 3.06881260871887 5.77031707763672 CD8+ Tem -Sample_3_ATGGGAGAGTTGTAGA-1 2.93261480331421 -6.99224948883057 NK cells -Sample_3_ATGTGTGAGCATCATC-1 1.0949045419693 -10.6816530227661 NK cells -Sample_3_ATGTGTGAGGTCATCT-1 0.928322017192841 -8.96856498718262 NK cells -Sample_3_ATGTGTGGTGTATGGG-1 3.27127385139465 4.23137331008911 CD8+ Tem -Sample_3_ATTACTCAGGTCATCT-1 -0.542717158794403 -10.6757259368896 NK cells -Sample_3_ATTACTCGTGGAAAGA-1 0.759462416172028 1.74820959568024 CD8+ Tcm -Sample_3_ATTATCCAGTAGCGGT-1 0.22003972530365 -10.4020357131958 NK cells -Sample_3_ATTATCCCACCCATGG-1 5.02305126190186 5.63511800765991 CD8+ Tcm -Sample_3_ATTCTACCAAGCGATG-1 -0.288484752178192 3.67431998252869 CD8+ Tcm -Sample_3_ATTCTACCATTAACCG-1 1.10585141181946 5.92396020889282 CD8+ Tem -Sample_3_ATTCTACGTCTAGTCA-1 1.01367950439453 -8.38042068481445 NK cells -Sample_3_ATTCTACGTGGTAACG-1 -1.57673525810242 0.26592555642128 CD8+ Tem -Sample_3_ATTGGACCAGCGTTCG-1 -1.78924512863159 0.794165968894958 CD8+ Tem -Sample_3_ATTGGACCAGCTTCGG-1 1.53935647010803 2.5507185459137 CD8+ Tcm -Sample_3_ATTGGACGTAGCGTGA-1 -2.56573247909546 7.91956233978271 CD8+ T-cells -Sample_3_ATTGGACGTCGCATCG-1 -0.481060236692429 -7.49798011779785 NK cells -Sample_3_ATTGGTGAGCCCGAAA-1 3.82445645332336 7.98057985305786 CD8+ Tem -Sample_3_CAACCAATCCTTGGTC-1 1.69249391555786 -10.6827373504639 NK cells -Sample_3_CAACTAGCATGGTAGG-1 0.458320200443268 -11.1998596191406 NK cells -Sample_3_CAAGAAAAGACTAAGT-1 -0.153314962983131 -9.97834491729736 NK cells -Sample_3_CAAGAAAAGTTAGCGG-1 -0.872168838977814 -9.41042518615723 NK cells -Sample_3_CAAGAAATCTATGTGG-1 1.59762406349182 -9.20169734954834 NK cells -Sample_3_CAAGATCGTTACTGAC-1 -0.548183560371399 0.619833946228027 NK cells -Sample_3_CAAGATCGTTCAACCA-1 3.98467016220093 -9.21397590637207 NK cells -Sample_3_CAAGATCGTTCAGCGC-1 3.08713340759277 6.41563653945923 CD8+ Tem -Sample_3_CAAGATCGTTTAGGAA-1 -2.74132370948792 8.52921390533447 CD4+ T-cells -Sample_3_CAAGTTGGTAACGCGA-1 -2.2722954750061 6.0800986289978 CD8+ T-cells -Sample_3_CACAAACAGTTCGATC-1 3.12134909629822 4.57727432250977 CD8+ Tem -Sample_3_CACACAACAATCACAC-1 -2.29870438575745 7.79659938812256 CD4+ T-cells -Sample_3_CACACAAGTCGCGTGT-1 -0.401744604110718 6.23093605041504 CD8+ Tem -Sample_3_CACACCTCAAACCTAC-1 0.000822234665974975 -11.2283906936646 NK cells -Sample_3_CACACCTTCCCATTTA-1 3.43436002731323 -9.8281946182251 NK cells -Sample_3_CACACTCCAGTACACT-1 0.489936143159866 -9.53086948394775 NK cells -Sample_3_CACACTCCATGTAAGA-1 -1.24106502532959 9.7615909576416 CD8+ T-cells -Sample_3_CACACTCTCTAACCGA-1 1.17571580410004 1.42682671546936 CD8+ Tem -Sample_3_CACAGGCGTGGGTATG-1 1.62724792957306 -11.1015548706055 NK cells -Sample_3_CACAGTAAGTGACATA-1 -1.72008860111237 0.82255631685257 CD8+ Tem -Sample_3_CACATAGCAAGCCGCT-1 -2.22038578987122 9.18139553070068 CD4+ T-cells -Sample_3_CACATAGGTAGCAAAT-1 0.00108729407656938 -11.0767421722412 NK cells -Sample_3_CACATAGTCGCGTTTC-1 -1.98076450824738 7.02449512481689 CD4+ T-cells -Sample_3_CACATTTCATTGCGGC-1 -0.272789716720581 7.47581624984741 CD4+ T-cells -Sample_3_CACATTTTCCGTCATC-1 -1.82262456417084 7.82293939590454 CD4+ T-cells -Sample_3_CACCACTCACTCGACG-1 0.097664900124073 1.85538160800934 CD8+ Tem -Sample_3_CACCACTTCATACGGT-1 1.17486882209778 -8.28981876373291 NK cells -Sample_3_CACCACTTCCGCAAGC-1 1.86311399936676 -9.52130508422852 NK cells -Sample_3_CACCACTTCTTGAGAC-1 1.50072681903839 -7.99178457260132 NK cells -Sample_3_CACCAGGCACCTCGTT-1 -1.77021992206573 0.216466546058655 CD8+ Tem -Sample_3_CACCAGGTCAAGGTAA-1 1.87831354141235 -5.23000288009644 NK cells -Sample_3_CACCTTGTCTCTAAGG-1 0.480559349060059 5.96506643295288 CD4+ Tcm -Sample_3_CAGAGAGCATCACGTA-1 -0.0417132638394833 5.70233678817749 Tregs -Sample_3_CAGATCAAGATATGGT-1 4.79084634780884 4.94593381881714 CD8+ Tem -Sample_3_CAGCAGCGTCGCCATG-1 -0.373294621706009 8.56947708129883 CD8+ Tcm -Sample_3_CAGCAGCTCGGAATCT-1 -9.67610836029053 -2.76709151268005 Memory B-cells -Sample_3_CAGCAGCTCTGTCCGT-1 4.86126279830933 6.23032569885254 CD8+ Tem -Sample_3_CAGCCGAAGCGACGTA-1 1.44806039333344 -8.60355377197266 NK cells -Sample_3_CAGCGACAGTAGCGGT-1 1.86695885658264 -7.30396175384521 NK cells -Sample_3_CAGCTGGAGTGTACGG-1 2.54253530502319 -9.10615921020508 NK cells -Sample_3_CAGCTGGTCTTTCCTC-1 0.628168404102325 -10.7242107391357 NK cells -Sample_3_CAGTAACCATCTCGCT-1 -1.07628655433655 2.66564011573792 CD8+ Tem -Sample_3_CAGTAACTCTGTCCGT-1 -1.0053699016571 9.11256885528564 CD8+ Tcm -Sample_3_CAGTCCTAGACAAGCC-1 1.19705903530121 -7.57151317596436 NK cells -Sample_3_CAGTCCTAGATCTGCT-1 -2.49235844612122 7.89328193664551 CD8+ T-cells -Sample_3_CAGTCCTAGTAGATGT-1 3.67368483543396 4.08617401123047 CD8+ Tem -Sample_3_CAGTCCTCAGGCGATA-1 3.56921362876892 6.08867025375366 CD8+ Tem -Sample_3_CAGTCCTGTTCAGCGC-1 3.23619914054871 4.30176401138306 CD8+ Tem -Sample_3_CAGTCCTTCTCTTGAT-1 3.9048273563385 4.57641935348511 CD8+ Tem -Sample_3_CATATGGTCTGTGCAA-1 2.19548916816711 -4.80521583557129 NK cells -Sample_3_CATATTCCAGTGACAG-1 -2.54221057891846 6.61751747131348 CD8+ Tcm -Sample_3_CATATTCGTAAATACG-1 2.79560446739197 8.09181594848633 CD8+ Tem -Sample_3_CATCAAGAGGTGTTAA-1 0.0237480234354734 5.25717210769653 CD8+ Tcm -Sample_3_CATCAAGCACGACGAA-1 5.16888856887817 6.38769674301147 CD8+ Tem -Sample_3_CATCAAGCAGGCAGTA-1 3.34976434707642 -9.60735702514648 NK cells -Sample_3_CATCAAGCATGTTGAC-1 0.865162372589111 2.66041231155396 CD8+ Tem -Sample_3_CATCAAGGTCTCCACT-1 3.80285286903381 -9.46530532836914 NK cells -Sample_3_CATCAGAAGAAACCAT-1 -0.108618125319481 5.64721250534058 CD8+ Tcm -Sample_3_CATCAGACACCCTATC-1 3.55324959754944 5.53215789794922 CD8+ Tcm -Sample_3_CATCAGAGTCCATGAT-1 0.719156742095947 -9.264817237854 NK cells -Sample_3_CATCAGAGTCCGAAGA-1 -1.26449108123779 -0.0703477635979652 CD8+ Tem -Sample_3_CATCCACGTCCCTTGT-1 -0.0893773585557938 5.16374063491821 CD8+ Tcm -Sample_3_CATCCACGTGCATCTA-1 0.225228324532509 6.56404638290405 CD4+ Tem -Sample_3_CATCCACTCTGAGGGA-1 2.76578879356384 -9.26353168487549 NK cells -Sample_3_CATCGAAAGGAATCGC-1 -1.002925157547 6.89066600799561 CD8+ Tcm -Sample_3_CATCGAACAGGCTGAA-1 -2.78314805030823 7.16065835952759 CD8+ T-cells -Sample_3_CATCGAACAGGGAGAG-1 3.08310484886169 6.80535650253296 CD8+ Tcm -Sample_3_CATCGGGCAAAGGTGC-1 0.0224177334457636 -11.1652936935425 NK cells -Sample_3_CATGACAAGTGAACGC-1 1.86130201816559 -4.66304636001587 CD8+ Tem -Sample_3_CATGACACACAGACTT-1 1.82601058483124 -10.3727798461914 NK cells -Sample_3_CATGACACACCGTTGG-1 3.36838746070862 3.73187804222107 CD8+ Tcm -Sample_3_CATGACACAGCTATTG-1 2.80301880836487 -9.77646827697754 NK cells -Sample_3_CATGACAGTCTCACCT-1 3.15170407295227 4.17508029937744 CD8+ Tem -Sample_3_CATGCCTCAATGAAAC-1 1.19016015529633 -9.58657073974609 NK cells -Sample_3_CATGGCGAGTGTGGCA-1 2.65015268325806 -7.357421875 NK cells -Sample_3_CATGGCGCACGAGGTA-1 2.24566435813904 -7.97040796279907 NK cells -Sample_3_CATGGCGGTACAGTGG-1 0.638587772846222 -8.83823490142822 NK cells -Sample_3_CATTATCAGGGTTCCC-1 2.30296969413757 -10.347710609436 NK cells -Sample_3_CATTATCCACAGCCCA-1 -3.81222486495972 8.67202568054199 CD8+ Tcm -Sample_3_CATTATCGTGGTCTCG-1 2.40660238265991 6.09558629989624 CD8+ Tem -Sample_3_CCAATCCGTCTCTTAT-1 -0.821308314800262 9.7714204788208 CD8+ T-cells -Sample_3_CCAATCCTCTCATTCA-1 -2.46389770507812 8.93087291717529 CD4+ T-cells -Sample_3_CCACCTATCTTGCCGT-1 0.685720324516296 -11.1230630874634 NK cells -Sample_3_CCACGGACAATCACAC-1 3.35874199867249 -9.98855495452881 NK cells -Sample_3_CCACGGATCAGGCAAG-1 2.3634786605835 -4.92572546005249 NK cells -Sample_3_CCACTACAGTGGACGT-1 3.96252465248108 5.62788152694702 CD8+ Tem -Sample_3_CCAGCGAGTTCGTTGA-1 4.59052658081055 7.05620956420898 CD8+ Tcm -Sample_3_CCATTCGCAAGCCATT-1 -2.55693817138672 8.77912425994873 CD8+ Tcm -Sample_3_CCCAATCTCAACACCA-1 3.81389498710632 6.97400760650635 CD8+ Tem -Sample_3_CCCTCCTAGTAGCCGA-1 2.84018516540527 -10.0352830886841 NK cells -Sample_3_CCCTCCTGTTGTACAC-1 -2.68330097198486 8.86049938201904 CD8+ T-cells -Sample_3_CCGGTAGAGTTACGGG-1 -2.09695053100586 8.95217418670654 CD8+ T-cells -Sample_3_CCGGTAGTCGCCTGAG-1 -0.367690682411194 -10.0292072296143 NK cells -Sample_3_CCGTACTCAGGGTATG-1 4.02957630157471 5.78465604782104 CD8+ Tem -Sample_3_CCGTACTTCTACTATC-1 2.31579160690308 -10.2220401763916 NK cells -Sample_3_CCGTTCAAGCCCGAAA-1 0.859101414680481 -8.84210968017578 NK cells -Sample_3_CCTAAAGAGCGATTCT-1 3.73600697517395 4.73077774047852 CD8+ Tem -Sample_3_CCTAAAGCAAGTAATG-1 3.32959794998169 6.24429893493652 CD8+ Tem -Sample_3_CCTACACAGCAAATCA-1 -9.63324928283691 -2.72319674491882 Monocytes -Sample_3_CCTACACTCTTTACGT-1 2.92660903930664 -10.9033441543579 NK cells -Sample_3_CCTACCACATCGGAAG-1 -0.96126800775528 0.313237071037292 CD8+ Tcm -Sample_3_CCTAGCTAGAGGTACC-1 -2.62778067588806 7.36778020858765 CD8+ T-cells -Sample_3_CCTATTATCAAACCAC-1 1.7908627986908 7.81265115737915 CD8+ Tcm -Sample_3_CCTCTGAGTAAGAGGA-1 -0.853547751903534 0.204179093241692 CD8+ Tem -Sample_3_CCTCTGAGTTATTCTC-1 3.39320635795593 6.30593395233154 CD8+ Tem -Sample_3_CCTCTGATCAGCCTAA-1 0.776785492897034 -8.35674667358398 NK cells -Sample_3_CCTTACGAGCACCGCT-1 2.23433780670166 -9.48871326446533 NK cells -Sample_3_CCTTACGGTCGCATCG-1 1.74165117740631 -6.97130298614502 NK cells -Sample_3_CCTTCCCCAAACCTAC-1 -0.571065306663513 9.2332124710083 CD8+ T-cells -Sample_3_CCTTCCCTCACTGGGC-1 -2.01559519767761 6.93144655227661 CD4+ T-cells -Sample_3_CCTTCGAAGCGTTTAC-1 3.59388542175293 -8.52132892608643 NK cells -Sample_3_CCTTCGACAAGAAGAG-1 3.77610898017883 6.01475095748901 CD8+ Tem -Sample_3_CGAACATAGTCAAGCG-1 1.85820877552032 -6.79544448852539 NK cells -Sample_3_CGAACATCATCAGTCA-1 4.08008289337158 3.94199824333191 CD8+ Tem -Sample_3_CGAATGTGTCATTAGC-1 1.28373539447784 8.49672985076904 CD4+ Tcm -Sample_3_CGACCTTAGCACCGCT-1 -0.218284904956818 5.34520196914673 CD8+ Tcm -Sample_3_CGACCTTAGTTGTAGA-1 -9.60087871551514 -2.69006514549255 Memory B-cells -Sample_3_CGACCTTTCAAGCCTA-1 -1.30314373970032 0.732021391391754 CD8+ Tem -Sample_3_CGACTTCTCATAGCAC-1 3.05998635292053 -8.93478679656982 NK cells -Sample_3_CGAGCACAGACGCAAC-1 1.58438503742218 -10.4225978851318 NK cells -Sample_3_CGAGCACAGCTCAACT-1 5.35456943511963 6.52657556533813 CD8+ Tem -Sample_3_CGAGCACAGGCGTACA-1 -2.93054699897766 7.7348747253418 CD8+ T-cells -Sample_3_CGAGCACTCTGCAAGT-1 -2.78346228599548 7.62291145324707 CD4+ T-cells -Sample_3_CGAGCCACATGCAACT-1 -2.52008771896362 6.63480949401855 CD4+ T-cells -Sample_3_CGAGCCATCGTGGTCG-1 0.33092001080513 3.89140391349792 CD8+ Tcm -Sample_3_CGATCGGAGACGCTTT-1 -0.988581717014313 1.12739515304565 NK cells -Sample_3_CGATCGGGTCCCTTGT-1 -0.713656604290009 1.0623391866684 CD8+ Tem -Sample_3_CGATGGCTCGGTCCGA-1 -0.558200538158417 9.59815406799316 CD8+ T-cells -Sample_3_CGATGTAAGGACATTA-1 2.87524271011353 5.50127363204956 CD8+ Tem -Sample_3_CGATTGAGTGGTACAG-1 2.30147433280945 -7.38990926742554 NK cells -Sample_3_CGCCAAGAGACCACGA-1 -1.56619584560394 0.278328686952591 CD8+ Tem -Sample_3_CGCCAAGGTGTCCTCT-1 2.79597401618958 -11.0225524902344 NK cells -Sample_3_CGCGGTAAGGCATGGT-1 3.82421803474426 -8.77615737915039 NK cells -Sample_3_CGCGGTAAGTATCGAA-1 1.30107069015503 -10.5036010742188 NK cells -Sample_3_CGCGGTACACAGGCCT-1 -0.528827369213104 -9.76459884643555 NK cells -Sample_3_CGCGGTATCAACGCTA-1 1.30671715736389 -8.08608150482178 NK cells -Sample_3_CGCGGTATCTCTGCTG-1 -0.376318126916885 4.47065544128418 CD8+ Tem -Sample_3_CGCGTTTTCCAGATCA-1 1.56143152713776 2.37268042564392 CD8+ Tem -Sample_3_CGCTATCCACTGTCGG-1 -2.53819298744202 7.20212411880493 CD4+ T-cells -Sample_3_CGCTATCGTGCAGACA-1 1.58960592746735 -10.3353519439697 NK cells -Sample_3_CGCTATCTCGCCATAA-1 0.65632688999176 -8.05765819549561 NK cells -Sample_3_CGCTATCTCTGTACGA-1 0.521823823451996 4.67654085159302 CD8+ Tcm -Sample_3_CGCTGGATCCTAGGGC-1 -0.405551493167877 7.01514196395874 CD8+ Tcm -Sample_3_CGCTTCACATTTCAGG-1 3.41797614097595 4.70816135406494 CD8+ Tem -Sample_3_CGGACACAGCAGCGTA-1 -1.64943540096283 9.73821830749512 CD8+ T-cells -Sample_3_CGGACACAGGTGCTTT-1 0.425260245800018 -8.56314086914062 NK cells -Sample_3_CGGACACTCCTCGCAT-1 -1.02939283847809 2.28370523452759 CD8+ Tem -Sample_3_CGGACGTTCAGGTTCA-1 3.18816208839417 5.31182241439819 CD8+ Tcm -Sample_3_CGGACTGTCTCTGAGA-1 -0.602764844894409 7.33610820770264 CD8+ Tcm -Sample_3_CGGAGCTCAAACTGTC-1 3.41487598419189 3.69396209716797 CD8+ Tem -Sample_3_CGGAGCTGTACCGTTA-1 4.22595834732056 6.79280614852905 CD8+ Tem -Sample_3_CGGAGTCTCAGGCCCA-1 0.326508522033691 -10.2638292312622 NK cells -Sample_3_CGGCTAGCAATCTGCA-1 2.56037259101868 -7.33620738983154 NK cells -Sample_3_CGTAGCGCAAACAACA-1 3.26266860961914 -7.18885374069214 NK cells -Sample_3_CGTAGCGTCAGCACAT-1 2.5395781993866 -8.31676197052002 NK cells -Sample_3_CGTAGCGTCCTAGAAC-1 1.98709738254547 -4.77577114105225 NK cells -Sample_3_CGTAGGCGTCTGATTG-1 -0.619279623031616 7.50405025482178 CD4+ Tcm -Sample_3_CGTCAGGCAAGCTGTT-1 0.0753060579299927 -9.3048095703125 NK cells -Sample_3_CGTCAGGTCAAGGTAA-1 -2.63144469261169 9.02789783477783 CD8+ T-cells -Sample_3_CGTCCATAGCTATGCT-1 2.21483564376831 -8.33411884307861 NK cells -Sample_3_CGTCCATCACCCTATC-1 3.28497958183289 4.63322687149048 CD8+ Tem -Sample_3_CGTCTACCATAGGATA-1 -1.21060514450073 8.77951812744141 CD4+ T-cells -Sample_3_CGTGAGCCATTCTTAC-1 2.07828450202942 7.81467628479004 CD8+ Tcm -Sample_3_CGTGAGCTCAATCTCT-1 -9.6484260559082 -2.73944091796875 naive B-cells -Sample_3_CGTTGGGTCAGAGACG-1 4.37944793701172 6.85676574707031 CD8+ Tem -Sample_3_CGTTGGGTCCTTGCCA-1 -1.06968486309052 5.78015422821045 CD8+ Tcm -Sample_3_CTAACTTAGCCCAGCT-1 3.89147472381592 5.43658208847046 CD8+ Tem -Sample_3_CTAAGACAGTGTCTCA-1 0.886093974113464 -10.2052412033081 NK cells -Sample_3_CTAAGACCAGATCGGA-1 2.13621091842651 8.08328437805176 CD8+ Tcm -Sample_3_CTAAGACGTCGGATCC-1 1.8533616065979 -4.64121246337891 NK cells -Sample_3_CTAAGACTCCACTGGG-1 1.71803915500641 -5.07418918609619 NK cells -Sample_3_CTAATGGCACAGACTT-1 -1.74541997909546 7.42806529998779 CD8+ T-cells -Sample_3_CTAATGGGTTCAGGCC-1 1.94342625141144 -7.20945644378662 NK cells -Sample_3_CTACACCCAACGCACC-1 2.32463836669922 -8.76958465576172 NK cells -Sample_3_CTACACCGTTCAGCGC-1 2.79750180244446 -9.97958660125732 NK cells -Sample_3_CTACACCTCGGTTCGG-1 2.58090949058533 4.74751424789429 CD8+ Tem -Sample_3_CTACATTCATGCCCGA-1 2.86111831665039 -8.59322357177734 NK cells -Sample_3_CTACATTGTTTGGCGC-1 3.55541372299194 -7.97768497467041 NK cells -Sample_3_CTACCCAAGGTGCACA-1 2.49249505996704 -5.08101177215576 NK cells -Sample_3_CTACCCACACGACTCG-1 2.29235577583313 -6.9712700843811 NK cells -Sample_3_CTACCCACAGCGATCC-1 -0.000262056826613843 -11.03053855896 NK cells -Sample_3_CTACCCATCCAAATGC-1 -0.569362163543701 -7.32235813140869 NK cells -Sample_3_CTACGTCAGAGCTGCA-1 -1.63184547424316 8.66928482055664 CD8+ T-cells -Sample_3_CTACGTCAGGCGATAC-1 3.45724582672119 6.82024717330933 CD4+ Tem -Sample_3_CTACGTCGTCTCAACA-1 1.98126339912415 -8.65328121185303 NK cells -Sample_3_CTACGTCTCGTCGTTC-1 1.53908729553223 -8.81343269348145 NK cells -Sample_3_CTAGAGTCAATCTACG-1 1.1817626953125 -9.61760711669922 NK cells -Sample_3_CTAGAGTCAGGAATCG-1 -0.525467693805695 0.754772186279297 CD8+ Tem -Sample_3_CTAGAGTTCAACTCTT-1 3.66063380241394 5.87924528121948 CD8+ Tem -Sample_3_CTAGTGACAGACGCTC-1 -1.25439083576202 6.63366413116455 CD8+ Tcm -Sample_3_CTAGTGACAGCGTAAG-1 -0.876407980918884 5.1278133392334 CD8+ Tcm -Sample_3_CTAGTGACATCTATGG-1 0.597473740577698 6.62446403503418 CD4+ Tem -Sample_3_CTAGTGATCCTTGACC-1 -1.79250776767731 9.16676998138428 CD8+ T-cells -Sample_3_CTCACACTCACATACG-1 2.78831887245178 5.32570505142212 CD8+ Tem -Sample_3_CTCAGAATCGGAAATA-1 -1.4486620426178 1.56519067287445 CD8+ Tcm -Sample_3_CTCATTAAGTCGTTTG-1 -0.745834171772003 8.1170825958252 CD8+ Tcm -Sample_3_CTCCTAGTCGGCGCTA-1 -1.25100517272949 0.948258221149445 CD8+ Tem -Sample_3_CTCGAAAAGAACAATC-1 0.675923109054565 5.43063116073608 CD8+ Tcm -Sample_3_CTCGAAAAGACGACGT-1 -0.96488356590271 3.43763613700867 CD8+ Tcm -Sample_3_CTCGAAACACAGCGTC-1 4.64035177230835 -9.0172643661499 NK cells -Sample_3_CTCGGGAGTTCGGCAC-1 2.55267524719238 6.29413318634033 CD4+ Tem -Sample_3_CTCGTCACACACTGCG-1 1.04794156551361 -9.85681438446045 NK cells -Sample_3_CTCGTCACACAGGAGT-1 0.54956579208374 -9.68562030792236 NK cells -Sample_3_CTCGTCAGTTCCACGG-1 4.49196481704712 4.40223598480225 CD8+ Tem -Sample_3_CTCTAATAGTCCGTAT-1 0.822036445140839 -10.6184358596802 NK cells -Sample_3_CTCTAATAGTGGGTTG-1 4.19224500656128 4.79536104202271 CD8+ Tem -Sample_3_CTCTACGAGGTGCACA-1 4.00816011428833 4.45665884017944 CD8+ Tem -Sample_3_CTCTACGTCGAACGGA-1 4.73598289489746 6.81779766082764 CD8+ Tem -Sample_3_CTCTGGTAGCGATATA-1 3.63283443450928 4.30411624908447 CD8+ Tem -Sample_3_CTCTGGTAGGGAACGG-1 -0.711273550987244 8.10572147369385 CD8+ Tcm -Sample_3_CTCTGGTAGGTCGGAT-1 3.17550611495972 6.43571043014526 CD8+ Tem -Sample_3_CTCTGGTCACTGCCAG-1 3.45649838447571 3.73452925682068 CD8+ Tem -Sample_3_CTGAAACGTGCCTGTG-1 0.361612498760223 -8.25915050506592 NK cells -Sample_3_CTGAAGTCACGAAGCA-1 -2.00648164749146 7.78593730926514 CD8+ T-cells -Sample_3_CTGAAGTTCCGCAAGC-1 2.18527150154114 -8.8297643661499 NK cells -Sample_3_CTGAAGTTCGCAAACT-1 -2.62209677696228 8.13439750671387 CD4+ T-cells -Sample_3_CTGATAGAGCGTCAAG-1 -0.260763376951218 9.59890747070312 CD4+ T-cells -Sample_3_CTGATAGTCGAATCCA-1 1.23334419727325 8.5150728225708 CD8+ Tem -Sample_3_CTGATCCTCATAACCG-1 5.11104297637939 6.06387662887573 CD8+ Tem -Sample_3_CTGATCCTCGCAAGCC-1 -2.69808650016785 8.3016357421875 CD8+ T-cells -Sample_3_CTGATCCTCTACTATC-1 -1.57070219516754 6.87515306472778 CD4+ T-cells -Sample_3_CTGCCTAAGAAGGTGA-1 0.679190337657928 -9.94143390655518 NK cells -Sample_3_CTGCCTAAGACAGAGA-1 0.915455222129822 2.87672543525696 CD8+ Tem -Sample_3_CTGCCTACAGACAGGT-1 4.14146041870117 -9.93666744232178 NK cells -Sample_3_CTGCTGTCAACGATCT-1 -0.000815199920907617 -7.89334583282471 NK cells -Sample_3_CTGGTCTCAACCGCCA-1 4.63996553421021 4.63255786895752 CD8+ Tem -Sample_3_CTGGTCTGTATCAGTC-1 -0.0864680558443069 -10.0939950942993 NK cells -Sample_3_CTGGTCTGTTGACGTT-1 1.64510440826416 7.94259977340698 CD8+ Tem -Sample_3_CTGGTCTGTTGTCGCG-1 -1.62071657180786 8.71461963653564 CD8+ T-cells -Sample_3_CTGTGCTCACATGGGA-1 -0.766901791095734 9.20287322998047 CD8+ T-cells -Sample_3_CTGTGCTCACGGCCAT-1 2.66919469833374 4.87982177734375 CD8+ Tcm -Sample_3_CTGTGCTTCAGCGATT-1 3.9865939617157 6.50607252120972 CD8+ Tem -Sample_3_CTTAACTGTGGACGAT-1 -1.12898600101471 8.20018005371094 CD4+ T-cells -Sample_3_CTTACCGAGCGTCAAG-1 2.59037041664124 6.77875947952271 CD8+ Tem -Sample_3_CTTAGGAAGCCAACAG-1 -1.72562718391418 8.09200954437256 CD8+ T-cells -Sample_3_CTTCTCTAGTATCGAA-1 3.01980757713318 5.15665864944458 CD8+ Tem -Sample_3_CTTCTCTAGTGCGATG-1 1.4551203250885 -10.7393026351929 NK cells -Sample_3_CTTTGCGTCGGGAGTA-1 3.50502991676331 7.63574504852295 CD8+ Tem -Sample_3_GAAACTCAGTACGTTC-1 0.00674171047285199 3.12543368339539 CD8+ Tem -Sample_3_GAAACTCGTCTGCCAG-1 1.45835280418396 1.71697854995728 NK cells -Sample_3_GAAATGAAGAAGGGTA-1 3.8746325969696 4.60850095748901 CD8+ Tcm -Sample_3_GAAATGAGTGTTAAGA-1 3.95319557189941 6.11653804779053 CD8+ Tem -Sample_3_GAACATCTCCTTGGTC-1 0.201914474368095 8.99405384063721 CD8+ Tcm -Sample_3_GAACCTACAACACCCG-1 -2.91090536117554 8.5191068649292 CD4+ T-cells -Sample_3_GAACGGAAGCAAATCA-1 2.62873125076294 5.96581315994263 CD8+ Tem -Sample_3_GAAGCAGGTTCAGGCC-1 2.45716977119446 -10.4699020385742 NK cells -Sample_3_GAAGCAGTCTGTGCAA-1 2.86909556388855 -10.628173828125 NK cells -Sample_3_GAATAAGAGCGATTCT-1 3.11325097084045 4.77268218994141 CD8+ Tem -Sample_3_GAATAAGAGTTGAGAT-1 0.281231075525284 -9.19375801086426 NK cells -Sample_3_GAATAAGGTACCGTTA-1 -1.33329880237579 9.55158996582031 CD8+ T-cells -Sample_3_GAATAAGTCCACTCCA-1 0.357625365257263 -9.79894638061523 NK cells -Sample_3_GACACGCCACGACGAA-1 1.6991970539093 6.34882926940918 CD4+ Tem -Sample_3_GACACGCGTAACGTTC-1 -0.400865107774734 4.29860305786133 CD8+ Tcm -Sample_3_GACAGAGAGGCGTACA-1 1.65135204792023 -6.3402624130249 NK cells -Sample_3_GACAGAGTCGAGAACG-1 -1.25732982158661 0.360542058944702 CD8+ Tem -Sample_3_GACCAATGTGAGCGAT-1 -0.0230451580137014 3.36191582679749 CD8+ Tem -Sample_3_GACCTGGCACTTCGAA-1 2.26678371429443 -9.71443367004395 NK cells -Sample_3_GACCTGGCAGTAAGAT-1 -0.733364462852478 -0.0744711831212044 NK cells -Sample_3_GACGGCTGTTGATTGC-1 0.0656752064824104 -10.1915435791016 NK cells -Sample_3_GACGGCTGTTTGGGCC-1 3.64528775215149 8.17752552032471 CD8+ Tcm -Sample_3_GACGGCTTCAACGCTA-1 3.58273720741272 4.83066415786743 CD8+ Tem -Sample_3_GACGTGCTCACCATAG-1 -1.2913783788681 0.387922555208206 CD8+ Tem -Sample_3_GACGTTAAGAATAGGG-1 1.29515051841736 -8.8024263381958 NK cells -Sample_3_GACGTTAAGTGTACGG-1 -1.19258511066437 7.63893699645996 CD8+ T-cells -Sample_3_GACTACAAGTACACCT-1 3.72931170463562 7.45560503005981 CD8+ Tem -Sample_3_GACTACATCGGAATCT-1 1.95430636405945 -9.1561393737793 NK cells -Sample_3_GACTGCGGTTCGGCAC-1 5.00520706176758 5.89709711074829 CD8+ Tem -Sample_3_GACTGCGTCAGTTGAC-1 -2.29703307151794 7.52107763290405 CD8+ T-cells -Sample_3_GACTGCGTCTGTACGA-1 -3.2152361869812 7.74477863311768 CD8+ T-cells -Sample_3_GAGCAGAAGTGCTGCC-1 -2.97077965736389 8.75049018859863 CD8+ Tcm -Sample_3_GAGCAGAGTCTTGATG-1 0.209139749407768 -8.44672966003418 NK cells -Sample_3_GAGGTGAGTCCTAGCG-1 -3.88871145248413 8.78944683074951 CD8+ T-cells -Sample_3_GAGGTGATCCCTAACC-1 4.42418956756592 -8.29409217834473 NK cells -Sample_3_GATCAGTAGAGTTGGC-1 -1.67353904247284 8.26211738586426 CD8+ T-cells -Sample_3_GATCAGTAGTAGCGGT-1 -2.82380127906799 7.92816162109375 CD8+ T-cells -Sample_3_GATCAGTCAAAGCGGT-1 -1.13361001014709 8.07742881774902 CD4+ T-cells -Sample_3_GATCAGTTCCGAGCCA-1 0.552249789237976 -9.32385063171387 NK cells -Sample_3_GATCAGTTCCTCGCAT-1 0.18181499838829 -8.91170883178711 NK cells -Sample_3_GATCGATGTTCCCGAG-1 -1.40146863460541 7.86016750335693 CD4+ T-cells -Sample_3_GATCGATTCCACGCAG-1 2.06132817268372 -9.73900318145752 NK cells -Sample_3_GATCGCGAGACCTTTG-1 3.70295929908752 4.33570718765259 CD8+ Tem -Sample_3_GATCGCGCAAGAAAGG-1 2.39136147499084 -5.1098484992981 NK cells -Sample_3_GATCGCGGTCAACTGT-1 4.37758731842041 -8.33300495147705 NK cells -Sample_3_GATCGCGTCAAGGTAA-1 0.403240233659744 -7.25218534469604 NK cells -Sample_3_GATCGTAAGGTACTCT-1 -2.07549667358398 8.38550853729248 CD4+ T-cells -Sample_3_GATGAAAAGACACTAA-1 -2.0687084197998 8.46674728393555 CD4+ T-cells -Sample_3_GATGAAATCGTCTGAA-1 0.628028690814972 -9.84457015991211 NK cells -Sample_3_GATGAGGTCCTAGGGC-1 2.82471179962158 -7.63779401779175 NK cells -Sample_3_GATGCTAAGTCCCACG-1 -2.67319893836975 7.82335233688354 CD4+ T-cells -Sample_3_GATGCTACAAAGGAAG-1 3.54630279541016 4.15844583511353 CD8+ Tem -Sample_3_GATGCTAGTAGAGGAA-1 3.61978054046631 5.46509218215942 CD8+ Tem -Sample_3_GATTCAGAGACAGGCT-1 0.541058480739594 2.35239124298096 CD8+ Tcm -Sample_3_GCAAACTCATCCCATC-1 0.180628418922424 1.79342031478882 CD8+ Tcm -Sample_3_GCAAACTGTGGACGAT-1 2.75032234191895 -7.48677253723145 NK cells -Sample_3_GCAATCAAGTAGGCCA-1 3.80446290969849 -9.58636665344238 NK cells -Sample_3_GCAATCAGTCTAGTCA-1 3.31701445579529 -8.01626777648926 NK cells -Sample_3_GCAATCATCACCCTCA-1 1.27079272270203 -8.80252838134766 NK cells -Sample_3_GCAGTTAGTTCAGCGC-1 -0.69513076543808 9.34218311309814 CD8+ T-cells -Sample_3_GCATACATCCTGCCAT-1 4.18068552017212 4.05074119567871 CD8+ Tem -Sample_3_GCATACATCTTTAGGG-1 2.54883313179016 -10.1527528762817 NK cells -Sample_3_GCATGCGGTCCCTTGT-1 0.589599907398224 5.54486227035522 CD4+ Tem -Sample_3_GCATGTAAGACGCACA-1 2.05238628387451 6.30721235275269 CD8+ Tem -Sample_3_GCATGTAAGAGTAATC-1 -1.73066627979279 1.21641004085541 CD8+ Tem -Sample_3_GCATGTACACCTCGTT-1 -0.856661796569824 -0.0631516799330711 CD8+ Tem -Sample_3_GCCTCTACAAACTGCT-1 1.82637798786163 2.88228917121887 CD8+ Tem -Sample_3_GCGACCACATGCCCGA-1 4.43061447143555 4.72671699523926 CD8+ Tem -Sample_3_GCGAGAAAGCTACCGC-1 -2.03265714645386 8.80957126617432 CD4+ T-cells -Sample_3_GCGAGAACAAGGCTCC-1 2.28752303123474 6.66260099411011 CD4+ Tem -Sample_3_GCGAGAACACCAGTTA-1 0.642812252044678 -8.64038753509521 NK cells -Sample_3_GCGCAACAGGGTGTGT-1 -0.842077255249023 4.96324491500854 CD8+ Tcm -Sample_3_GCGCAACCACCCAGTG-1 1.51763236522675 -8.33842754364014 NK cells -Sample_3_GCGCAACTCCTATGTT-1 2.06628155708313 7.48973083496094 CD8+ Tem -Sample_3_GCGCAACTCTTGCATT-1 0.481996268033981 -7.73344039916992 NK cells -Sample_3_GCGCAGTTCCGTCATC-1 -0.381986886262894 -0.131981939077377 CD8+ Tem -Sample_3_GCGCCAACACCGTTGG-1 -3.88529419898987 8.97266101837158 CD4+ T-cells -Sample_3_GCGCGATTCCGTAGTA-1 2.69661617279053 -8.66417598724365 NK cells -Sample_3_GCGGGTTGTGCGCTTG-1 -1.11262214183807 5.31766939163208 CD8+ Tcm -Sample_3_GCTCCTAGTAGAAGGA-1 -0.252534300088882 0.621051430702209 CD8+ Tem -Sample_3_GCTCTGTCAAGTCTAC-1 -0.126354888081551 -9.7888126373291 NK cells -Sample_3_GCTGCAGAGTGGGATC-1 3.86400008201599 7.3141622543335 CD8+ Tem -Sample_3_GCTGCAGCACCACCAG-1 1.1487762928009 -10.9851970672607 NK cells -Sample_3_GCTGCAGGTCGATTGT-1 0.697325587272644 -10.0958766937256 NK cells -Sample_3_GCTGCAGTCCTTTACA-1 2.13161373138428 2.47667670249939 CD8+ Tcm -Sample_3_GCTGCAGTCGGAGGTA-1 3.9808566570282 -8.55561351776123 NK cells -Sample_3_GCTGCGAAGGCTACGA-1 -0.458487302064896 -0.133442103862762 CD8+ Tem -Sample_3_GCTGCGAGTGTCAATC-1 -0.920918345451355 9.20080280303955 CD4+ T-cells -Sample_3_GCTGCGATCTGATACG-1 0.949126243591309 -8.46565246582031 NK cells -Sample_3_GCTGGGTCATATGCTG-1 -0.39779606461525 -10.2490644454956 NK cells -Sample_3_GCTTCCACACATCCGG-1 4.84063911437988 4.49413251876831 CD8+ Tem -Sample_3_GCTTCCACATCTATGG-1 -0.670076906681061 -9.42746543884277 NK cells -Sample_3_GCTTCCATCTCTGTCG-1 0.465932548046112 4.29997301101685 CD8+ Tcm -Sample_3_GCTTGAACAGTATAAG-1 4.10181093215942 6.11099433898926 CD8+ Tem -Sample_3_GCTTGAATCTAACCGA-1 0.00235395273193717 -8.32886695861816 NK cells -Sample_3_GGAAAGCTCCTTGCCA-1 1.84809458255768 -8.15682697296143 NK cells -Sample_3_GGAACTTCAGATGGCA-1 4.02384996414185 4.82018566131592 CD4+ Tem -Sample_3_GGAATAACAAATTGCC-1 4.19135332107544 -8.32954788208008 NK cells -Sample_3_GGAATAACAGTTCATG-1 -2.55092263221741 8.75176525115967 CD4+ T-cells -Sample_3_GGACATTCACTTCGAA-1 2.78548836708069 -9.63747501373291 NK cells -Sample_3_GGAGCAACAAGCGCTC-1 2.74655556678772 6.07033061981201 CD8+ Tem -Sample_3_GGAGCAACATCTCCCA-1 0.0567693822085857 6.02639150619507 CD4+ Tem -Sample_3_GGAGCAATCGCATGGC-1 -0.478161692619324 -7.05643606185913 NK cells -Sample_3_GGATGTTAGTCCGTAT-1 -2.82938146591187 7.10598945617676 CD4+ T-cells -Sample_3_GGATTACCATTGAGCT-1 -0.0655752420425415 -9.62844753265381 NK cells -Sample_3_GGATTACTCACCAGGC-1 -0.0680253654718399 5.07917022705078 CD8+ Tem -Sample_3_GGCAATTAGAGGTAGA-1 3.77203989028931 4.17069864273071 CD8+ Tem -Sample_3_GGCAATTAGTGTTGAA-1 -1.63958287239075 1.35030055046082 CD8+ Tem -Sample_3_GGCAATTCAGATTGCT-1 -0.387528359889984 3.73221969604492 CD8+ Tem -Sample_3_GGCAATTGTCAAACTC-1 1.11350476741791 -8.63481616973877 NK cells -Sample_3_GGCAATTGTGATAAGT-1 4.70686292648315 4.24559020996094 CD8+ Tem -Sample_3_GGCCGATGTTTCCACC-1 2.57983016967773 -10.46568775177 NK cells -Sample_3_GGCGACTCATCCTAGA-1 0.759951114654541 -8.86960411071777 NK cells -Sample_3_GGCGACTCATCCTTGC-1 -0.328466802835464 6.55317783355713 CD4+ Tcm -Sample_3_GGCGTGTCACATGACT-1 1.31636464595795 -8.78923988342285 NK cells -Sample_3_GGGAATGAGATGGCGT-1 -1.87136578559875 8.77960395812988 CD8+ T-cells -Sample_3_GGGAATGGTCATCCCT-1 -1.29436635971069 7.82770586013794 CD8+ T-cells -Sample_3_GGGACCTTCTTGGGTA-1 -0.318974852561951 0.0206285491585732 CD8+ Tem -Sample_3_GGGAGATAGTGTTTGC-1 -1.2744619846344 8.74278545379639 CD8+ T-cells -Sample_3_GGGAGATCAGATGGCA-1 0.56903612613678 4.57202053070068 CD8+ Tcm -Sample_3_GGGAGATCATACCATG-1 -1.9172614812851 7.91445732116699 CD8+ T-cells -Sample_3_GGGAGATGTCAAACTC-1 4.36907529830933 -9.78216743469238 NK cells -Sample_3_GGGATGAGTCGTCTTC-1 3.44304156303406 6.28863000869751 CD8+ Tem -Sample_3_GGGATGATCAGTTCGA-1 1.42094206809998 6.23867607116699 CD4+ Tem -Sample_3_GGGCACTAGCAGGTCA-1 3.63338804244995 -8.57704162597656 NK cells -Sample_3_GGGCACTCAAAGCAAT-1 -0.910828530788422 3.93954157829285 CD8+ Tcm -Sample_3_GGGCACTTCCTAGGGC-1 -2.32239317893982 7.58723592758179 CD4+ T-cells -Sample_3_GGGCACTTCTATCGCC-1 0.688692450523376 -9.35353565216064 NK cells -Sample_3_GGGCATCCACGGCGTT-1 0.406541228294373 -9.07730960845947 NK cells -Sample_3_GGGCATCCATCACAAC-1 0.132013455033302 3.39646291732788 CD8+ Tem -Sample_3_GGGCATCGTTCCTCCA-1 5.1486930847168 5.77917337417603 CD8+ Tem -Sample_3_GGGCATCTCCGCATCT-1 2.62838673591614 5.33065986633301 CD8+ Tem -Sample_3_GGTATTGGTGAGGGTT-1 -1.80872070789337 6.55647802352905 CD8+ T-cells -Sample_3_GGTATTGGTTCCCGAG-1 0.349281162023544 3.89721393585205 CD8+ Tem -Sample_3_GGTGAAGAGAGGTAGA-1 1.13299989700317 2.07345414161682 CD8+ Tem -Sample_3_GGTGAAGTCCATGAGT-1 3.69966697692871 6.26280736923218 CD8+ Tem -Sample_3_GGTGAAGTCCTCATTA-1 2.53847885131836 -8.13879585266113 NK cells -Sample_3_GGTGCGTAGAGTACAT-1 1.36625242233276 -10.670449256897 NK cells -Sample_3_GGTGCGTAGTACATGA-1 0.0283868946135044 5.63370847702026 CD8+ Tcm -Sample_3_GGTGTTAGTAGTAGTA-1 -0.904113829135895 0.473134130239487 CD8+ Tem -Sample_3_GTAACGTAGGACAGCT-1 2.90893697738647 5.25704050064087 CD8+ Tem -Sample_3_GTAACGTTCACTTACT-1 2.33033537864685 4.73113632202148 CD8+ Tem -Sample_3_GTAACTGCATCGGTTA-1 3.4090416431427 -8.2517557144165 NK cells -Sample_3_GTAACTGTCCGAACGC-1 -0.558022201061249 -7.31613445281982 NK cells -Sample_3_GTACGTAAGACACTAA-1 -0.773694753646851 -6.8994402885437 CD8+ Tem -Sample_3_GTACGTACATGCTGGC-1 3.39331603050232 4.64169025421143 CD8+ Tem -Sample_3_GTACGTATCCTGTAGA-1 -2.90796804428101 8.29296398162842 CD4+ T-cells -Sample_3_GTACTTTGTGTCAATC-1 -0.0505617186427116 8.16171169281006 CD8+ Tcm -Sample_3_GTAGGCCAGCGCTCCA-1 2.79841494560242 7.69221496582031 CD8+ Tem -Sample_3_GTAGGCCGTAACGTTC-1 0.127387374639511 -9.19612693786621 NK cells -Sample_3_GTATCTTTCCTATGTT-1 2.77060389518738 6.44494438171387 CD8+ Tem -Sample_3_GTATTCTCAGCTGCTG-1 3.21144890785217 6.6800856590271 CD8+ Tem -Sample_3_GTATTCTGTCCCTACT-1 0.81026691198349 2.71471095085144 CD8+ Tem -Sample_3_GTATTCTTCCAATGGT-1 3.17291951179504 4.8632173538208 CD8+ Tcm -Sample_3_GTCACAAGTCCGAAGA-1 -1.27039289474487 8.21162891387939 CD4+ T-cells -Sample_3_GTCACAAGTTCCACAA-1 -0.0641278252005577 -8.43955135345459 NK cells -Sample_3_GTCACGGCACCGGAAA-1 -2.224205493927 7.83433103561401 CD4+ T-cells -Sample_3_GTCACGGGTTGCCTCT-1 4.90020656585693 5.54644870758057 CD8+ Tem -Sample_3_GTCACGGTCAAGGCTT-1 -1.74379503726959 1.29663133621216 CD8+ Tem -Sample_3_GTCATTTAGCTTTGGT-1 1.10458421707153 -10.1377220153809 NK cells -Sample_3_GTCATTTTCAGTTTGG-1 3.36592602729797 5.80474376678467 CD8+ Tem -Sample_3_GTCCTCAAGTATCGAA-1 -1.99205660820007 8.98696327209473 CD8+ T-cells -Sample_3_GTCGGGTGTCCTCTTG-1 3.5161714553833 7.54802227020264 CD8+ Tem -Sample_3_GTCGTAATCCATGAGT-1 0.625626444816589 3.01829361915588 CD8+ Tcm -Sample_3_GTCTCGTAGTTCGCAT-1 -2.75748229026794 8.24715232849121 CD8+ T-cells -Sample_3_GTCTTCGGTATAAACG-1 3.51083850860596 3.53115463256836 CD8+ Tem -Sample_3_GTGAAGGAGTACGACG-1 1.35986578464508 -9.38763236999512 NK cells -Sample_3_GTGAAGGCAATAACGA-1 1.09948456287384 -8.39147281646729 NK cells -Sample_3_GTGAAGGCATGTTGAC-1 3.60473895072937 6.62968587875366 CD8+ Tem -Sample_3_GTGAAGGGTCCTCTTG-1 0.781003057956696 4.58572578430176 CD8+ Tcm -Sample_3_GTGAAGGTCATGTAGC-1 1.98217463493347 -4.8615460395813 CD8+ Tem -Sample_3_GTGCAGCAGGCTACGA-1 4.86655521392822 4.53052425384521 CD8+ Tcm -Sample_3_GTGCAGCTCAAAGACA-1 -2.5434741973877 7.3812894821167 CD4+ T-cells -Sample_3_GTGCATACATCCGGGT-1 2.59546852111816 -9.90190029144287 NK cells -Sample_3_GTGCATATCACCGTAA-1 1.46991276741028 -7.05592679977417 NK cells -Sample_3_GTGCATATCATTATCC-1 2.26824808120728 -9.36928653717041 NK cells -Sample_3_GTGCGGTAGAGGTACC-1 3.36321353912354 7.0063362121582 CD8+ Tem -Sample_3_GTGCGGTCAAGCCGTC-1 0.160051852464676 -8.76552486419678 NK cells -Sample_3_GTGCGGTCACATTCGA-1 -2.63863682746887 7.97915983200073 CD8+ T-cells -Sample_3_GTGCTTCAGCCAACAG-1 2.11035990715027 -7.5344877243042 NK cells -Sample_3_GTGGGTCTCAAGAAGT-1 0.0581071972846985 0.667853593826294 CD8+ Tem -Sample_3_GTGTGCGTCGGTGTCG-1 0.117205157876015 8.51464080810547 CD8+ Tem -Sample_3_GTGTTAGCATCCTAGA-1 2.74114799499512 -9.48130226135254 NK cells -Sample_3_GTGTTAGGTAGATTAG-1 3.29543018341064 5.5779390335083 CD8+ Tem -Sample_3_GTTACAGCATTAGGCT-1 -2.18893027305603 7.94757509231567 CD4+ T-cells -Sample_3_GTTCATTCATGCATGT-1 3.22557020187378 -10.689902305603 NK cells -Sample_3_GTTCATTTCCAAACAC-1 -0.983115494251251 7.75830030441284 CD8+ T-cells -Sample_3_GTTCTCGTCTAGCACA-1 1.73445081710815 -9.8812255859375 NK cells -Sample_3_GTTTCTAAGATTACCC-1 -0.707846939563751 4.21703481674194 CD8+ Tem -Sample_3_GTTTCTAAGGTGCAAC-1 -0.993634819984436 6.3808069229126 CD8+ Tcm -Sample_3_GTTTCTACATCACGAT-1 -1.75316417217255 8.79950523376465 CD8+ T-cells -Sample_3_TAAACCGAGTTCGATC-1 4.19515609741211 -8.48150825500488 NK cells -Sample_3_TAAACCGGTCTGCGGT-1 2.40385437011719 6.69005441665649 CD8+ Tem -Sample_3_TAAACCGGTTCGCGAC-1 4.24549341201782 5.60409355163574 CD8+ T-cells -Sample_3_TAAGAGAGTACTTAGC-1 -0.21730250120163 -9.07652282714844 NK cells -Sample_3_TAAGAGATCCGAAGAG-1 -2.93015766143799 8.65698146820068 CD8+ T-cells -Sample_3_TAAGCGTAGGACTGGT-1 0.966175556182861 -7.38570308685303 NK cells -Sample_3_TAAGTGCCAGACAAAT-1 3.13663983345032 -10.8593273162842 NK cells -Sample_3_TAAGTGCTCGCCATAA-1 1.3595552444458 -9.09127044677734 NK cells -Sample_3_TACACGAGTCACTTCC-1 2.86383318901062 -10.5342693328857 NK cells -Sample_3_TACACGAGTTTCGCTC-1 -3.72021913528442 8.85080432891846 CD8+ T-cells -Sample_3_TACACGATCCCAGGTG-1 0.373452574014664 2.07866311073303 CD8+ Tem -Sample_3_TACCTATGTCCAACTA-1 2.00653505325317 7.82164669036865 CD8+ Tem -Sample_3_TACCTTAAGCTAACAA-1 1.99293041229248 -10.1733169555664 NK cells -Sample_3_TACCTTATCTGGCGTG-1 3.46196556091309 -7.45022010803223 NK cells -Sample_3_TACGGGCCATCCTAGA-1 -0.768954575061798 6.30992269515991 CD8+ Tcm -Sample_3_TACGGGCCATTACCTT-1 -0.871034741401672 3.27377104759216 CD8+ Tcm -Sample_3_TACGGTAAGGATGGAA-1 2.63725399971008 6.4339599609375 CD8+ Tem -Sample_3_TACTCATTCGGCCGAT-1 -1.05317187309265 1.84786581993103 CD8+ Tem -Sample_3_TACTCGCCAACTGCGC-1 0.110215649008751 -9.40203475952148 NK cells -Sample_3_TACTCGCTCCGAATGT-1 -1.69399762153625 8.95248031616211 CD8+ T-cells -Sample_3_TACTCGCTCGTAGGTT-1 -1.24293482303619 8.54566478729248 CD8+ T-cells -Sample_3_TACTTACTCCGTTGTC-1 -0.438259273767471 -10.4138345718384 NK cells -Sample_3_TACTTGTGTACTTAGC-1 0.520776748657227 -8.68942260742188 NK cells -Sample_3_TAGCCGGCAATGTTGC-1 2.31410026550293 5.5320086479187 CD8+ Tem -Sample_3_TAGCCGGTCGAGAACG-1 0.662798345088959 2.70067954063416 CD8+ Tcm -Sample_3_TAGGCATCAGTCCTTC-1 -0.187973454594612 4.97060775756836 Tregs -Sample_3_TAGGCATGTTGTACAC-1 -1.81556928157806 9.47457790374756 CD8+ T-cells -Sample_3_TAGTGGTCAAGGGTCA-1 1.91261804103851 -8.98585319519043 NK cells -Sample_3_TAGTGGTTCAATACCG-1 -2.03491187095642 7.88862085342407 CD4+ T-cells -Sample_3_TAGTGGTTCCTAGTGA-1 4.47494697570801 5.72257041931152 CD8+ Tcm -Sample_3_TAGTTGGAGTAGGCCA-1 1.65117359161377 -9.54350090026855 NK cells -Sample_3_TATCAGGCAACTGCTA-1 3.18426465988159 -7.92161893844604 NK cells -Sample_3_TATCAGGGTCGGATCC-1 2.58460474014282 6.08617830276489 CD8+ Tcm -Sample_3_TATCTCAAGACTTGAA-1 2.99773955345154 -8.38348960876465 NK cells -Sample_3_TATCTCAAGGGAGTAA-1 1.30431699752808 -9.84789180755615 NK cells -Sample_3_TATCTCAAGGTCATCT-1 0.00162555405404419 4.18080520629883 CD8+ Tcm -Sample_3_TATCTCAGTCTACCTC-1 0.412862569093704 3.92703747749329 CD8+ Tcm -Sample_3_TATCTCATCATGTAGC-1 4.6569652557373 5.60174798965454 CD8+ Tcm -Sample_3_TATGCCCCAAGCTGTT-1 4.76230573654175 5.88475561141968 CD8+ Tcm -Sample_3_TATGCCCCAATCACAC-1 -1.6865359544754 8.64875030517578 CD8+ T-cells -Sample_3_TATTACCCAAACCCAT-1 -1.33362746238708 9.75904941558838 CD8+ Tcm -Sample_3_TATTACCCACGAAAGC-1 -3.18336892127991 7.87725448608398 CD8+ T-cells -Sample_3_TATTACCGTGATGTGG-1 -0.731655657291412 -10.5908823013306 NK cells -Sample_3_TCAACGACAATCTACG-1 0.96381026506424 -9.23630619049072 NK cells -Sample_3_TCAACGATCCTTTCGG-1 -0.0893415212631226 1.7358912229538 CD8+ Tcm -Sample_3_TCAATCTGTGCAGACA-1 -0.648929834365845 0.864116430282593 CD8+ Tem -Sample_3_TCAATCTGTGCGATAG-1 3.22662854194641 6.97017288208008 CD8+ Tem -Sample_3_TCACAAGCAATCGAAA-1 2.98343801498413 -9.82026958465576 NK cells -Sample_3_TCACAAGGTCTCATCC-1 -0.995560109615326 -9.28674602508545 NK cells -Sample_3_TCACGAACATACTACG-1 -0.879132211208344 0.432495832443237 CD8+ Tcm -Sample_3_TCACGAAGTAGAAAGG-1 -0.0635165721178055 0.974073350429535 CD8+ Tem -Sample_3_TCACGAATCATCGATG-1 3.85435628890991 5.69896459579468 CD4+ Tem -Sample_3_TCACGAATCGTCACGG-1 3.06755256652832 5.27196311950684 CD8+ Tem -Sample_3_TCAGATGAGTTGAGTA-1 -0.755187332630157 6.62595796585083 CD8+ Tcm -Sample_3_TCAGCAAGTGCTTCTC-1 4.56104469299316 6.38318777084351 CD8+ Tem -Sample_3_TCAGCTCAGACAGGCT-1 0.518759548664093 3.02213215827942 CD8+ Tem -Sample_3_TCAGCTCGTCGAGTTT-1 3.15685081481934 7.71561717987061 CD8+ Tem -Sample_3_TCAGGATTCATTTGGG-1 3.59086990356445 5.60978174209595 CD8+ Tem -Sample_3_TCAGGTAAGTCCGGTC-1 -0.9634969830513 3.4638819694519 CD8+ Tem -Sample_3_TCAGGTACAGGATTGG-1 -1.36623060703278 1.05477225780487 CD8+ Tem -Sample_3_TCAGGTAGTCCGTCAG-1 1.66215515136719 -6.90166330337524 NK cells -Sample_3_TCAGGTATCGAGGTAG-1 2.65110397338867 -9.54476833343506 NK cells -Sample_3_TCAGGTATCGCCTGTT-1 0.379516869783401 5.8350133895874 CD8+ Tcm -Sample_3_TCATTACCAGTATAAG-1 3.57474398612976 -9.45399188995361 NK cells -Sample_3_TCATTTGAGGCAATTA-1 3.33750891685486 6.62061548233032 CD8+ Tem -Sample_3_TCATTTGAGTGGTAGC-1 -1.83520519733429 0.636157929897308 CD8+ Tem -Sample_3_TCATTTGGTGTTGAGG-1 -0.341910749673843 0.331509947776794 CD8+ Tem -Sample_3_TCCACACAGTACACCT-1 -2.2033212184906 7.57005167007446 CD8+ Tcm -Sample_3_TCCACACCAGTTTACG-1 1.27929973602295 -10.4656229019165 NK cells -Sample_3_TCCCGATAGACAGGCT-1 -2.31060814857483 7.84246826171875 CD8+ T-cells -Sample_3_TCCCGATCAAGTTCTG-1 0.903681933879852 -8.0204610824585 NK cells -Sample_3_TCCCGATTCGTAGATC-1 -0.462989747524261 6.69073104858398 CD8+ Tcm -Sample_3_TCGAGGCGTAGAAGGA-1 -1.69976603984833 5.60164642333984 CD8+ Tcm -Sample_3_TCGAGGCTCCTCAACC-1 3.18223309516907 4.32811784744263 CD8+ Tem -Sample_3_TCGAGGCTCGTTGACA-1 2.56907629966736 -9.21863174438477 NK cells -Sample_3_TCGCGAGGTCAGTGGA-1 0.724505364894867 2.83857774734497 CD8+ Tem -Sample_3_TCGCGAGTCGACAGCC-1 -0.561817646026611 -7.3774471282959 NK cells -Sample_3_TCGGGACCAGACGTAG-1 2.38499140739441 7.31801319122314 CD8+ Tem -Sample_3_TCGGGACGTGATAAGT-1 0.445334523916245 -8.74581909179688 NK cells -Sample_3_TCGGGACTCACCCGAG-1 -1.90442276000977 7.23723936080933 CD8+ T-cells -Sample_3_TCGGGACTCCGAAGAG-1 1.22314023971558 -9.56136798858643 NK cells -Sample_3_TCGGTAACACAGGCCT-1 2.94108319282532 5.41960763931274 CD8+ Tem -Sample_3_TCGGTAACATTAACCG-1 2.74222040176392 -7.20949935913086 NK cells -Sample_3_TCGTACCAGATCTGCT-1 2.75614905357361 4.98100423812866 CD8+ Tem -Sample_3_TCGTACCGTTTGACAC-1 1.10085964202881 -8.25449848175049 NK cells -Sample_3_TCGTACCTCAAACCGT-1 2.75823783874512 -6.84388065338135 NK cells -Sample_3_TCGTACCTCCCGACTT-1 3.8091037273407 4.46155023574829 CD8+ Tcm -Sample_3_TCGTAGACATCTATGG-1 2.29687309265137 -9.14501285552979 NK cells -Sample_3_TCTATTGAGGCGACAT-1 3.25663232803345 7.28457736968994 CD8+ Tcm -Sample_3_TCTATTGGTAGCTCCG-1 1.99517071247101 8.41094207763672 CD8+ Tem -Sample_3_TCTCATAAGACTTTCG-1 0.0501250140368938 0.454271763563156 CD8+ Tem -Sample_3_TCTCTAATCCGTAGTA-1 0.356933772563934 3.28167605400085 CD8+ Tem -Sample_3_TCTGAGAAGAGTGAGA-1 0.17549355328083 -10.5956354141235 NK cells -Sample_3_TCTGAGACACCACGTG-1 -0.276751011610031 0.564424753189087 NK cells -Sample_3_TCTGAGACAGATCTGT-1 2.14804077148438 4.55136728286743 CD8+ Tem -Sample_3_TCTGGAAAGTACGTTC-1 2.65169811248779 7.81635999679565 CD8+ Tem -Sample_3_TCTTCGGAGGGATGGG-1 3.4784083366394 7.25637197494507 CD8+ Tcm -Sample_3_TCTTTCCAGACTGGGT-1 3.0172975063324 -9.29521560668945 NK cells -Sample_3_TCTTTCCGTGCTTCTC-1 4.36383152008057 5.15998411178589 CD8+ Tem -Sample_3_TGAAAGATCCCTAATT-1 0.376287162303925 -11.5120801925659 NK cells -Sample_3_TGAAAGATCGTTGCCT-1 -1.81525540351868 0.675574064254761 CD8+ Tem -Sample_3_TGACAACTCCTACAGA-1 4.18995571136475 -9.6337251663208 NK cells -Sample_3_TGACTTTCAATGGATA-1 0.112384893000126 2.36812949180603 CD8+ Tcm -Sample_3_TGACTTTTCTAGCACA-1 4.13757562637329 5.68711519241333 CD8+ Tem -Sample_3_TGAGAGGAGCAATCTC-1 3.17253613471985 -10.2919797897339 NK cells -Sample_3_TGAGCATGTCAAAGCG-1 -0.163914382457733 3.77138733863831 CD8+ Tcm -Sample_3_TGAGCCGGTGCCTTGG-1 0.314369767904282 -11.2171726226807 NK cells -Sample_3_TGAGGGAAGCATGGCA-1 -2.32948517799377 6.79531574249268 CD4+ T-cells -Sample_3_TGAGGGACATGGGAAC-1 -0.254692405462265 4.15532112121582 CD8+ Tem -Sample_3_TGAGGGATCACATGCA-1 3.93977022171021 4.94442081451416 CD8+ Tem -Sample_3_TGATTTCTCCTTCAAT-1 1.40044665336609 -8.283203125 NK cells -Sample_3_TGCACCTAGGGCACTA-1 1.98788702487946 -9.65441703796387 NK cells -Sample_3_TGCCCATCAGTAAGCG-1 2.67769265174866 4.50941944122314 CD8+ Tem -Sample_3_TGCCCTAGTTCAACCA-1 0.437285214662552 -8.26924991607666 NK cells -Sample_3_TGCCCTATCAGGCAAG-1 -0.565350949764252 7.79767227172852 CD8+ Tcm -Sample_3_TGCGGGTAGACCTAGG-1 3.31296491622925 6.15546321868896 CD8+ Tem -Sample_3_TGCGGGTGTTATGTGC-1 1.83045446872711 -4.66947937011719 NK cells -Sample_3_TGCGTGGGTAATTGGA-1 2.61938261985779 -7.94413328170776 NK cells -Sample_3_TGCTACCAGCTCCCAG-1 -0.516371488571167 9.60774421691895 CD8+ T-cells -Sample_3_TGCTGCTGTCCAACTA-1 -0.121767766773701 5.52820634841919 CD8+ Tcm -Sample_3_TGGACGCAGGAGCGAG-1 -1.50911664962769 5.71784019470215 CD8+ Tcm -Sample_3_TGGACGCGTAACGCGA-1 3.73525214195251 7.26278209686279 CD4+ Tem -Sample_3_TGGCGCAAGTACGCGA-1 3.43888449668884 6.22272634506226 CD8+ Tem -Sample_3_TGGCGCAGTTCCCGAG-1 0.446681261062622 -8.0354118347168 NK cells -Sample_3_TGGCGCATCCTACAGA-1 3.70231795310974 3.67331123352051 CD8+ Tem -Sample_3_TGGGAAGAGGAACTGC-1 -2.96594023704529 8.59525012969971 CD8+ T-cells -Sample_3_TGGGAAGTCTATCCTA-1 -1.02171659469604 -8.13236618041992 NK cells -Sample_3_TGGGCGTCAGTTAACC-1 2.43946218490601 7.96482610702515 CD8+ Tem -Sample_3_TGGTTAGAGCGCCTTG-1 1.85190546512604 -8.87875270843506 NK cells -Sample_3_TGGTTAGAGGAATTAC-1 0.200113266706467 3.16261410713196 CD8+ Tem -Sample_3_TGGTTAGGTCAATACC-1 1.9065580368042 -7.11719608306885 NK cells -Sample_3_TGGTTCCTCCACTCCA-1 -0.0732661783695221 5.54109144210815 CD8+ Tcm -Sample_3_TGTATTCAGATGTCGG-1 -0.168151706457138 4.74865818023682 CD8+ Tcm -Sample_3_TGTATTCCAGCGTCCA-1 2.03673529624939 -9.02143001556396 NK cells -Sample_3_TGTATTCTCTGATACG-1 2.76250791549683 -9.44964122772217 NK cells -Sample_3_TGTCCCAAGAGACTAT-1 4.28805255889893 -9.7507963180542 NK cells -Sample_3_TGTCCCATCTTGTACT-1 1.97649824619293 -9.85092067718506 NK cells -Sample_3_TGTGGTACATGCAACT-1 -2.53194093704224 8.87195873260498 CD4+ T-cells -Sample_3_TGTGGTAGTGCATCTA-1 -1.42099416255951 0.324003636837006 CD8+ Tem -Sample_3_TGTGGTATCCCGACTT-1 4.03301286697388 4.91901445388794 CD8+ Tem -Sample_3_TGTGTTTAGGCCCTCA-1 4.42280340194702 -8.73600959777832 NK cells -Sample_3_TGTGTTTGTTGTGGAG-1 0.93818861246109 2.68866872787476 CD8+ Tem -Sample_3_TGTGTTTTCATGCTCC-1 4.07108545303345 6.76891851425171 CD8+ Tem -Sample_3_TGTTCCGAGCCACCTG-1 1.9491468667984 -7.54811573028564 NK cells -Sample_3_TGTTCCGCAGTGACAG-1 2.15312361717224 -4.79551839828491 NK cells -Sample_3_TTAGGACAGGGTCGAT-1 -1.71604371070862 1.0514919757843 CD8+ Tem -Sample_3_TTAGGACGTGGTACAG-1 3.12388157844543 5.93119430541992 CD8+ Tem -Sample_3_TTAGGCACATAAAGGT-1 3.84238266944885 7.77144432067871 CD8+ Tcm -Sample_3_TTAGTTCTCACTTACT-1 2.9904887676239 4.45491743087769 CD8+ Tem -Sample_3_TTAGTTCTCTTGAGAC-1 4.01016855239868 5.30387735366821 CD8+ Tcm -Sample_3_TTATGCTTCACGAAGG-1 -0.769375145435333 8.93002605438232 CD8+ T-cells -Sample_3_TTCGGTCAGCCAGAAC-1 2.72353959083557 -11.0902070999146 NK cells -Sample_3_TTCGGTCAGTTCCACA-1 -9.77152442932129 -2.86742925643921 naive B-cells -Sample_3_TTCTACACATTACCTT-1 -0.885757148265839 0.874507784843445 CD8+ Tem -Sample_3_TTCTACATCGCAAGCC-1 3.67795872688293 4.57094430923462 CD8+ Tem -Sample_3_TTCTCAACACACTGCG-1 -0.397082179784775 4.93495035171509 CD8+ Tem -Sample_3_TTCTCCTGTTCCCGAG-1 2.92777490615845 -10.8755970001221 NK cells -Sample_3_TTCTCCTTCCGAATGT-1 3.29207158088684 5.65155792236328 CD8+ Tem -Sample_3_TTCTTAGGTTTAGGAA-1 1.80687046051025 2.29979825019836 CD8+ Tem -Sample_3_TTGAACGAGCCATCGC-1 1.52759397029877 -8.49742794036865 NK cells -Sample_3_TTGAACGGTAGCTCCG-1 1.7077968120575 3.33323812484741 CD8+ Tem -Sample_3_TTGAACGGTCTCAACA-1 0.16275642812252 0.10180226713419 NK cells -Sample_3_TTGAACGTCAGTTCGA-1 -0.843507289886475 8.61132049560547 CD8+ T-cells -Sample_3_TTGACTTTCCCTAACC-1 2.670161485672 -10.9075269699097 NK cells -Sample_3_TTGCCGTCACCTTGTC-1 4.27182245254517 -8.85572338104248 NK cells -Sample_3_TTGCCGTTCTTGCAAG-1 2.52126812934875 4.84965372085571 CD8+ Tcm -Sample_3_TTGCGTCCAGCCTTGG-1 1.37443399429321 3.12751388549805 CD8+ Tem -Sample_3_TTGCGTCTCACGGTTA-1 -0.902223527431488 7.22042655944824 CD8+ Tcm -Sample_3_TTGCGTCTCCTATTCA-1 4.59255695343018 6.41105365753174 CD8+ Tem -Sample_3_TTGCGTCTCTTCTGGC-1 -1.47658431529999 5.85149574279785 CD8+ Tcm -Sample_3_TTGGCAAAGAGATGAG-1 3.57122921943665 -8.90363693237305 NK cells -Sample_3_TTGGCAAGTATCACCA-1 4.32964086532593 4.9979190826416 CD8+ Tem -Sample_3_TTGTAGGAGTCGTACT-1 4.21509695053101 5.29737854003906 CD8+ Tem -Sample_3_TTTATGCAGCCACTAT-1 1.9352388381958 5.81448745727539 CD8+ Tem -Sample_3_TTTATGCCAGTTAACC-1 4.59991979598999 6.713791847229 CD4+ Tem -Sample_3_TTTGCGCAGGCTACGA-1 3.72126221656799 -10.3079385757446 NK cells -Sample_3_TTTGCGCTCACTATTC-1 4.13837766647339 5.47839593887329 CD8+ Tem -Sample_3_TTTGGTTTCTGTTTGT-1 -0.464156359434128 0.0898076370358467 CD8+ Tem -Sample_3_TTTGTCACAATTGCTG-1 2.50085854530334 2.97856187820435 CD8+ Tem -Sample_3_TTTGTCAGTAGGAGTC-1 -9.54189491271973 -2.63138031959534 Memory B-cells -Sample_3_TTTGTCATCCTCATTA-1 0.553207814693451 -11.1732807159424 NK cells -Sample_4_AAACCTGTCTGCTGTC-1 -0.441392093896866 0.275921195745468 CD8+ Tem -Sample_4_AAACGGGGTTTGCATG-1 -3.81197834014893 8.5780611038208 CD4+ T-cells -Sample_4_AAACGGGTCCTTTACA-1 4.31900024414062 5.99839448928833 CD8+ Tem -Sample_4_AAAGTAGCAAACAACA-1 -2.38874292373657 7.42229270935059 CD8+ T-cells -Sample_4_AAAGTAGCATAGAAAC-1 -2.69520330429077 7.48875379562378 CD4+ T-cells -Sample_4_AAATGCCCATGATCCA-1 2.91867589950562 6.2869758605957 CD8+ Tem -Sample_4_AAATGCCGTTAGTGGG-1 -1.4110689163208 7.4989595413208 CD8+ T-cells -Sample_4_AAATGCCTCAGTTAGC-1 2.36249089241028 6.8055534362793 CD8+ Tem -Sample_4_AACACGTGTCGCTTCT-1 3.02072405815125 7.02020740509033 CD8+ Tem -Sample_4_AACCATGGTCTCACCT-1 -1.00919008255005 4.96875524520874 CD8+ Tcm -Sample_4_AACTCAGGTACCGTAT-1 1.41154098510742 -10.2447385787964 NK cells -Sample_4_AACTCAGGTCCATCCT-1 -0.418437451124191 -9.30278873443604 NK cells -Sample_4_AACTCAGGTCGCTTCT-1 2.96149730682373 7.30643653869629 CD8+ Tcm -Sample_4_AACTCAGTCCTTTCTC-1 1.97340977191925 -10.2578430175781 NK cells -Sample_4_AACTCTTCACACATGT-1 -0.216927230358124 2.67747974395752 CD8+ Tem -Sample_4_AACTCTTGTGCCTGTG-1 -3.73407912254333 8.88540077209473 CD8+ Tcm -Sample_4_AACTCTTTCAAACAAG-1 2.95591306686401 -9.75669574737549 NK cells -Sample_4_AACTCTTTCCTCGCAT-1 -0.368060797452927 0.877899050712585 CD8+ Tcm -Sample_4_AACTGGTGTGCGGTAA-1 2.96936535835266 6.76996660232544 CD8+ Tem -Sample_4_AACTTTCGTAGCGATG-1 2.1764657497406 -11.2292776107788 NK cells -Sample_4_AAGACCTAGATGTCGG-1 3.99948644638062 7.04505491256714 CD8+ Tem -Sample_4_AAGCCGCAGCTGAACG-1 2.94951057434082 -7.05850124359131 NK cells -Sample_4_AAGCCGCGTTCGCGAC-1 1.76625978946686 -4.37805414199829 NK cells -Sample_4_AAGGAGCCAATACGCT-1 2.34533262252808 8.30580234527588 CD8+ Tem -Sample_4_AAGGCAGAGAGGTAGA-1 -0.181867986917496 7.37893152236938 CD8+ Tcm -Sample_4_AAGGCAGAGGAATCGC-1 -2.26968955993652 6.40983819961548 CD4+ T-cells -Sample_4_AAGGTTCGTCATATGC-1 -2.1593713760376 8.575439453125 CD4+ T-cells -Sample_4_AAGGTTCTCAGTGCAT-1 2.85642051696777 7.04971075057983 CD8+ Tcm -Sample_4_AAGTCTGAGAGGTAGA-1 2.38644886016846 -4.7858362197876 NK cells -Sample_4_AATCCAGCAAGCCCAC-1 1.76898717880249 -10.2729921340942 NK cells -Sample_4_AATCCAGGTAAGCACG-1 -0.605815172195435 1.23974645137787 CD8+ Tem -Sample_4_AATCCAGTCACAACGT-1 2.72673320770264 4.78290462493896 CD8+ Tem -Sample_4_AATCGGTCATCACAAC-1 4.14252996444702 5.23344612121582 CD8+ Tcm -Sample_4_ACACCCTCAGCTGTGC-1 2.51007866859436 -8.91355895996094 NK cells -Sample_4_ACACCCTCATACCATG-1 1.36900973320007 -7.36846113204956 NK cells -Sample_4_ACACTGAAGCTGAACG-1 -1.82274782657623 6.15101003646851 CD4+ T-cells -Sample_4_ACACTGATCTTACCTA-1 1.40713441371918 -10.2318534851074 NK cells -Sample_4_ACAGCCGAGCTACCGC-1 -0.271210670471191 -11.002818107605 NK cells -Sample_4_ACAGCTAAGGCAGTCA-1 0.160717591643333 -8.74949645996094 NK cells -Sample_4_ACATACGAGAGAACAG-1 2.0244038105011 5.17639875411987 CD8+ Tem -Sample_4_ACATACGAGGCTAGAC-1 1.30527520179749 2.42105722427368 CD8+ Tem -Sample_4_ACATACGTCAACCAAC-1 4.0503134727478 -9.83982181549072 NK cells -Sample_4_ACATCAGAGGTCGGAT-1 1.95867764949799 -7.37573719024658 NK cells -Sample_4_ACATCAGGTCCGTTAA-1 3.28871321678162 -9.49096870422363 NK cells -Sample_4_ACATGGTGTCGCTTCT-1 4.65954446792603 7.08635759353638 CD8+ Tem -Sample_4_ACCAGTACACAACTGT-1 -1.06519198417664 6.47612285614014 CD8+ Tcm -Sample_4_ACCAGTATCCTCGCAT-1 4.29034376144409 6.52041149139404 CD8+ Tem -Sample_4_ACCAGTATCTGAAAGA-1 1.90517294406891 -7.83093500137329 NK cells -Sample_4_ACCGTAACAAGAAGAG-1 -1.66275990009308 8.78939819335938 CD8+ T-cells -Sample_4_ACCGTAACATTGAGCT-1 0.541090607643127 4.68152570724487 CD8+ Tcm -Sample_4_ACCGTAATCGCTTGTC-1 4.85925436019897 5.95454120635986 CD8+ Tem -Sample_4_ACCTTTAAGCGATATA-1 -1.62054979801178 7.79296588897705 CD8+ T-cells -Sample_4_ACGAGCCAGACCTAGG-1 -1.59242343902588 9.51449966430664 CD4+ T-cells -Sample_4_ACGAGCCGTCTAGTCA-1 -0.930519998073578 -9.59261512756348 NK cells -Sample_4_ACGATACAGAGACGAA-1 3.52305340766907 5.31095886230469 CD8+ Tem -Sample_4_ACGATACCAATACGCT-1 4.12706518173218 -9.71988773345947 NK cells -Sample_4_ACGATACGTCTGCCAG-1 -0.126912474632263 8.97251224517822 CD8+ T-cells -Sample_4_ACGATGTAGAGCTTCT-1 3.04687356948853 5.49647092819214 CD8+ Tem -Sample_4_ACGATGTCATTCACTT-1 3.14541602134705 6.48817300796509 CD8+ Tem -Sample_4_ACGATGTGTCATATCG-1 -0.0723644122481346 4.10259532928467 CD8+ Tcm -Sample_4_ACGCAGCCAAGCGCTC-1 3.45015645027161 6.10743856430054 CD8+ Tcm -Sample_4_ACGCCGAAGGTGTTAA-1 3.34506034851074 -9.83516979217529 NK cells -Sample_4_ACGGAGAAGACAGAGA-1 -1.32186377048492 5.45000076293945 CD8+ Tcm -Sample_4_ACGGCCAGTTGATTCG-1 -1.30683863162994 1.26951336860657 CD8+ Tem -Sample_4_ACGTCAAAGGTAGCTG-1 2.12539958953857 -9.31902408599854 NK cells -Sample_4_ACGTCAATCGGAGCAA-1 3.85719013214111 -9.97425556182861 NK cells -Sample_4_ACGTCAATCGGCATCG-1 3.69266319274902 7.47711372375488 CD4+ Tem -Sample_4_ACTATCTAGCTACCTA-1 1.33930218219757 -9.57081413269043 NK cells -Sample_4_ACTGAACGTCAATGTC-1 -0.780478835105896 8.52621269226074 CD4+ T-cells -Sample_4_ACTGAACTCCCAACGG-1 -1.82173764705658 9.30308532714844 CD4+ T-cells -Sample_4_ACTGCTCCATTAGCCA-1 3.82089948654175 -9.03951740264893 NK cells -Sample_4_ACTGTCCAGAAGAAGC-1 3.47375702857971 3.74435710906982 CD8+ Tem -Sample_4_ACTGTCCGTACCGCTG-1 2.66862893104553 5.92043018341064 CD8+ Tem -Sample_4_ACTTACTAGCCGGTAA-1 -0.79288113117218 6.4877233505249 CD4+ T-cells -Sample_4_ACTTACTGTACCGGCT-1 3.33052778244019 -10.4446430206299 NK cells -Sample_4_ACTTGTTCAGATGGCA-1 3.00056266784668 -8.32422637939453 NK cells -Sample_4_ACTTGTTTCACGATGT-1 1.645139336586 8.40946865081787 CD8+ Tcm -Sample_4_ACTTTCACACAGTCGC-1 -1.44753193855286 9.21219539642334 CD8+ T-cells -Sample_4_ACTTTCAGTAAGGATT-1 3.62578368186951 3.68228530883789 CD8+ Tem -Sample_4_ACTTTCATCAGTTGAC-1 -2.88941478729248 8.2373218536377 CD8+ T-cells -Sample_4_AGAATAGTCAACACCA-1 0.351208239793777 -9.62041759490967 NK cells -Sample_4_AGAGCGAAGGTCATCT-1 2.71201825141907 7.31613492965698 CD8+ Tem -Sample_4_AGAGCGAGTACCGGCT-1 2.45053052902222 -8.99453449249268 NK cells -Sample_4_AGAGCTTAGGAGTTTA-1 1.24902033805847 -9.70102977752686 NK cells -Sample_4_AGAGCTTTCAAGAAGT-1 1.86892509460449 -7.19459819793701 NK cells -Sample_4_AGAGTGGTCCAGTAGT-1 -1.92446672916412 8.39554691314697 CD8+ Tcm -Sample_4_AGAGTGGTCCTTGACC-1 -1.98877990245819 6.52427816390991 CD8+ T-cells -Sample_4_AGCAGCCAGTTCCACA-1 -0.928058445453644 8.49355983734131 CD4+ T-cells -Sample_4_AGCCTAACAGTCTTCC-1 4.18707275390625 -8.5496997833252 NK cells -Sample_4_AGCGTATAGTGCAAGC-1 4.45372581481934 6.62870836257935 CD8+ Tem -Sample_4_AGCGTATGTGACGGTA-1 3.5035514831543 -8.72855091094971 NK cells -Sample_4_AGCGTCGCAGCGAACA-1 1.87029349803925 8.15264129638672 CD8+ Tem -Sample_4_AGCTCCTTCAGAGCTT-1 0.331871658563614 -11.3042163848877 NK cells -Sample_4_AGCTCTCCATTTGCCC-1 2.35161781311035 -4.86351203918457 NK cells -Sample_4_AGCTTGAAGACTAGAT-1 2.54831480979919 -8.79245471954346 NK cells -Sample_4_AGCTTGAGTCTCACCT-1 -2.7892439365387 8.27637100219727 CD8+ T-cells -Sample_4_AGGCCACCAAGCGAGT-1 4.77039432525635 -8.81098937988281 NK cells -Sample_4_AGGCCACCAAGTAATG-1 4.44174575805664 -9.42769718170166 NK cells -Sample_4_AGGCCACCACGGCTAC-1 0.618652403354645 2.32638740539551 CD8+ Tem -Sample_4_AGGCCACTCTCTTATG-1 1.4733989238739 -10.2654304504395 NK cells -Sample_4_AGGCCGTGTAGAGGAA-1 2.82257294654846 5.54938316345215 CD8+ Tcm -Sample_4_AGGCCGTTCATCGATG-1 5.11702871322632 6.80764961242676 CD8+ Tem -Sample_4_AGGGATGGTCTCAACA-1 -0.474834144115448 -0.0524896271526814 CD8+ Tem -Sample_4_AGGGTGACAGTAAGAT-1 2.5605194568634 -10.014256477356 NK cells -Sample_4_AGGTCATCACAGACTT-1 4.12159156799316 -9.55960655212402 NK cells -Sample_4_AGGTCATTCGAGAGCA-1 0.0379535295069218 -8.58825206756592 NK cells -Sample_4_AGGTCCGCAGCTGTGC-1 2.07888078689575 -4.55546140670776 NK cells -Sample_4_AGTAGTCTCACTCCTG-1 1.84760594367981 5.18111848831177 CD8+ Tem -Sample_4_AGTCTTTAGCCTCGTG-1 -1.06231796741486 2.37414741516113 CD8+ Tcm -Sample_4_AGTCTTTAGGATTCGG-1 -0.751634538173676 4.36213159561157 CD8+ Tcm -Sample_4_AGTGAGGAGGCTCTTA-1 1.50854301452637 -9.88154029846191 NK cells -Sample_4_AGTGAGGGTGCACCAC-1 2.03625583648682 8.07965278625488 CD8+ Tem -Sample_4_AGTGAGGTCTGATACG-1 0.749421417713165 3.21944308280945 CD8+ Tem -Sample_4_AGTGTCATCCTTTCGG-1 -1.31132245063782 1.51801705360413 CD8+ Tem -Sample_4_AGTGTCATCTGGGCCA-1 -1.03675699234009 -8.14039325714111 NK cells -Sample_4_AGTGTCATCTTACCTA-1 -0.519922316074371 0.56949657201767 CD8+ Tem -Sample_4_AGTTGGTTCGTCCAGG-1 4.2142162322998 5.56794309616089 CD8+ Tcm -Sample_4_ATAACGCAGTGGCACA-1 2.46136498451233 -4.79567909240723 NK cells -Sample_4_ATAACGCCAGTCAGAG-1 0.18731227517128 1.86297655105591 CD8+ Tcm -Sample_4_ATAACGCGTAGCTTGT-1 -1.28388905525208 7.99421119689941 CD8+ T-cells -Sample_4_ATAAGAGGTGCCTGGT-1 4.33509683609009 -9.30431747436523 NK cells -Sample_4_ATAGACCTCTTTACGT-1 2.42587924003601 -9.35573577880859 NK cells -Sample_4_ATCACGAAGTGGAGTC-1 -0.573104739189148 3.99318623542786 CD8+ Tem -Sample_4_ATCACGATCAGCAACT-1 -2.70308804512024 7.64794588088989 CD4+ T-cells -Sample_4_ATCATCTCAACACGCC-1 -2.99547672271729 7.07107400894165 CD8+ T-cells -Sample_4_ATCATGGAGGTGATAT-1 -0.655697882175446 -10.1247329711914 NK cells -Sample_4_ATCATGGCAGCCAGAA-1 -9.68507099151611 -2.77597045898438 naive B-cells -Sample_4_ATCCACCAGTAGATGT-1 -0.884573578834534 -9.36307430267334 NK cells -Sample_4_ATCCACCCATCGATGT-1 -9.51201343536377 -2.60097193717957 Class-switched memory B-cells -Sample_4_ATCCACCGTAACGACG-1 1.45116758346558 3.06866312026978 CD8+ Tem -Sample_4_ATCCGAAAGAAGATTC-1 3.4659378528595 -8.54224681854248 NK cells -Sample_4_ATCCGAACACTAGTAC-1 -0.416778743267059 -10.1551828384399 NK cells -Sample_4_ATCCGAAGTACACCGC-1 1.54847550392151 -7.92299318313599 NK cells -Sample_4_ATCGAGTCACAACTGT-1 3.3446958065033 6.76155614852905 CD8+ Tem -Sample_4_ATCTACTCACGAGGTA-1 0.18793623149395 -9.83943843841553 NK cells -Sample_4_ATCTACTCATGGGACA-1 0.396301358938217 6.64712142944336 CD4+ Tcm -Sample_4_ATCTACTTCGGCCGAT-1 -1.30414199829102 8.33154010772705 CD8+ Tcm -Sample_4_ATCTGCCAGCGCTCCA-1 2.31005382537842 6.42743062973022 CD8+ Tem -Sample_4_ATCTGCCAGTGACATA-1 4.08317184448242 -9.11158275604248 NK cells -Sample_4_ATCTGCCAGTTGAGTA-1 -0.478581637144089 -9.52898216247559 NK cells -Sample_4_ATCTGCCCACTTAACG-1 1.52556729316711 6.79562330245972 CD8+ Tem -Sample_4_ATCTGCCCATCCCACT-1 1.43322789669037 -7.66765117645264 NK cells -Sample_4_ATCTGCCGTCTCTTTA-1 -0.74528568983078 3.05766201019287 CD8+ Tcm -Sample_4_ATCTGCCGTTAGTGGG-1 0.702815890312195 8.4635705947876 CD8+ Tem -Sample_4_ATCTGCCTCGCTTAGA-1 0.577290594577789 3.95420861244202 CD8+ Tem -Sample_4_ATGAGGGGTACCGTAT-1 -1.29292392730713 0.201977849006653 CD8+ Tcm -Sample_4_ATGCGATGTTCTGTTT-1 3.26878881454468 5.76602363586426 CD8+ Tem -Sample_4_ATGGGAGAGTTGTAGA-1 3.09738969802856 -6.99935054779053 NK cells -Sample_4_ATGTGTGAGCATCATC-1 1.15974271297455 -10.4609394073486 NK cells -Sample_4_ATGTGTGAGGTCATCT-1 0.783474087715149 -9.22678279876709 NK cells -Sample_4_ATGTGTGGTGTATGGG-1 2.91850590705872 4.4542670249939 CD8+ Tem -Sample_4_ATTACTCAGGTCATCT-1 -0.583621621131897 -10.5457916259766 NK cells -Sample_4_ATTACTCGTGGAAAGA-1 0.645171403884888 1.93687129020691 CD8+ Tcm -Sample_4_ATTATCCAGTAGCGGT-1 -0.0460267886519432 -10.6186895370483 NK cells -Sample_4_ATTATCCCACCCATGG-1 4.82932233810425 6.57686901092529 CD8+ Tcm -Sample_4_ATTCTACCAAGCGATG-1 -0.321604818105698 3.93373942375183 CD8+ Tcm -Sample_4_ATTCTACCATTAACCG-1 1.39911735057831 5.96888399124146 CD8+ Tem -Sample_4_ATTCTACGTCTAGTCA-1 0.721874713897705 -9.7070484161377 NK cells -Sample_4_ATTCTACGTGGTAACG-1 -1.56551122665405 0.192362993955612 CD8+ Tem -Sample_4_ATTGGACCAGCGTTCG-1 -1.76123487949371 0.738894641399384 CD8+ Tem -Sample_4_ATTGGACCAGCTTCGG-1 1.4244966506958 2.57818412780762 CD8+ Tem -Sample_4_ATTGGACGTAGCGTGA-1 -2.21579098701477 8.79263687133789 CD8+ T-cells -Sample_4_ATTGGACGTCGCATCG-1 -0.493488222360611 -7.53775548934937 NK cells -Sample_4_ATTGGTGAGCCCGAAA-1 3.70485877990723 8.1004695892334 CD8+ Tem -Sample_4_CAACCAATCCTTGGTC-1 1.60537970066071 -10.6601524353027 NK cells -Sample_4_CAACTAGCATGGTAGG-1 0.868809759616852 -10.8845243453979 NK cells -Sample_4_CAACTAGTCGCATGGC-1 3.62499237060547 4.68763732910156 CD8+ Tem -Sample_4_CAAGAAAAGACTAAGT-1 -0.41252812743187 -9.95775032043457 NK cells -Sample_4_CAAGAAAAGTTAGCGG-1 -0.856565356254578 -9.39055252075195 NK cells -Sample_4_CAAGAAATCTATGTGG-1 2.74142980575562 -7.81566858291626 NK cells -Sample_4_CAAGATCGTTACTGAC-1 -0.514392971992493 0.305047690868378 CD8+ Tem -Sample_4_CAAGATCGTTCAACCA-1 4.06712341308594 -9.04968929290771 NK cells -Sample_4_CAAGATCGTTCAGCGC-1 2.468829870224 6.86499357223511 CD8+ Tem -Sample_4_CAAGATCGTTTAGGAA-1 -2.9012770652771 8.59417247772217 CD8+ T-cells -Sample_4_CAAGTTGGTAACGCGA-1 -2.31818532943726 6.47965097427368 CD8+ T-cells -Sample_4_CACAAACAGTTCGATC-1 2.83846855163574 4.81039619445801 CD8+ Tem -Sample_4_CACACAACAATCACAC-1 -1.26386618614197 8.81221866607666 CD8+ T-cells -Sample_4_CACACAAGTCGCGTGT-1 -0.241774424910545 8.77503871917725 CD8+ Tem -Sample_4_CACACCTCAAACCTAC-1 0.00739712221547961 -11.1960535049438 NK cells -Sample_4_CACACCTTCCCATTTA-1 3.45293807983398 -9.95949554443359 NK cells -Sample_4_CACACTCCAGTACACT-1 0.231815993785858 -9.88775539398193 NK cells -Sample_4_CACACTCCATGTAAGA-1 -1.03064298629761 9.8472957611084 CD8+ Tcm -Sample_4_CACACTCTCTAACCGA-1 0.949794411659241 1.35567462444305 CD8+ Tem -Sample_4_CACAGGCGTGGGTATG-1 2.09127378463745 -11.203236579895 NK cells -Sample_4_CACAGTAAGTGACATA-1 -1.69412589073181 0.946304261684418 CD8+ Tem -Sample_4_CACATAGCAAGCCGCT-1 -1.83098268508911 9.21330833435059 CD8+ T-cells -Sample_4_CACATAGGTAGCAAAT-1 -0.580059170722961 -10.4024906158447 NK cells -Sample_4_CACATAGTCGCGTTTC-1 -2.26157689094543 7.09643888473511 CD4+ T-cells -Sample_4_CACATTTCATTGCGGC-1 -0.428848832845688 7.17079639434814 CD8+ Tcm -Sample_4_CACATTTTCCGTCATC-1 -1.3519401550293 8.29600811004639 CD4+ T-cells -Sample_4_CACCACTCACTCGACG-1 0.0745825469493866 2.07551598548889 CD8+ Tem -Sample_4_CACCACTTCATACGGT-1 1.27495527267456 -8.21658706665039 NK cells -Sample_4_CACCACTTCCGCAAGC-1 1.63321590423584 -9.586594581604 NK cells -Sample_4_CACCACTTCTTGAGAC-1 1.59616422653198 -8.4629545211792 NK cells -Sample_4_CACCAGGCACCTCGTT-1 -1.72269129753113 0.291505247354507 CD8+ Tem -Sample_4_CACCAGGTCAAGGTAA-1 1.87611615657806 -5.33736085891724 NK cells -Sample_4_CACCTTGTCTCTAAGG-1 0.290066599845886 6.03847646713257 CD4+ Tem -Sample_4_CAGAGAGCATCACGTA-1 0.0296014714986086 5.67794036865234 Tregs -Sample_4_CAGATCAAGATATGGT-1 4.75585556030273 5.61840200424194 CD8+ Tem -Sample_4_CAGCAGCGTCGCCATG-1 0.031576968729496 9.14037704467773 CD8+ Tcm -Sample_4_CAGCAGCTCGGAATCT-1 -9.73073768615723 -2.82229518890381 Memory B-cells -Sample_4_CAGCAGCTCTGTCCGT-1 4.43347454071045 6.77809476852417 CD8+ Tem -Sample_4_CAGCCGAAGCGACGTA-1 1.49459505081177 -8.54748916625977 NK cells -Sample_4_CAGCGACAGTAGCGGT-1 1.97490048408508 -6.81227493286133 NK cells -Sample_4_CAGCTGGAGTGTACGG-1 2.93184804916382 -9.3095703125 NK cells -Sample_4_CAGCTGGTCTTTCCTC-1 0.555420994758606 -10.3475761413574 NK cells -Sample_4_CAGTAACCATCTCGCT-1 -1.09221577644348 2.67556142807007 CD8+ Tem -Sample_4_CAGTAACTCTGTCCGT-1 -0.694556534290314 9.16415882110596 CD8+ Tcm -Sample_4_CAGTCCTAGACAAGCC-1 1.10067844390869 -7.62287664413452 NK cells -Sample_4_CAGTCCTAGATCTGCT-1 -1.92243087291718 8.37854480743408 CD8+ T-cells -Sample_4_CAGTCCTAGTAGATGT-1 4.6773853302002 5.20285940170288 CD8+ Tcm -Sample_4_CAGTCCTCAGGCGATA-1 3.46707391738892 6.65273904800415 CD4+ Tem -Sample_4_CAGTCCTGTTCAGCGC-1 2.94031476974487 4.31161212921143 CD8+ Tem -Sample_4_CAGTCCTTCTCTTGAT-1 3.8479175567627 4.97932195663452 CD8+ Tem -Sample_4_CATATGGTCTGTGCAA-1 2.09922289848328 -4.70929527282715 NK cells -Sample_4_CATATTCCAGTGACAG-1 -2.66324639320374 6.69612121582031 CD8+ Tcm -Sample_4_CATATTCGTAAATACG-1 2.51683568954468 8.21828174591064 CD8+ Tem -Sample_4_CATCAAGAGGTGTTAA-1 0.246334820985794 5.78072595596313 CD8+ Tcm -Sample_4_CATCAAGCACGACGAA-1 5.16487169265747 6.68360328674316 CD8+ Tem -Sample_4_CATCAAGCAGGCAGTA-1 3.76885175704956 -9.47387313842773 NK cells -Sample_4_CATCAAGCATGTTGAC-1 0.753702700138092 2.57761478424072 CD8+ Tem -Sample_4_CATCAAGGTCTCCACT-1 3.86490988731384 -9.48742485046387 NK cells -Sample_4_CATCAGAAGAAACCAT-1 -0.281968593597412 5.86942911148071 CD8+ Tcm -Sample_4_CATCAGACACCCTATC-1 3.32271528244019 5.64789438247681 CD8+ Tem -Sample_4_CATCAGAGTCCATGAT-1 0.542875111103058 -9.09690761566162 NK cells -Sample_4_CATCAGAGTCCGAAGA-1 -1.28924214839935 -0.0808154940605164 CD8+ Tcm -Sample_4_CATCCACGTCCCTTGT-1 -0.0922311618924141 5.14866828918457 CD8+ Tcm -Sample_4_CATCCACGTGCATCTA-1 0.035347692668438 6.55699062347412 CD4+ Tem -Sample_4_CATCCACTCTGAGGGA-1 3.06420087814331 -9.21440982818604 NK cells -Sample_4_CATCGAAAGGAATCGC-1 -1.08698844909668 6.85142230987549 CD8+ Tcm -Sample_4_CATCGAACAGGCTGAA-1 -2.55363869667053 7.41505336761475 CD8+ T-cells -Sample_4_CATCGAACAGGGAGAG-1 3.0362401008606 7.06713724136353 CD8+ Tem -Sample_4_CATCGGGCAAAGGTGC-1 0.0243236906826496 -11.2618923187256 NK cells -Sample_4_CATGACAAGTGAACGC-1 2.67769050598145 7.30754327774048 CD8+ Tem -Sample_4_CATGACACACAGACTT-1 2.00833654403687 -10.5155076980591 NK cells -Sample_4_CATGACACACCGTTGG-1 2.93715476989746 3.84529709815979 CD8+ Tem -Sample_4_CATGACACAGCTATTG-1 3.65375351905823 -9.3817195892334 NK cells -Sample_4_CATGACAGTCTCACCT-1 2.97021102905273 4.77485466003418 CD8+ Tem -Sample_4_CATGCCTCAATGAAAC-1 1.52851974964142 -9.78715324401855 NK cells -Sample_4_CATGGCGAGTGTGGCA-1 2.75222754478455 -7.21822786331177 NK cells -Sample_4_CATGGCGCACGAGGTA-1 1.96093702316284 -8.02787208557129 NK cells -Sample_4_CATGGCGGTACAGTGG-1 0.305569916963577 -8.43215847015381 NK cells -Sample_4_CATTATCAGGGTTCCC-1 2.49715375900269 -10.3624095916748 NK cells -Sample_4_CATTATCCACAGCCCA-1 -3.83073496818542 8.62426280975342 CD8+ Tcm -Sample_4_CATTATCGTGGTCTCG-1 1.98200595378876 7.47884464263916 CD4+ Tem -Sample_4_CCAATCCGTCTCTTAT-1 -0.541877031326294 9.56807136535645 CD8+ Tcm -Sample_4_CCAATCCTCTCATTCA-1 -1.3502916097641 8.88281917572021 CD4+ T-cells -Sample_4_CCACCTATCTTGCCGT-1 0.759855031967163 -11.1925487518311 NK cells -Sample_4_CCACGGACAATCACAC-1 3.47255277633667 -9.91471290588379 NK cells -Sample_4_CCACGGATCAGGCAAG-1 2.31029558181763 -4.83527517318726 NK cells -Sample_4_CCACTACAGTGGACGT-1 3.74550342559814 6.19755125045776 CD8+ Tem -Sample_4_CCAGCGAGTTCGTTGA-1 4.42884016036987 7.19988441467285 CD8+ Tem -Sample_4_CCATTCGCAAGCCATT-1 -2.20666813850403 9.40115356445312 CD8+ Tcm -Sample_4_CCCAATCTCAACACCA-1 3.81726980209351 7.24668455123901 CD4+ Tem -Sample_4_CCCTCCTAGTAGCCGA-1 -0.649967789649963 -9.48384952545166 NK cells -Sample_4_CCCTCCTGTTGTACAC-1 -2.41132116317749 9.12191867828369 CD8+ T-cells -Sample_4_CCGGTAGAGTTACGGG-1 -1.78318679332733 9.40444183349609 CD4+ T-cells -Sample_4_CCGGTAGTCGCCTGAG-1 -0.362636178731918 -10.1273860931396 NK cells -Sample_4_CCGTACTCAGGGTATG-1 3.99856400489807 6.13421297073364 CD8+ Tcm -Sample_4_CCGTACTTCTACTATC-1 2.19634461402893 -10.1909503936768 NK cells -Sample_4_CCGTTCAAGCCCGAAA-1 1.09164237976074 -8.95924377441406 NK cells -Sample_4_CCTAAAGAGCGATTCT-1 3.69366049766541 5.4032301902771 CD8+ Tcm -Sample_4_CCTAAAGCAAGTAATG-1 3.46142077445984 6.19719266891479 CD8+ Tem -Sample_4_CCTACACTCTTTACGT-1 3.13015651702881 -10.9573211669922 NK cells -Sample_4_CCTACCACATCGGAAG-1 -0.887716293334961 0.23839445412159 CD8+ Tcm -Sample_4_CCTAGCTAGAGGTACC-1 -1.3592209815979 8.04293060302734 CD4+ T-cells -Sample_4_CCTATTATCAAACCAC-1 0.774684965610504 7.9462718963623 CD8+ Tcm -Sample_4_CCTCTGAGTAAGAGGA-1 -0.826628684997559 0.233779400587082 CD8+ Tcm -Sample_4_CCTCTGAGTTATTCTC-1 3.36406683921814 7.10299730300903 CD8+ Tem -Sample_4_CCTCTGATCAGCCTAA-1 0.749646902084351 -8.06550598144531 NK cells -Sample_4_CCTTACGAGCACCGCT-1 2.25183844566345 -9.43730545043945 NK cells -Sample_4_CCTTACGGTCGCATCG-1 1.83694529533386 -6.6435694694519 NK cells -Sample_4_CCTTCCCCAAACCTAC-1 -0.198623076081276 9.47645950317383 CD8+ Tcm -Sample_4_CCTTCCCTCACTGGGC-1 -1.83327043056488 7.38699150085449 CD8+ T-cells -Sample_4_CCTTCGAAGCGTTTAC-1 3.47576665878296 -8.51034736633301 NK cells -Sample_4_CCTTCGACAAGAAGAG-1 3.5662407875061 6.91966342926025 CD8+ Tem -Sample_4_CGAACATAGTCAAGCG-1 1.89536428451538 -6.85836505889893 NK cells -Sample_4_CGAACATCATCAGTCA-1 3.93132901191711 3.95299291610718 CD8+ Tem -Sample_4_CGAATGTGTCATTAGC-1 1.10314679145813 8.5892972946167 CD4+ Tem -Sample_4_CGACCTTAGCACCGCT-1 -0.253065824508667 5.34703874588013 CD8+ Tcm -Sample_4_CGACCTTAGTTGTAGA-1 -9.55367946624756 -2.64242100715637 naive B-cells -Sample_4_CGACCTTTCAAGCCTA-1 -0.98992520570755 0.725528180599213 CD8+ Tem -Sample_4_CGACTTCTCATAGCAC-1 3.08417701721191 -8.87587451934814 NK cells -Sample_4_CGAGCACAGACGCAAC-1 1.32329297065735 -10.1918830871582 NK cells -Sample_4_CGAGCACAGCTCAACT-1 5.32076787948608 6.49257898330688 CD8+ Tem -Sample_4_CGAGCACAGGCGTACA-1 -2.94087171554565 7.7086443901062 CD8+ T-cells -Sample_4_CGAGCACTCTGCAAGT-1 -2.83979749679565 7.9252233505249 CD8+ T-cells -Sample_4_CGAGCCACATGCAACT-1 -2.08019113540649 7.25218200683594 CD8+ T-cells -Sample_4_CGAGCCATCGTGGTCG-1 0.190497323870659 4.32825374603271 CD8+ Tem -Sample_4_CGATCGGAGACGCTTT-1 -0.773147284984589 1.41506958007812 CD8+ Tem -Sample_4_CGATCGGGTCCCTTGT-1 -0.528613209724426 1.16456043720245 NK cells -Sample_4_CGATGGCTCGGTCCGA-1 -0.350359976291656 9.59238719940186 CD8+ T-cells -Sample_4_CGATGTAAGGACATTA-1 2.70415425300598 5.82664203643799 CD8+ Tcm -Sample_4_CGATTGAGTGGTACAG-1 2.35494804382324 -7.35420608520508 NK cells -Sample_4_CGCCAAGAGACCACGA-1 -1.44305884838104 0.32533672451973 CD8+ Tem -Sample_4_CGCCAAGGTGTCCTCT-1 2.54869365692139 -11.2578582763672 NK cells -Sample_4_CGCGGTAAGGCATGGT-1 3.35390257835388 -8.49584484100342 NK cells -Sample_4_CGCGGTAAGTATCGAA-1 1.45929300785065 -10.3366947174072 NK cells -Sample_4_CGCGGTACACAGGCCT-1 -0.607682168483734 -9.49770450592041 NK cells -Sample_4_CGCGGTATCAACGCTA-1 1.54290068149567 -8.08938598632812 NK cells -Sample_4_CGCGGTATCTCTGCTG-1 -1.1296398639679 5.07925653457642 CD8+ Tcm -Sample_4_CGCGTTTTCCAGATCA-1 1.4546959400177 2.61207580566406 CD8+ Tem -Sample_4_CGCTATCCACTGTCGG-1 -2.3868350982666 7.15400409698486 CD8+ T-cells -Sample_4_CGCTATCGTGCAGACA-1 1.54044330120087 -10.227858543396 NK cells -Sample_4_CGCTATCTCGCCATAA-1 1.062619805336 -7.86687278747559 NK cells -Sample_4_CGCTATCTCTGTACGA-1 0.642769694328308 4.79257440567017 CD8+ Tcm -Sample_4_CGCTGGATCCTAGGGC-1 -0.349677801132202 7.43659067153931 CD8+ Tcm -Sample_4_CGCTTCACATTTCAGG-1 3.18506240844727 5.50397443771362 CD8+ Tem -Sample_4_CGGACACAGCAGCGTA-1 -1.69395339488983 9.65801906585693 CD8+ Tcm -Sample_4_CGGACACAGGTGCTTT-1 0.723231375217438 -8.61409664154053 NK cells -Sample_4_CGGACACTCCTCGCAT-1 -0.921796262264252 2.71702575683594 CD8+ Tem -Sample_4_CGGACGTTCAGGTTCA-1 3.83988213539124 7.66978120803833 CD8+ Tem -Sample_4_CGGACTGTCTCTGAGA-1 -0.45953157544136 7.32730531692505 CD8+ Tcm -Sample_4_CGGAGCTCAAACTGTC-1 3.28523921966553 3.92335724830627 CD8+ Tem -Sample_4_CGGAGCTGTACCGTTA-1 3.91034436225891 7.25612497329712 CD8+ Tem -Sample_4_CGGAGTCTCAGGCCCA-1 0.370376229286194 -9.9898853302002 NK cells -Sample_4_CGTAGCGCAAACAACA-1 3.28239107131958 -7.27099084854126 NK cells -Sample_4_CGTAGCGTCAGCACAT-1 2.7889256477356 -8.04408073425293 NK cells -Sample_4_CGTAGCGTCCTAGAAC-1 1.96660578250885 -4.69207048416138 NK cells -Sample_4_CGTAGGCGTCTGATTG-1 -0.540888547897339 7.58581209182739 CD8+ Tcm -Sample_4_CGTCAGGCAAGCTGTT-1 -0.0512369237840176 -9.26284599304199 NK cells -Sample_4_CGTCAGGTCAAGGTAA-1 -2.36622452735901 9.19214534759521 CD4+ Tcm -Sample_4_CGTCCATAGCTATGCT-1 2.04772734642029 -8.20678806304932 NK cells -Sample_4_CGTCCATCACCCTATC-1 2.80704021453857 5.87118864059448 CD8+ Tem -Sample_4_CGTCTACCATAGGATA-1 -1.05429327487946 8.8824520111084 CD4+ T-cells -Sample_4_CGTGAGCCATTCTTAC-1 1.83727610111237 8.04073715209961 CD8+ Tcm -Sample_4_CGTGAGCTCAATCTCT-1 -9.71537780761719 -2.8054986000061 naive B-cells -Sample_4_CGTTGGGTCAGAGACG-1 4.21422386169434 7.29021406173706 CD8+ Tem -Sample_4_CGTTGGGTCCTTGCCA-1 -1.31996488571167 5.95880365371704 CD8+ Tcm -Sample_4_CTAACTTAGCCCAGCT-1 3.60165405273438 5.64022207260132 CD8+ Tem -Sample_4_CTAAGACAGTGTCTCA-1 0.662050008773804 -10.4070987701416 NK cells -Sample_4_CTAAGACCAGATCGGA-1 1.88608813285828 8.11819362640381 CD8+ Tem -Sample_4_CTAAGACGTCGGATCC-1 1.87449193000793 -4.63822603225708 NK cells -Sample_4_CTAAGACTCCACTGGG-1 1.93803751468658 -4.91024684906006 NK cells -Sample_4_CTAATGGCACAGACTT-1 -1.43137001991272 7.56065654754639 CD8+ T-cells -Sample_4_CTAATGGGTTCAGGCC-1 1.77365148067474 -7.02016925811768 NK cells -Sample_4_CTACACCCAACGCACC-1 2.23020839691162 -8.5327091217041 NK cells -Sample_4_CTACACCGTTCAGCGC-1 3.53624677658081 -9.87158203125 NK cells -Sample_4_CTACACCTCGGTTCGG-1 2.065110206604 5.52213191986084 CD4+ Tem -Sample_4_CTACATTCATGCCCGA-1 3.07442283630371 -8.81100177764893 NK cells -Sample_4_CTACATTGTTTGGCGC-1 3.41028308868408 -7.68258142471313 NK cells -Sample_4_CTACCCAAGGTGCACA-1 2.45562028884888 -5.15124702453613 NK cells -Sample_4_CTACCCACAAGGTGTG-1 -1.37074077129364 7.78947496414185 CD8+ T-cells -Sample_4_CTACCCACACGACTCG-1 2.2301549911499 -7.07437515258789 NK cells -Sample_4_CTACCCACAGCGATCC-1 -0.0115415276959538 -10.9189796447754 NK cells -Sample_4_CTACGTCAGAGCTGCA-1 -1.30370879173279 8.59014511108398 CD4+ T-cells -Sample_4_CTACGTCAGGCGATAC-1 3.14234137535095 7.08226776123047 CD8+ Tem -Sample_4_CTACGTCGTCTCAACA-1 1.80032920837402 -8.60989379882812 NK cells -Sample_4_CTACGTCTCGTCGTTC-1 2.01942753791809 -8.54211235046387 NK cells -Sample_4_CTAGAGTCAATCTACG-1 1.06161785125732 -9.60361099243164 NK cells -Sample_4_CTAGAGTCAGGAATCG-1 -0.441415190696716 0.802632212638855 CD8+ Tem -Sample_4_CTAGAGTTCAACTCTT-1 3.16670107841492 6.16503572463989 CD8+ Tem -Sample_4_CTAGTGACAGACGCTC-1 -1.17568814754486 7.01918458938599 CD8+ T-cells -Sample_4_CTAGTGACAGCGTAAG-1 -0.58835506439209 7.02424383163452 CD8+ Tem -Sample_4_CTAGTGACATCTATGG-1 0.903838694095612 6.63483953475952 CD4+ Tem -Sample_4_CTAGTGATCCTTGACC-1 -0.151729226112366 9.07928848266602 CD8+ T-cells -Sample_4_CTCACACTCACATACG-1 2.84571480751038 5.239098072052 CD8+ Tem -Sample_4_CTCAGAATCGGAAATA-1 -1.29622542858124 1.72433984279633 CD8+ Tem -Sample_4_CTCATTAAGTCGTTTG-1 -0.0216594841331244 8.19995021820068 CD8+ Tcm -Sample_4_CTCCTAGTCGGCGCTA-1 -1.00029146671295 0.814678966999054 CD8+ Tem -Sample_4_CTCGAAAAGAACAATC-1 0.732138156890869 5.21873617172241 CD8+ Tcm -Sample_4_CTCGAAAAGACGACGT-1 -0.980774581432343 3.15572476387024 CD8+ Tem -Sample_4_CTCGAAACACAGCGTC-1 4.61449813842773 -8.86505794525146 NK cells -Sample_4_CTCGGGAGTTCGGCAC-1 0.658286869525909 5.38132286071777 CD8+ Tem -Sample_4_CTCGTCACACACTGCG-1 0.784248530864716 -9.96391105651855 NK cells -Sample_4_CTCGTCACACAGGAGT-1 0.52077579498291 -9.72446823120117 NK cells -Sample_4_CTCGTCAGTTCCACGG-1 4.50846242904663 4.42498111724854 CD8+ Tcm -Sample_4_CTCTAATAGTCCGTAT-1 0.644814908504486 -11.0041341781616 NK cells -Sample_4_CTCTAATAGTGGGTTG-1 4.23060894012451 5.00617361068726 CD8+ Tem -Sample_4_CTCTACGAGGTGCACA-1 3.95114231109619 4.6629433631897 CD8+ Tem -Sample_4_CTCTACGTCGAACGGA-1 4.7053050994873 7.01615238189697 CD4+ Tem -Sample_4_CTCTGGTAGCGATATA-1 3.47962355613708 4.87794256210327 CD8+ Tem -Sample_4_CTCTGGTAGGGAACGG-1 -0.412609487771988 8.2377872467041 CD8+ T-cells -Sample_4_CTCTGGTAGGTCGGAT-1 2.86601662635803 7.1994047164917 CD8+ Tem -Sample_4_CTCTGGTCACTGCCAG-1 3.33236575126648 3.91104912757874 CD8+ Tem -Sample_4_CTGAAACGTGCCTGTG-1 0.545451879501343 -8.09907054901123 NK cells -Sample_4_CTGAAGTCACGAAGCA-1 -2.11571025848389 8.14039993286133 CD8+ T-cells -Sample_4_CTGAAGTTCCGCAAGC-1 2.40491962432861 -8.86037921905518 NK cells -Sample_4_CTGAAGTTCGCAAACT-1 -2.02889680862427 8.51068782806396 CD4+ T-cells -Sample_4_CTGATAGAGCGTCAAG-1 -0.148602157831192 9.55273246765137 CD8+ Tem -Sample_4_CTGATCCTCATAACCG-1 4.8563346862793 6.07413005828857 CD8+ Tem -Sample_4_CTGATCCTCGCAAGCC-1 -2.53957796096802 8.39149475097656 CD8+ T-cells -Sample_4_CTGATCCTCTACTATC-1 -1.1503632068634 7.01016616821289 CD8+ T-cells -Sample_4_CTGCCTAAGAAGGTGA-1 0.904857516288757 -9.31028175354004 NK cells -Sample_4_CTGCCTAAGACAGAGA-1 0.756010711193085 3.19539856910706 CD8+ Tem -Sample_4_CTGCCTACAGACAGGT-1 4.09913921356201 -9.85410976409912 NK cells -Sample_4_CTGCTGTCAACGATCT-1 0.0339944921433926 -7.80442380905151 NK cells -Sample_4_CTGGTCTCAACCGCCA-1 4.55977630615234 4.9577751159668 CD8+ Tem -Sample_4_CTGGTCTGTATCAGTC-1 -0.163820058107376 -10.1280717849731 NK cells -Sample_4_CTGGTCTGTTGACGTT-1 1.89174854755402 7.96364307403564 CD8+ Tem -Sample_4_CTGGTCTGTTGTCGCG-1 -1.52351379394531 9.24864387512207 CD8+ T-cells -Sample_4_CTGTGCTCACATGGGA-1 -0.523701667785645 9.35876655578613 CD8+ T-cells -Sample_4_CTGTGCTCACGGCCAT-1 2.57604312896729 5.46818542480469 CD8+ Tem -Sample_4_CTGTGCTTCAGCGATT-1 4.13501739501953 7.36677169799805 CD8+ Tem -Sample_4_CTTAACTGTGGACGAT-1 -1.08851373195648 8.33471870422363 CD4+ T-cells -Sample_4_CTTACCGAGCGTCAAG-1 2.03011083602905 7.48927068710327 CD8+ Tem -Sample_4_CTTAGGAAGCCAACAG-1 -1.03774440288544 8.06512546539307 CD8+ T-cells -Sample_4_CTTCTCTAGTATCGAA-1 3.13842058181763 5.21912717819214 CD8+ Tem -Sample_4_CTTCTCTAGTGCGATG-1 1.56030488014221 -10.5971689224243 NK cells -Sample_4_CTTTGCGTCGGGAGTA-1 3.21726894378662 7.81453084945679 CD8+ Tcm -Sample_4_GAAACTCAGTACGTTC-1 -0.0722941383719444 3.0442430973053 CD8+ Tem -Sample_4_GAAACTCGTCTGCCAG-1 1.41671407222748 1.76972341537476 CD8+ Tem -Sample_4_GAAATGAAGAAGGGTA-1 2.49100112915039 5.23477792739868 CD8+ Tcm -Sample_4_GAAATGAGTGTTAAGA-1 2.20198965072632 5.61635732650757 CD8+ Tem -Sample_4_GAACATCTCCTTGGTC-1 0.417959570884705 8.93068408966064 CD8+ Tcm -Sample_4_GAACCTACAACACCCG-1 -2.55022239685059 8.62898349761963 CD8+ T-cells -Sample_4_GAACGGAAGCAAATCA-1 2.76051425933838 6.69839286804199 CD8+ Tem -Sample_4_GAAGCAGGTTCAGGCC-1 2.66348147392273 -10.1153745651245 NK cells -Sample_4_GAAGCAGTCTGTGCAA-1 2.85053014755249 -10.7694835662842 NK cells -Sample_4_GAATAAGAGCGATTCT-1 2.34409928321838 6.32629680633545 CD8+ Tem -Sample_4_GAATAAGAGTTGAGAT-1 0.451137244701385 -9.29934120178223 NK cells -Sample_4_GAATAAGGTACCGTTA-1 -0.906922996044159 9.71813869476318 CD8+ T-cells -Sample_4_GAATAAGTCCACTCCA-1 0.364830940961838 -10.4551219940186 NK cells -Sample_4_GACACGCCACGACGAA-1 1.85791599750519 6.332200050354 CD4+ Tem -Sample_4_GACACGCGTAACGTTC-1 -0.385885506868362 4.16464567184448 CD8+ Tcm -Sample_4_GACAGAGAGGCGTACA-1 1.7986261844635 -5.76750612258911 NK cells -Sample_4_GACAGAGTCGAGAACG-1 -1.22528493404388 0.469820827245712 CD8+ Tem -Sample_4_GACCAATGTGAGCGAT-1 -0.586983323097229 3.49679851531982 NK cells -Sample_4_GACCTGGCACTTCGAA-1 2.07273411750793 -9.86728572845459 NK cells -Sample_4_GACCTGGCAGTAAGAT-1 -0.490604519844055 0.0147863179445267 NK cells -Sample_4_GACGGCTGTTGATTGC-1 -0.20732544362545 -10.2955532073975 NK cells -Sample_4_GACGGCTGTTTGGGCC-1 3.59995174407959 8.15137004852295 CD8+ Tem -Sample_4_GACGGCTTCAACGCTA-1 2.86558723449707 6.3006157875061 CD8+ Tem -Sample_4_GACGTGCTCACCATAG-1 -1.23814153671265 0.261791884899139 CD8+ Tem -Sample_4_GACGTTAAGAATAGGG-1 0.81626033782959 -8.64441967010498 NK cells -Sample_4_GACGTTAAGTGTACGG-1 -0.723692119121552 8.21931552886963 CD8+ T-cells -Sample_4_GACTACAAGTACACCT-1 3.7502076625824 7.46552705764771 CD8+ Tem -Sample_4_GACTACATCGGAATCT-1 2.0419807434082 -9.06209373474121 NK cells -Sample_4_GACTGCGGTTCGGCAC-1 5.03668117523193 6.16010570526123 CD8+ Tem -Sample_4_GACTGCGTCAGTTGAC-1 -1.81715440750122 7.72609281539917 CD4+ T-cells -Sample_4_GACTGCGTCTGTACGA-1 -3.03465247154236 7.62672424316406 CD4+ T-cells -Sample_4_GAGCAGAAGTGCTGCC-1 -2.15155577659607 9.19285678863525 CD8+ Tcm -Sample_4_GAGCAGAGTCTTGATG-1 -0.660983443260193 -9.23805904388428 NK cells -Sample_4_GAGGTGAGTCCTAGCG-1 -3.88304853439331 8.91674423217773 CD8+ Tcm -Sample_4_GAGGTGATCCCTAACC-1 4.53762006759644 -8.25377750396729 NK cells -Sample_4_GATCAGTAGAGTTGGC-1 -0.716016054153442 9.0363187789917 CD4+ T-cells -Sample_4_GATCAGTAGTAGCGGT-1 -2.31725335121155 8.80664157867432 CD8+ T-cells -Sample_4_GATCAGTCAAAGCGGT-1 -0.69595068693161 8.8181734085083 CD8+ T-cells -Sample_4_GATCAGTTCCGAGCCA-1 0.507834613323212 -9.86602973937988 NK cells -Sample_4_GATCAGTTCCTCGCAT-1 0.417182296514511 -8.63998222351074 NK cells -Sample_4_GATCGATGTTCCCGAG-1 -1.19271612167358 7.84647941589355 CD8+ T-cells -Sample_4_GATCGATTCCACGCAG-1 1.98945033550262 -9.87321281433105 NK cells -Sample_4_GATCGCGAGACCTTTG-1 3.5277054309845 4.56473541259766 CD8+ Tem -Sample_4_GATCGCGCAAGAAAGG-1 2.41729044914246 -4.97959327697754 NK cells -Sample_4_GATCGCGGTCAACTGT-1 4.43166017532349 -8.29131317138672 NK cells -Sample_4_GATCGCGTCAAGGTAA-1 0.375902056694031 -7.13705205917358 NK cells -Sample_4_GATCGTAAGGTACTCT-1 -1.23263216018677 8.73010063171387 CD4+ T-cells -Sample_4_GATGAAAAGACACTAA-1 -1.36091589927673 8.98454761505127 CD8+ T-cells -Sample_4_GATGAAATCGTCTGAA-1 0.539239883422852 -9.85723876953125 NK cells -Sample_4_GATGAGGTCCTAGGGC-1 2.81008338928223 -7.77062845230103 NK cells -Sample_4_GATGCTAAGTCCCACG-1 -2.26488327980042 7.98031425476074 CD8+ T-cells -Sample_4_GATGCTACAAAGGAAG-1 3.18330240249634 4.13144826889038 CD8+ Tem -Sample_4_GATGCTAGTAGAGGAA-1 3.32802820205688 5.94809436798096 CD8+ Tem -Sample_4_GATTCAGAGACAGGCT-1 0.495007395744324 2.60995268821716 CD8+ Tem -Sample_4_GCAAACTCATCCCATC-1 0.166068121790886 1.93350827693939 CD8+ Tcm -Sample_4_GCAAACTGTGGACGAT-1 2.61057472229004 -7.3010835647583 NK cells -Sample_4_GCAATCAAGTAGGCCA-1 3.87194108963013 -9.58748722076416 NK cells -Sample_4_GCAATCAGTCTAGTCA-1 3.41527366638184 -8.00953960418701 NK cells -Sample_4_GCAATCATCACCCTCA-1 1.19235920906067 -8.71486949920654 NK cells -Sample_4_GCAGTTACAACGATGG-1 1.09853327274323 -10.6549663543701 NK cells -Sample_4_GCAGTTAGTTCAGCGC-1 -0.361817866563797 9.3632698059082 CD8+ T-cells -Sample_4_GCATACATCCTGCCAT-1 4.35754728317261 4.31616115570068 CD8+ Tem -Sample_4_GCATACATCTTTAGGG-1 2.58932089805603 -10.238655090332 NK cells -Sample_4_GCATGCGGTCCCTTGT-1 0.817392826080322 5.41855812072754 CD8+ Tcm -Sample_4_GCATGTAAGACGCACA-1 2.19864106178284 6.52956342697144 CD8+ Tem -Sample_4_GCATGTAAGAGTAATC-1 -1.76698863506317 1.24423217773438 CD8+ Tem -Sample_4_GCATGTACACCTCGTT-1 -0.655803799629211 -0.140363126993179 CD8+ Tem -Sample_4_GCCAAATAGTGGAGAA-1 5.30239868164062 5.68880414962769 CD8+ Tem -Sample_4_GCCTCTACAAACTGCT-1 1.73125517368317 2.98586392402649 CD8+ Tcm -Sample_4_GCGACCACATGCCCGA-1 4.33183002471924 4.73381328582764 CD8+ Tcm -Sample_4_GCGAGAAAGCTACCGC-1 -0.993173360824585 9.38336372375488 CD8+ Tcm -Sample_4_GCGAGAACAAGGCTCC-1 2.07136607170105 6.93732023239136 CD8+ T-cells -Sample_4_GCGAGAACACCAGTTA-1 0.560653984546661 -8.54242038726807 NK cells -Sample_4_GCGCAACAGGGTGTGT-1 -0.809902310371399 5.02618360519409 CD8+ Tcm -Sample_4_GCGCAACCACCCAGTG-1 1.20489227771759 -8.8532018661499 NK cells -Sample_4_GCGCAACTCCTATGTT-1 1.62560045719147 7.97047710418701 CD8+ Tcm -Sample_4_GCGCAACTCTTGCATT-1 0.67878657579422 -7.54980564117432 NK cells -Sample_4_GCGCAGTTCCGTCATC-1 -0.300557881593704 -0.168840706348419 CD8+ Tem -Sample_4_GCGCCAACACCGTTGG-1 -3.91555547714233 8.94174385070801 CD8+ Tcm -Sample_4_GCGCGATTCCGTAGTA-1 2.58385920524597 -9.09524822235107 NK cells -Sample_4_GCGGGTTGTGCGCTTG-1 -1.16308581829071 5.42116785049438 CD8+ Tcm -Sample_4_GCTCCTAGTAGAAGGA-1 -0.27324190735817 0.797622442245483 CD8+ Tem -Sample_4_GCTCTGTCAAGTCTAC-1 0.0685698688030243 -9.65400886535645 NK cells -Sample_4_GCTGCAGAGTGGGATC-1 3.84816479682922 7.48202133178711 CD8+ Tem -Sample_4_GCTGCAGCACCACCAG-1 1.15521311759949 -11.0129737854004 NK cells -Sample_4_GCTGCAGGTCGATTGT-1 0.640073597431183 -9.90541553497314 NK cells -Sample_4_GCTGCAGTCCTTTACA-1 3.11285805702209 3.99834227561951 CD8+ Tcm -Sample_4_GCTGCAGTCGGAGGTA-1 4.17864513397217 -8.263991355896 NK cells -Sample_4_GCTGCGAAGGCTACGA-1 -0.362784326076508 0.0673495456576347 CD8+ Tem -Sample_4_GCTGCGAGTGTCAATC-1 -0.692948698997498 9.30006790161133 CD8+ Tcm -Sample_4_GCTGCGATCTGATACG-1 0.915777981281281 -9.51488590240479 NK cells -Sample_4_GCTGGGTCATATGCTG-1 -0.131171464920044 -10.562292098999 NK cells -Sample_4_GCTTCCACACATCCGG-1 4.79168033599854 4.68096017837524 CD8+ Tem -Sample_4_GCTTCCACATCTATGG-1 -0.816711783409119 -9.61010360717773 NK cells -Sample_4_GCTTCCATCTCTGTCG-1 0.454681098461151 4.40617227554321 CD8+ Tcm -Sample_4_GCTTGAACAGTATAAG-1 3.7943229675293 6.67590999603271 CD8+ Tcm -Sample_4_GCTTGAATCTAACCGA-1 0.198622986674309 -8.32938003540039 NK cells -Sample_4_GGAAAGCTCCTTGCCA-1 2.06951451301575 -8.07919406890869 NK cells -Sample_4_GGAACTTCAGATGGCA-1 4.04993963241577 4.82511138916016 CD8+ Tem -Sample_4_GGAATAACAAATTGCC-1 4.28509712219238 -8.38352298736572 NK cells -Sample_4_GGAATAACAGTTCATG-1 -2.45329236984253 8.85737133026123 CD8+ T-cells -Sample_4_GGACATTCACTTCGAA-1 2.99975943565369 -9.64812660217285 NK cells -Sample_4_GGAGCAACAAGCGCTC-1 2.59820199012756 6.5576343536377 CD8+ Tem -Sample_4_GGAGCAACATCTCCCA-1 -0.0336346812546253 6.35661029815674 CD4+ Tem -Sample_4_GGAGCAATCGCATGGC-1 -0.439368009567261 -7.0039529800415 NK cells -Sample_4_GGAGCAATCGTAGATC-1 -0.202099934220314 -11.1647386550903 NK cells -Sample_4_GGATGTTAGTCCGTAT-1 -2.57144355773926 8.1316967010498 CD4+ T-cells -Sample_4_GGATTACCATTGAGCT-1 0.136021092534065 -9.53519821166992 NK cells -Sample_4_GGATTACTCACCAGGC-1 -0.0264776572585106 5.1288480758667 CD8+ Tem -Sample_4_GGCAATTAGAGGTAGA-1 3.19847536087036 4.46668100357056 CD8+ Tcm -Sample_4_GGCAATTAGTGTTGAA-1 -1.66317391395569 1.38391137123108 CD8+ Tem -Sample_4_GGCAATTCAGATTGCT-1 -0.536256670951843 3.78329348564148 CD8+ Tcm -Sample_4_GGCAATTGTCAAACTC-1 1.06483554840088 -9.30867767333984 NK cells -Sample_4_GGCAATTGTGATAAGT-1 4.52600193023682 4.40410566329956 CD8+ Tem -Sample_4_GGCCGATGTTTCCACC-1 2.55496287345886 -10.5852136611938 NK cells -Sample_4_GGCGACTCATCCTAGA-1 0.921071350574493 -8.94488143920898 NK cells -Sample_4_GGCGACTCATCCTTGC-1 -0.113928690552711 6.72214889526367 CD4+ Tcm -Sample_4_GGCGTGTCACATGACT-1 1.04193603992462 -9.52906322479248 NK cells -Sample_4_GGGAATGAGATGGCGT-1 -2.07443404197693 8.92208099365234 CD8+ T-cells -Sample_4_GGGAATGGTCATCCCT-1 -1.16411626338959 8.04463863372803 CD8+ Tcm -Sample_4_GGGACCTTCTTGGGTA-1 -0.335170537233353 -0.107734657824039 NK cells -Sample_4_GGGAGATAGTGTTTGC-1 -0.548690497875214 8.77071762084961 CD8+ T-cells -Sample_4_GGGAGATCAGATGGCA-1 0.170355260372162 4.6270956993103 CD8+ Tcm -Sample_4_GGGAGATCATACCATG-1 -1.58050084114075 7.70808506011963 CD8+ T-cells -Sample_4_GGGAGATGTCAAACTC-1 4.46578645706177 -9.69121074676514 NK cells -Sample_4_GGGATGAGTCGTCTTC-1 3.20505475997925 6.23378133773804 CD8+ Tem -Sample_4_GGGATGATCAGTTCGA-1 1.5357780456543 6.34644269943237 CD4+ Tem -Sample_4_GGGCACTAGCAGGTCA-1 3.95869493484497 -8.9282808303833 NK cells -Sample_4_GGGCACTCAAAGCAAT-1 -0.971709907054901 3.7784104347229 CD8+ Tcm -Sample_4_GGGCACTTCCTAGGGC-1 -1.39314639568329 8.38517284393311 CD8+ T-cells -Sample_4_GGGCACTTCTATCGCC-1 0.747305631637573 -9.23353481292725 NK cells -Sample_4_GGGCATCCACGGCGTT-1 0.18043614923954 -9.09004497528076 NK cells -Sample_4_GGGCATCCATCACAAC-1 0.146525129675865 3.4598753452301 CD8+ Tcm -Sample_4_GGGCATCGTTCCTCCA-1 4.93719673156738 6.10484170913696 CD8+ Tem -Sample_4_GGGCATCTCCGCATCT-1 2.24211454391479 6.00154256820679 CD8+ Tem -Sample_4_GGTATTGGTGAGGGTT-1 -1.33870267868042 6.66053485870361 CD8+ T-cells -Sample_4_GGTATTGGTTCCCGAG-1 0.0838364586234093 4.14972019195557 CD8+ Tcm -Sample_4_GGTGAAGAGAGGTAGA-1 0.845558822154999 1.97352039813995 CD8+ Tcm -Sample_4_GGTGAAGTCCATGAGT-1 0.551440060138702 6.90953397750854 CD8+ Tem -Sample_4_GGTGAAGTCCTCATTA-1 2.13707089424133 -8.1862325668335 NK cells -Sample_4_GGTGCGTAGAGTACAT-1 1.35472738742828 -10.6533288955688 NK cells -Sample_4_GGTGCGTAGTACATGA-1 -0.0204605031758547 5.67822170257568 CD8+ Tcm -Sample_4_GGTGTTAGTAGTAGTA-1 -0.917377471923828 0.193556562066078 CD8+ Tem -Sample_4_GTAACGTAGGACAGCT-1 2.43856263160706 5.90525007247925 CD8+ Tem -Sample_4_GTAACGTTCACTTACT-1 2.50395035743713 5.04548740386963 CD8+ Tcm -Sample_4_GTAACTGTCCGAACGC-1 -0.584291279315948 -7.26534557342529 NK cells -Sample_4_GTACGTAAGACACTAA-1 -0.799187779426575 -6.87278032302856 CD8+ Tem -Sample_4_GTACGTACATGCTGGC-1 3.2635440826416 5.23559284210205 CD8+ Tem -Sample_4_GTACGTATCCTGTAGA-1 -2.88656187057495 8.48159027099609 CD4+ T-cells -Sample_4_GTACTTTGTGTCAATC-1 0.34433051943779 8.4898681640625 CD8+ Tem -Sample_4_GTAGGCCAGCGCTCCA-1 2.61811876296997 7.78613138198853 CD8+ Tem -Sample_4_GTAGGCCGTAACGTTC-1 0.250970870256424 -9.64937686920166 NK cells -Sample_4_GTATCTTTCCTATGTT-1 2.28350973129272 7.14672803878784 CD8+ Tem -Sample_4_GTATTCTCAGCTGCTG-1 2.91013836860657 6.39682960510254 CD8+ Tem -Sample_4_GTATTCTGTCCCTACT-1 0.851823031902313 2.56911420822144 CD8+ Tcm -Sample_4_GTATTCTTCCAATGGT-1 3.60863018035889 6.21348762512207 CD8+ Tem -Sample_4_GTCACAAGTCCGAAGA-1 -1.28522157669067 8.54194831848145 CD8+ T-cells -Sample_4_GTCACAAGTTCCACAA-1 -0.0721996501088142 -8.38572883605957 NK cells -Sample_4_GTCACGGCACCGGAAA-1 -2.00060367584229 8.01577186584473 CD8+ T-cells -Sample_4_GTCACGGGTTGCCTCT-1 4.92973470687866 6.07403612136841 CD8+ Tem -Sample_4_GTCACGGTCAAGGCTT-1 -1.67163908481598 1.37842273712158 CD8+ Tcm -Sample_4_GTCATTTAGCTTTGGT-1 1.09552788734436 -10.0902194976807 NK cells -Sample_4_GTCATTTTCAGTTTGG-1 2.96609401702881 6.45203733444214 CD8+ Tem -Sample_4_GTCCTCAAGTATCGAA-1 -1.4478896856308 9.45058059692383 CD8+ T-cells -Sample_4_GTCCTCAAGTGCCATT-1 4.51319742202759 5.84762859344482 CD8+ Tem -Sample_4_GTCGGGTGTCCTCTTG-1 3.55742073059082 7.83484125137329 CD8+ Tem -Sample_4_GTCGTAATCCATGAGT-1 0.585071384906769 3.36078572273254 CD8+ Tcm -Sample_4_GTCTCGTAGTTCGCAT-1 -2.14251923561096 8.80745220184326 CD8+ T-cells -Sample_4_GTCTTCGGTATAAACG-1 3.39027881622314 3.55169010162354 CD8+ Tem -Sample_4_GTGAAGGAGTACGACG-1 1.56009018421173 -9.32473182678223 NK cells -Sample_4_GTGAAGGCAATAACGA-1 1.55118072032928 -8.4436206817627 NK cells -Sample_4_GTGAAGGCATGTTGAC-1 3.53449463844299 6.94433641433716 CD8+ Tem -Sample_4_GTGAAGGGTCCTCTTG-1 0.738539397716522 4.61432361602783 CD8+ Tcm -Sample_4_GTGAAGGTCATGTAGC-1 1.77418756484985 -5.24987745285034 NK cells -Sample_4_GTGCAGCAGGCTACGA-1 4.86850214004517 4.44997310638428 CD8+ Tcm -Sample_4_GTGCAGCTCAAAGACA-1 -2.06569147109985 7.60121011734009 CD8+ T-cells -Sample_4_GTGCATACATCCGGGT-1 2.64701294898987 -9.94343280792236 NK cells -Sample_4_GTGCATATCACCGTAA-1 1.5187873840332 -7.03052091598511 NK cells -Sample_4_GTGCATATCATTATCC-1 2.2845983505249 -9.22703075408936 NK cells -Sample_4_GTGCGGTAGAGGTACC-1 3.43324160575867 7.60318183898926 CD8+ Tem -Sample_4_GTGCGGTCAAGCCGTC-1 0.292840480804443 -8.64549160003662 NK cells -Sample_4_GTGCGGTCACATTCGA-1 -2.3158552646637 8.16868591308594 CD8+ T-cells -Sample_4_GTGCTTCAGCCAACAG-1 2.07118368148804 -7.51876640319824 NK cells -Sample_4_GTGGGTCTCAAGAAGT-1 0.222942650318146 1.21270966529846 NK cells -Sample_4_GTGTTAGCATCCTAGA-1 2.85655403137207 -9.55843448638916 NK cells -Sample_4_GTGTTAGGTAGATTAG-1 2.79255104064941 6.22996044158936 CD8+ Tem -Sample_4_GTTACAGCATTAGGCT-1 -1.99654519557953 8.27771663665771 CD8+ Tcm -Sample_4_GTTCATTCATGCATGT-1 3.09515738487244 -10.7979593276978 NK cells -Sample_4_GTTCATTTCCAAACAC-1 -0.84246289730072 7.89794683456421 CD4+ T-cells -Sample_4_GTTCTCGTCTAGCACA-1 2.39494609832764 -9.791090965271 NK cells -Sample_4_GTTTCTAAGATTACCC-1 -0.919292807579041 4.48605394363403 CD8+ Tcm -Sample_4_GTTTCTAAGGTGCAAC-1 -1.00798451900482 6.93204879760742 CD8+ Tcm -Sample_4_GTTTCTACATCACGAT-1 -1.85675251483917 9.05569267272949 CD8+ T-cells -Sample_4_TAAACCGAGTTCGATC-1 4.19866371154785 -8.50193119049072 NK cells -Sample_4_TAAACCGGTCTGCGGT-1 2.30050086975098 6.72130680084229 CD8+ Tem -Sample_4_TAAACCGGTTCGCGAC-1 4.2095832824707 5.65017557144165 CD8+ Tem -Sample_4_TAAGAGAGTACTTAGC-1 -0.11424458026886 -9.08722400665283 NK cells -Sample_4_TAAGAGATCCGAAGAG-1 -1.82684791088104 9.10421085357666 CD8+ Tcm -Sample_4_TAAGCGTAGGACTGGT-1 0.920004725456238 -7.46937894821167 NK cells -Sample_4_TAAGTGCCAGACAAAT-1 3.17179274559021 -10.8727874755859 NK cells -Sample_4_TAAGTGCTCGCCATAA-1 1.04929757118225 -8.90903568267822 NK cells -Sample_4_TACACGAGTCACTTCC-1 3.08858442306519 -10.2619571685791 NK cells -Sample_4_TACACGAGTTTCGCTC-1 -3.60197019577026 8.94740962982178 CD8+ Tcm -Sample_4_TACACGATCCCAGGTG-1 0.514220774173737 2.18630695343018 CD8+ Tem -Sample_4_TACCTATGTCCAACTA-1 1.84309017658234 8.06527042388916 CD8+ Tcm -Sample_4_TACCTTAAGCTAACAA-1 2.09974789619446 -10.1599760055542 NK cells -Sample_4_TACCTTATCTGGCGTG-1 3.48149061203003 -7.41972875595093 NK cells -Sample_4_TACGGGCCATCCTAGA-1 -0.844205915927887 6.71220302581787 CD8+ Tcm -Sample_4_TACGGGCCATTACCTT-1 -0.839768171310425 3.0155553817749 CD8+ Tcm -Sample_4_TACGGTAAGGATGGAA-1 2.46150755882263 6.47106790542603 CD8+ Tem -Sample_4_TACTCATTCGGCCGAT-1 -1.00912308692932 2.30642175674438 CD8+ Tem -Sample_4_TACTCGCCAACTGCGC-1 0.13128200173378 -9.45115852355957 NK cells -Sample_4_TACTCGCTCCGAATGT-1 -1.00198912620544 9.41030693054199 CD8+ T-cells -Sample_4_TACTCGCTCGTAGGTT-1 -0.959559381008148 8.21357917785645 CD8+ T-cells -Sample_4_TACTTACTCCGTTGTC-1 -0.362355411052704 -10.3800296783447 NK cells -Sample_4_TACTTGTGTACTTAGC-1 0.670386970043182 -8.56776523590088 NK cells -Sample_4_TAGCCGGCAATGTTGC-1 2.16743731498718 6.43622064590454 CD8+ Tem -Sample_4_TAGCCGGTCGAGAACG-1 0.547460794448853 2.6642107963562 CD8+ Tcm -Sample_4_TAGGCATCAGTCCTTC-1 -0.145308628678322 5.16295385360718 Tregs -Sample_4_TAGGCATGTTGTACAC-1 -1.31761491298676 9.72503471374512 CD8+ Tcm -Sample_4_TAGTGGTCAAGGGTCA-1 1.92702996730804 -8.72838306427002 NK cells -Sample_4_TAGTGGTTCAATACCG-1 -2.47714757919312 7.55079889297485 CD4+ T-cells -Sample_4_TAGTGGTTCCTAGTGA-1 4.05702829360962 6.43235635757446 CD8+ Tem -Sample_4_TAGTTGGAGTAGGCCA-1 1.99177372455597 -9.35480880737305 NK cells -Sample_4_TATCAGGCAACTGCTA-1 2.43378567695618 -7.68731164932251 NK cells -Sample_4_TATCAGGGTCGGATCC-1 2.36107325553894 5.75515699386597 CD8+ Tem -Sample_4_TATCTCAAGACTTGAA-1 2.91889572143555 -8.28905010223389 NK cells -Sample_4_TATCTCAAGGGAGTAA-1 0.480999857187271 -9.71246814727783 NK cells -Sample_4_TATCTCAAGGTCATCT-1 -0.017389003187418 4.13004684448242 CD8+ Tcm -Sample_4_TATCTCAGTCTACCTC-1 0.0704030841588974 4.35082292556763 CD8+ Tcm -Sample_4_TATCTCATCATGTAGC-1 3.93963956832886 7.08222341537476 CD8+ Tcm -Sample_4_TATGCCCCAAGCTGTT-1 4.16375589370728 5.55267763137817 CD8+ Tem -Sample_4_TATGCCCCAATCACAC-1 -1.8670300245285 8.80719566345215 CD8+ T-cells -Sample_4_TATTACCCAAACCCAT-1 -0.482553839683533 9.69484043121338 CD8+ Tcm -Sample_4_TATTACCCACGAAAGC-1 -3.17549705505371 8.19273090362549 CD8+ T-cells -Sample_4_TATTACCGTGATGTGG-1 -0.746126651763916 -10.4878740310669 NK cells -Sample_4_TCAACGACAATCTACG-1 0.665214478969574 -9.13584232330322 NK cells -Sample_4_TCAACGATCCTTTCGG-1 -0.415875136852264 1.52327859401703 CD8+ Tem -Sample_4_TCAATCTGTGCAGACA-1 -0.770709633827209 0.933943748474121 CD8+ Tcm -Sample_4_TCAATCTGTGCGATAG-1 3.40839815139771 7.42054653167725 CD4+ Tem -Sample_4_TCACAAGCAATCGAAA-1 2.91567301750183 -9.90912055969238 NK cells -Sample_4_TCACAAGGTCTCATCC-1 -0.871197760105133 -9.28185272216797 NK cells -Sample_4_TCACGAACATACTACG-1 -0.820149719715118 0.495873957872391 CD8+ Tem -Sample_4_TCACGAAGTAGAAAGG-1 0.0441069416701794 1.09257328510284 CD8+ Tem -Sample_4_TCACGAATCATCGATG-1 2.9005286693573 5.38338279724121 CD8+ Tem -Sample_4_TCACGAATCGTCACGG-1 3.10099363327026 5.36832809448242 CD8+ Tcm -Sample_4_TCAGATGAGTTGAGTA-1 -0.751651346683502 7.00010061264038 CD8+ Tcm -Sample_4_TCAGCAAGTGCTTCTC-1 4.4470009803772 6.65266704559326 CD8+ Tem -Sample_4_TCAGCTCAGACAGGCT-1 0.458611339330673 2.99758625030518 CD8+ Tem -Sample_4_TCAGCTCGTCGAGTTT-1 2.03235292434692 7.38896465301514 CD8+ Tem -Sample_4_TCAGGATTCATTTGGG-1 3.29937410354614 5.87556791305542 CD8+ Tem -Sample_4_TCAGGTAAGTCCGGTC-1 -1.0563530921936 3.57738852500916 CD8+ Tem -Sample_4_TCAGGTACAGGATTGG-1 -1.21626663208008 0.924502432346344 NK cells -Sample_4_TCAGGTAGTCCGTCAG-1 1.92283749580383 -6.73725891113281 NK cells -Sample_4_TCAGGTATCGAGGTAG-1 2.98783874511719 -9.52119827270508 NK cells -Sample_4_TCAGGTATCGCCTGTT-1 0.255892157554626 5.87352418899536 CD8+ Tcm -Sample_4_TCATTACCAGTATAAG-1 3.66268086433411 -9.30105972290039 NK cells -Sample_4_TCATTTGAGGCAATTA-1 3.78855443000793 7.51539611816406 CD8+ Tcm -Sample_4_TCATTTGAGTGGTAGC-1 -1.83909428119659 0.619179964065552 CD8+ Tem -Sample_4_TCATTTGGTGTTGAGG-1 -0.250400215387344 0.497301280498505 NK cells -Sample_4_TCCACACAGTACACCT-1 -1.77428078651428 7.45681715011597 CD8+ T-cells -Sample_4_TCCACACCAGTTTACG-1 1.18594563007355 -10.3424205780029 NK cells -Sample_4_TCCCGATAGACAGGCT-1 -1.27544140815735 8.54353523254395 CD8+ Tcm -Sample_4_TCCCGATCAAGTTCTG-1 1.33855617046356 -8.05630970001221 NK cells -Sample_4_TCCCGATTCGTAGATC-1 -0.443473815917969 6.76876735687256 CD8+ Tcm -Sample_4_TCGAGGCGTAGAAGGA-1 -1.79598498344421 5.72872543334961 CD8+ Tcm -Sample_4_TCGAGGCTCCTCAACC-1 3.23569369316101 4.53603506088257 CD8+ Tem -Sample_4_TCGAGGCTCGTTGACA-1 2.65242218971252 -9.13494110107422 NK cells -Sample_4_TCGCGAGGTCAGTGGA-1 0.728972792625427 2.68103456497192 CD8+ Tem -Sample_4_TCGGGACCAGACGTAG-1 2.38268089294434 7.41542291641235 CD8+ Tem -Sample_4_TCGGGACGTGATAAGT-1 0.489630103111267 -8.87857818603516 NK cells -Sample_4_TCGGGACTCACCCGAG-1 -1.36291360855103 7.99363327026367 CD8+ Tcm -Sample_4_TCGGGACTCCGAAGAG-1 0.903656601905823 -9.68252086639404 NK cells -Sample_4_TCGGTAACACAGGCCT-1 2.85324573516846 5.64202499389648 CD8+ Tcm -Sample_4_TCGGTAACATTAACCG-1 2.65202403068542 -7.35500621795654 NK cells -Sample_4_TCGTACCAGATCTGCT-1 2.60163307189941 5.32825088500977 CD8+ Tem -Sample_4_TCGTACCGTTTGACAC-1 0.968153119087219 -9.37406063079834 NK cells -Sample_4_TCGTACCTCAAACCGT-1 2.59955477714539 -6.90439701080322 NK cells -Sample_4_TCGTACCTCCCGACTT-1 4.00438213348389 5.02660799026489 CD8+ Tem -Sample_4_TCGTAGACATCTATGG-1 2.30730247497559 -9.23996448516846 NK cells -Sample_4_TCTATTGAGGCGACAT-1 2.88637137413025 7.49854755401611 CD8+ Tem -Sample_4_TCTCATAAGACTTTCG-1 -0.209001183509827 0.405786663293839 CD8+ Tem -Sample_4_TCTCTAATCCGTAGTA-1 0.27355620265007 3.34794640541077 CD8+ Tem -Sample_4_TCTGAGAAGAGTGAGA-1 0.350656270980835 -10.630410194397 NK cells -Sample_4_TCTGAGACACCACGTG-1 -0.399697095155716 0.553774416446686 CD8+ Tem -Sample_4_TCTGAGACAGATCTGT-1 2.12117457389832 4.87308359146118 CD8+ Tem -Sample_4_TCTGGAAAGTACGTTC-1 2.51449203491211 7.88939237594604 CD8+ Tem -Sample_4_TCTTCGGAGGGATGGG-1 3.14019060134888 7.71518182754517 CD8+ Tem -Sample_4_TCTTTCCAGACTGGGT-1 2.96888399124146 -9.07755470275879 NK cells -Sample_4_TCTTTCCGTGCTTCTC-1 4.1909499168396 5.40419483184814 CD8+ Tem -Sample_4_TGAAAGATCCCTAATT-1 0.470678865909576 -11.4633293151855 NK cells -Sample_4_TGAAAGATCGTTGCCT-1 -1.74231564998627 0.758616387844086 CD8+ Tem -Sample_4_TGACAACTCCTACAGA-1 3.92455816268921 -9.49243545532227 NK cells -Sample_4_TGACTTTCAATGGATA-1 0.0692679584026337 2.24117708206177 CD8+ Tcm -Sample_4_TGACTTTTCTAGCACA-1 4.48354244232178 6.19219303131104 CD8+ Tem -Sample_4_TGAGAGGAGCAATCTC-1 3.33779454231262 -10.1234188079834 NK cells -Sample_4_TGAGCATGTCAAAGCG-1 -0.673156976699829 4.23046112060547 CD8+ Tcm -Sample_4_TGAGCCGGTGCCTTGG-1 0.362415611743927 -11.2780523300171 NK cells -Sample_4_TGAGGGAAGCATGGCA-1 -2.13364672660828 6.72815418243408 CD8+ T-cells -Sample_4_TGAGGGACATGGGAAC-1 -0.504172325134277 4.0880560874939 CD8+ Tcm -Sample_4_TGAGGGATCACATGCA-1 3.77803564071655 5.18264055252075 CD8+ Tem -Sample_4_TGATTTCTCCTTCAAT-1 1.60831272602081 -8.37379264831543 NK cells -Sample_4_TGCACCTAGGGCACTA-1 2.06760120391846 -9.67107200622559 NK cells -Sample_4_TGCCCATCAGTAAGCG-1 2.64246010780334 4.86371421813965 CD8+ Tcm -Sample_4_TGCCCTAGTTCAACCA-1 0.515762507915497 -8.25684452056885 NK cells -Sample_4_TGCCCTATCAGGCAAG-1 -0.16435644030571 8.28202247619629 CD8+ Tcm -Sample_4_TGCGGGTAGACCTAGG-1 3.29564356803894 5.8372688293457 CD8+ Tem -Sample_4_TGCGGGTGTTATGTGC-1 1.85008931159973 -4.5346508026123 NK cells -Sample_4_TGCGTGGGTAATTGGA-1 2.53721952438354 -7.9424901008606 NK cells -Sample_4_TGCTACCAGCTCCCAG-1 -0.325230658054352 9.47831344604492 CD8+ T-cells -Sample_4_TGCTGCTGTCCAACTA-1 -0.131751537322998 5.76944589614868 CD8+ Tcm -Sample_4_TGGACGCAGGAGCGAG-1 -1.31168878078461 5.5460319519043 CD8+ Tcm -Sample_4_TGGACGCGTAACGCGA-1 3.54717874526978 7.46514654159546 CD8+ Tem -Sample_4_TGGCGCAAGTACGCGA-1 3.24612593650818 6.42028093338013 CD8+ Tem -Sample_4_TGGCGCAGTTCCCGAG-1 0.465166866779327 -8.14164638519287 NK cells -Sample_4_TGGCGCATCCTACAGA-1 3.66203498840332 3.68850636482239 CD8+ Tem -Sample_4_TGGGAAGAGGAACTGC-1 -2.82387828826904 8.82495975494385 CD8+ T-cells -Sample_4_TGGGAAGTCTATCCTA-1 -1.11498761177063 -8.13706016540527 NK cells -Sample_4_TGGGCGTCAGTTAACC-1 2.32784938812256 8.0306453704834 CD8+ Tem -Sample_4_TGGTTAGAGCGCCTTG-1 2.07264256477356 -9.16225624084473 NK cells -Sample_4_TGGTTAGAGGAATTAC-1 0.203374922275543 3.18812251091003 CD8+ Tem -Sample_4_TGGTTAGGTCAATACC-1 1.95705592632294 -7.06457042694092 NK cells -Sample_4_TGGTTCCTCCACTCCA-1 0.095970444381237 5.62136697769165 CD8+ Tcm -Sample_4_TGTATTCAGATGTCGG-1 0.309991002082825 4.9100193977356 CD8+ Tcm -Sample_4_TGTATTCCAGCGTCCA-1 2.39181566238403 -8.9264554977417 NK cells -Sample_4_TGTATTCTCTGATACG-1 2.76540899276733 -9.35356140136719 NK cells -Sample_4_TGTCCCAAGAGACTAT-1 4.32136631011963 -9.61208057403564 NK cells -Sample_4_TGTCCCATCTTGTACT-1 2.46080851554871 -10.1187019348145 NK cells -Sample_4_TGTGGTACATGCAACT-1 -1.83528137207031 9.07018280029297 CD4+ T-cells -Sample_4_TGTGGTAGTGCATCTA-1 -1.335524559021 0.265484571456909 CD8+ Tem -Sample_4_TGTGGTATCCCGACTT-1 4.01675748825073 4.8514666557312 CD8+ Tem -Sample_4_TGTGTTTAGGCCCTCA-1 4.44736909866333 -8.74761486053467 NK cells -Sample_4_TGTGTTTGTTGTGGAG-1 1.02691054344177 2.78939437866211 CD8+ Tem -Sample_4_TGTGTTTTCATGCTCC-1 3.97195076942444 6.94306755065918 CD8+ Tem -Sample_4_TGTTCCGAGCCACCTG-1 1.91381812095642 -7.61367893218994 NK cells -Sample_4_TGTTCCGCAGTGACAG-1 2.26311993598938 -4.78206968307495 CD8+ Tcm -Sample_4_TTAGGACAGGGTCGAT-1 -1.65904605388641 1.26755845546722 CD8+ Tem -Sample_4_TTAGGACGTGGTACAG-1 2.66826295852661 6.35769081115723 CD8+ T-cells -Sample_4_TTAGGCACATAAAGGT-1 3.94447326660156 7.88790941238403 CD8+ Tcm -Sample_4_TTAGTTCTCACTTACT-1 2.73116230964661 4.99942207336426 CD8+ Tem -Sample_4_TTAGTTCTCTTGAGAC-1 3.97892212867737 5.81872844696045 CD8+ Tem -Sample_4_TTATGCTTCACGAAGG-1 -0.802635133266449 9.03095722198486 CD8+ T-cells -Sample_4_TTCGGTCAGCCAGAAC-1 2.7600417137146 -10.9972047805786 NK cells -Sample_4_TTCGGTCAGTTCCACA-1 -9.81457042694092 -2.91104578971863 naive B-cells -Sample_4_TTCTACACATTACCTT-1 -0.844274461269379 0.622758090496063 NK cells -Sample_4_TTCTACATCGCAAGCC-1 3.33834671974182 4.66835880279541 CD8+ Tem -Sample_4_TTCTCAACACACTGCG-1 -0.508517324924469 4.94144010543823 CD8+ Tem -Sample_4_TTCTCCTGTTCCCGAG-1 2.48332285881042 -10.8556251525879 NK cells -Sample_4_TTCTCCTTCCGAATGT-1 2.6969051361084 5.8803653717041 CD8+ Tem -Sample_4_TTCTTAGGTTTAGGAA-1 1.65020656585693 2.23946690559387 CD8+ Tem -Sample_4_TTGAACGAGCCATCGC-1 1.95342314243317 -8.48818969726562 NK cells -Sample_4_TTGAACGGTAGCTCCG-1 1.78384149074554 3.5133900642395 CD8+ Tcm -Sample_4_TTGAACGGTCTCAACA-1 0.147097870707512 0.238351210951805 CD8+ Tem -Sample_4_TTGAACGTCAGTTCGA-1 -0.660673975944519 8.70661067962646 CD4+ T-cells -Sample_4_TTGCCGTCACCTTGTC-1 4.3368935585022 -8.76253604888916 NK cells -Sample_4_TTGCCGTTCTTGCAAG-1 2.39001798629761 5.41715240478516 CD8+ Tcm -Sample_4_TTGCGTCCAGCCTTGG-1 1.30715334415436 3.275723695755 CD8+ Tem -Sample_4_TTGCGTCTCACGGTTA-1 -0.67403382062912 7.46691465377808 CD8+ Tcm -Sample_4_TTGCGTCTCCTATTCA-1 4.65778398513794 6.47557497024536 CD8+ Tem -Sample_4_TTGCGTCTCTTCTGGC-1 -1.45244705677032 5.65656280517578 CD8+ Tem -Sample_4_TTGGCAAAGAGATGAG-1 3.81781363487244 -8.77507019042969 NK cells -Sample_4_TTGGCAAGTATCACCA-1 4.60452604293823 5.25131034851074 CD8+ Tem -Sample_4_TTGTAGGAGTCGTACT-1 3.18819141387939 6.20803546905518 CD8+ Tem -Sample_4_TTTATGCAGCCACTAT-1 1.9394063949585 5.74754047393799 CD8+ Tem -Sample_4_TTTATGCCAGTTAACC-1 4.10701560974121 7.55610036849976 CD8+ Tem -Sample_4_TTTGCGCAGGCTACGA-1 4.01955270767212 -10.1364908218384 NK cells -Sample_4_TTTGCGCTCACTATTC-1 3.95524954795837 5.41893863677979 CD8+ Tem -Sample_4_TTTGGTTTCTGTTTGT-1 -0.497337251901627 0.13110388815403 CD8+ Tcm -Sample_4_TTTGTCACAATTGCTG-1 2.50592064857483 2.97795271873474 CD8+ Tem -Sample_4_TTTGTCAGTAGGAGTC-1 -9.51404285430908 -2.60254168510437 Class-switched memory B-cells -Sample_4_TTTGTCATCCTCATTA-1 0.464586406946182 -11.3375959396362 NK cells \ No newline at end of file diff --git a/sample_data_for_tsne/genelist.txt b/sample_data_for_tsne/genelist.txt deleted file mode 100644 index a3840007a..000000000 --- a/sample_data_for_tsne/genelist.txt +++ /dev/null @@ -1,19 +0,0 @@ -CD4 -CD8 -CD69 -IL2 -IL2RA -CTLA4 -PDCD1 -CD274 -CD28 -CD80 -CD47 -B2M -CD74 -ACTB -RPLP1 -GNLY -RPS27 -MALAT1 -EEF1A1 diff --git a/sample_data_for_tsne/genelist_old.txt b/sample_data_for_tsne/genelist_old.txt deleted file mode 100644 index f91776d51..000000000 --- a/sample_data_for_tsne/genelist_old.txt +++ /dev/null @@ -1,4 +0,0 @@ -SDF4 -AURKAIP1 -EFHD2 -CDC42 \ No newline at end of file diff --git a/sample_data_for_tsne/tsne.csv b/sample_data_for_tsne/tsne.csv deleted file mode 100644 index a7c8a7127..000000000 --- a/sample_data_for_tsne/tsne.csv +++ /dev/null @@ -1,3116 +0,0 @@ -"Sample_1_AAACCTGTCTGCTGTC",-6.44067843268116,-76.1761495360147 -"Sample_1_AAACGGGGTTTGCATG",2.56687207421171,75.401760809482 -"Sample_1_AAACGGGTCCTTTACA",101.917649720298,-36.7766714392109 -"Sample_1_AAAGTAGCAAACAACA",6.75300524022182,110.637895431027 -"Sample_1_AAAGTAGCATAGAAAC",7.63489492148682,96.7439382688952 -"Sample_1_AAATGCCCATGATCCA",100.716744594656,-18.6212488315983 -"Sample_1_AAATGCCGTTAGTGGG",30.2510305141091,94.8195175852085 -"Sample_1_AAATGCCTCAGTTAGC",95.4988462874679,-15.4519124099136 -"Sample_1_AACACGTGTCGCTTCT",104.425822517925,-12.5476954107485 -"Sample_1_AACCATGGTCTCACCT",16.5017337813797,36.0013219922758 -"Sample_1_AACTCAGGTACCGTAT",-105.920113087127,2.92314750060075 -"Sample_1_AACTCAGGTCCATCCT",-45.0600199749263,-31.1487478760408 -"Sample_1_AACTCAGGTCGCTTCT",97.1245909330912,-1.108022094523 -"Sample_1_AACTCAGTCCTTTCTC",-92.4040983135107,-1.06999267489519 -"Sample_1_AACTCTTCAAGTAATG",-53.133958505608,-12.200160903181 -"Sample_1_AACTCTTCACACATGT",-3.14783342680041,3.5140478135593 -"Sample_1_AACTCTTGTGCCTGTG",-3.63566912163882,78.8452531254566 -"Sample_1_AACTCTTTCAAACAAG",-94.633271858585,31.0357159751228 -"Sample_1_AACTCTTTCCTCGCAT",0.781786775761737,-68.3464182767807 -"Sample_1_AACTGGTGTGCGGTAA",96.9440830356165,-4.07932504391677 -"Sample_1_AACTTTCGTAGCGATG",-102.583075271014,41.0918122286835 -"Sample_1_AAGACCTAGATGTCGG",101.747710182405,-50.085703809905 -"Sample_1_AAGCCGCAGCTGAACG",-49.1011150750341,-14.6891731241903 -"Sample_1_AAGCCGCGTTCGCGAC",-13.3152919843663,-44.8739362835779 -"Sample_1_AAGGAGCCAATACGCT",79.8689025904984,22.8862920393777 -"Sample_1_AAGGCAGAGAGGTAGA",29.5413961864835,55.5181799014627 -"Sample_1_AAGGCAGAGGAATCGC",15.2465268852486,48.0223932052806 -"Sample_1_AAGGTTCGTCATATGC",6.84538277095346,117.853298113399 -"Sample_1_AAGGTTCTCAGTGCAT",102.702654117397,-7.67728297244466 -"Sample_1_AAGTCTGAGAGGTAGA",-29.7483576847312,-104.231590597245 -"Sample_1_AATCCAGCAAGCCCAC",-108.560429624501,-0.707971220534373 -"Sample_1_AATCCAGGTAAGCACG",-0.667472439167914,-52.0965893279758 -"Sample_1_AATCCAGTCACAACGT",68.4108534511594,-40.9827387609183 -"Sample_1_AATCGGTCATCACAAC",77.9409093220569,-55.1063243863394 -"Sample_1_ACACCCTCAGCTGTGC",-81.3465210977009,7.83755799267391 -"Sample_1_ACACCCTCATACCATG",-55.9395664489196,-24.9724204543176 -"Sample_1_ACACCCTGTCACAAGG",-31.4348981530424,-17.5944783721133 -"Sample_1_ACACTGAAGCTGAACG",19.0056626254758,47.9603233425809 -"Sample_1_ACACTGATCTTACCTA",-108.746442866482,-6.88580638565494 -"Sample_1_ACAGCCGAGCTACCGC",-38.9747518140368,-9.25254854273517 -"Sample_1_ACAGCTAAGGCAGTCA",-89.6164015514118,-26.820976319772 -"Sample_1_ACATACGAGAGAACAG",62.2614603529726,-31.9009473313802 -"Sample_1_ACATACGAGGCTAGAC",28.0638913185949,-43.2099265026963 -"Sample_1_ACATACGTCAACCAAC",-70.6579804635447,35.6333796821432 -"Sample_1_ACATCAGAGGTCGGAT",-66.0053310079064,-18.3725022975455 -"Sample_1_ACATCAGGTCCGTTAA",-71.925380655093,20.3623030831343 -"Sample_1_ACATGGTGTCGCTTCT",96.1677890510999,-52.5752699210397 -"Sample_1_ACCAGTACACAACTGT",54.5300014838584,21.7429979428856 -"Sample_1_ACCAGTATCCTCGCAT",93.9976621494361,-44.346129450027 -"Sample_1_ACCAGTATCTGAAAGA",-68.8773059535799,-17.6700442980553 -"Sample_1_ACCGTAACAAGAAGAG",22.6009780214662,119.034633097643 -"Sample_1_ACCGTAACATTGAGCT",29.8833944761598,10.2413959853094 -"Sample_1_ACCGTAATCGCTTGTC",96.6045692298895,-56.3870661041334 -"Sample_1_ACCTTTAAGCGATATA",25.1396451900837,99.8070691039689 -"Sample_1_ACGAGCCAGACCTAGG",43.4545905985102,103.058140082491 -"Sample_1_ACGAGCCGTCTAGTCA",-48.1134749289922,0.775862501318284 -"Sample_1_ACGATACAGAGACGAA",72.3546584236305,-47.4327561960241 -"Sample_1_ACGATACCAATACGCT",-67.4224973283128,29.1478899916845 -"Sample_1_ACGATACGTCTGCCAG",50.2401756171557,109.687831907036 -"Sample_1_ACGATGTAGAGCTTCT",65.0587809825768,-55.9458237550028 -"Sample_1_ACGATGTCATTCACTT",80.085740921923,-25.9067395427407 -"Sample_1_ACGATGTGTCATATCG",10.8121982119171,14.6662844636823 -"Sample_1_ACGCAGCCAAGCGCTC",78.7359703685389,-26.9772316343542 -"Sample_1_ACGCCGAAGGTGTTAA",-82.9998721349591,26.2220731074179 -"Sample_1_ACGGAGAAGACAGAGA",15.4466257706012,37.7700816714754 -"Sample_1_ACGGCCAGTTGATTCG",8.04154890616956,-66.9628152377672 -"Sample_1_ACGTCAAAGGTAGCTG",-104.129420643399,16.4754131596999 -"Sample_1_ACGTCAATCGGAGCAA",-82.1393235612457,42.341943619355 -"Sample_1_ACGTCAATCGGCATCG",94.6422879267801,-10.2566353113688 -"Sample_1_ACTATCTAGCTACCTA",-89.1289565705195,-11.6292248120277 -"Sample_1_ACTGAACGTCAATGTC",41.9840209578326,117.270074507788 -"Sample_1_ACTGAACTCCCAACGG",45.6961176031877,101.785397443798 -"Sample_1_ACTGCTCCATTAGCCA",-59.2876958289873,7.66860042533831 -"Sample_1_ACTGTCCAGAAGAAGC",61.6202970009611,-56.7819010551532 -"Sample_1_ACTGTCCGTACCGCTG",72.603918679701,-35.0640880675156 -"Sample_1_ACTTACTAGCCGGTAA",27.0545986710225,50.8115666702934 -"Sample_1_ACTTACTGTACCGGCT",-91.6313267331684,40.2118268881753 -"Sample_1_ACTTGTTCAGATGGCA",-68.4577261578919,-6.04835279233692 -"Sample_1_ACTTGTTTCACGATGT",-10.842844766348,-35.3471353203694 -"Sample_1_ACTTTCACACAGTCGC",45.997396418779,97.684089287066 -"Sample_1_ACTTTCAGTAAGGATT",63.9755730620313,-60.3260381325503 -"Sample_1_ACTTTCATCAGTTGAC",11.2362188831666,89.5585119708893 -"Sample_1_AGAATAGTCAACACCA",-42.2025905097481,-22.2146667850138 -"Sample_1_AGAGCGAAGGTCATCT",95.8742225326191,7.79751165914786 -"Sample_1_AGAGCGAGTACCGGCT",-99.8848319002732,15.2968693464527 -"Sample_1_AGAGCTTAGGAGTTTA",-95.1119405938818,2.77546889771852 -"Sample_1_AGAGCTTTCAAGAAGT",-52.5063342526696,-24.3539039226549 -"Sample_1_AGAGTGGTCCAGTAGT",21.4038112874529,83.547669271159 -"Sample_1_AGAGTGGTCCTTGACC",20.0190428724883,51.8182140595087 -"Sample_1_AGCAGCCAGTTCCACA",23.8986539661455,58.63045197499 -"Sample_1_AGCCTAACAGTCTTCC",-59.6810313297997,29.609695463042 -"Sample_1_AGCGTATAGTGCAAGC",97.74567519039,-49.8629070658763 -"Sample_1_AGCGTATGTGACGGTA",-68.3906818097862,24.3907955285668 -"Sample_1_AGCGTCGCAGCGAACA",82.3382936483338,2.94325390882417 -"Sample_1_AGCTCCTTCAGAGCTT",-104.346122130002,-48.6998850161984 -"Sample_1_AGCTCTCCATTTGCCC",-27.154408896672,-104.41285498048 -"Sample_1_AGCTTGAAGACTAGAT",-74.2981500511858,-0.433616071604702 -"Sample_1_AGCTTGAGTCTCACCT",13.3394170548997,111.772815639912 -"Sample_1_AGGCCACCAAGCGAGT",-29.4860099000674,-10.9675460288528 -"Sample_1_AGGCCACCAAGTAATG",-72.9486618497573,41.9914523073179 -"Sample_1_AGGCCACCACGGCTAC",22.0981474445154,-16.2232792368347 -"Sample_1_AGGCCACTCTCTTATG",-114.03478696831,-10.9057988504998 -"Sample_1_AGGCCGTGTAGAGGAA",49.7742287057627,-21.959650254277 -"Sample_1_AGGCCGTTCATCGATG",102.173739561388,-57.647199720447 -"Sample_1_AGGGATGGTCTCAACA",-10.2377400699649,-77.6078883454669 -"Sample_1_AGGGTGACAGTAAGAT",-94.5090666297216,26.0493308145961 -"Sample_1_AGGTCATCACAGACTT",-72.8416318072964,33.0712851699185 -"Sample_1_AGGTCATTCGAGAGCA",-84.2530817995047,-34.6984472864579 -"Sample_1_AGGTCCGCAGCTGTGC",-28.7647173513602,-110.635277433723 -"Sample_1_AGTAGTCTCACTCCTG",41.4778892114435,-3.9409798902875 -"Sample_1_AGTCTTTAGCCTCGTG",2.62580562739381,-29.7115167951036 -"Sample_1_AGTCTTTAGGATTCGG",10.4058248284391,-3.47913251935048 -"Sample_1_AGTGAGGAGGCTCTTA",-105.197910311555,-9.11475336462608 -"Sample_1_AGTGAGGGTGCACCAC",84.0144913148422,20.8076271585482 -"Sample_1_AGTGAGGTCTGATACG",18.184852573837,-7.90609081289749 -"Sample_1_AGTGTCATCCTTTCGG",4.86190471403917,-18.8182359370133 -"Sample_1_AGTGTCATCTGGGCCA",-73.2437937776936,-65.7016957234664 -"Sample_1_AGTGTCATCTTACCTA",-0.711409088831446,-72.3267372624314 -"Sample_1_AGTTGGTTCGTCCAGG",86.9896814319414,-74.8792946667643 -"Sample_1_ATAACGCAGTGGCACA",-32.3037611696776,-107.657549653157 -"Sample_1_ATAACGCCAGTCAGAG",2.01066625399435,-43.4037927613328 -"Sample_1_ATAACGCGTAGCTTGT",18.2674165042053,99.5986103569592 -"Sample_1_ATAAGAGGTGCCTGGT",-70.9208801938603,12.0844191155598 -"Sample_1_ATAGACCTCTTTACGT",-81.4289909345522,1.42669820313143 -"Sample_1_ATCACGAAGTGGAGTC",14.7291114489878,-1.9757862871007 -"Sample_1_ATCACGATCAGCAACT",8.94846616949433,94.3492519194252 -"Sample_1_ATCATCTCAACACGCC",8.39091937093806,94.0130918597828 -"Sample_1_ATCATGGAGGTGATAT",-94.286805119428,-45.7404104486978 -"Sample_1_ATCATGGCAGCCAGAA",-25.5312496713325,-179.486839801311 -"Sample_1_ATCCACCAGTAGATGT",-48.2696567151669,3.59189477609101 -"Sample_1_ATCCACCCATCGATGT",-23.2536997789321,-185.086548595472 -"Sample_1_ATCCACCGTAACGACG",27.0193164943869,-22.9826914564572 -"Sample_1_ATCCGAAAGAAGATTC",-40.5291369684451,-14.5247686372546 -"Sample_1_ATCCGAACACTAGTAC",-80.2856313278265,-44.737137818308 -"Sample_1_ATCCGAAGTACACCGC",-75.8547233434523,-13.0478627906843 -"Sample_1_ATCGAGTCACAACTGT",52.4190155024477,-19.6609596403158 -"Sample_1_ATCTACTCACGAGGTA",-101.651940499547,-35.4190791412841 -"Sample_1_ATCTACTCATGGGACA",33.7768061488208,50.8857999329371 -"Sample_1_ATCTACTTCGGCCGAT",36.5326363082981,74.5501031532974 -"Sample_1_ATCTGCCAGCGCTCCA",86.4528929086007,-17.6201536817205 -"Sample_1_ATCTGCCAGTGACATA",-86.9326062406568,35.1594956778125 -"Sample_1_ATCTGCCAGTTGAGTA",-85.8775000787356,-43.8886552558436 -"Sample_1_ATCTGCCCACTTAACG",58.950929721573,-16.606114282823 -"Sample_1_ATCTGCCCATCCCACT",-76.7364424494591,-15.9385107801556 -"Sample_1_ATCTGCCGTCTCTTTA",1.05320305261301,-4.27927033787165 -"Sample_1_ATCTGCCGTTAGTGGG",90.1153358990685,31.379767186995 -"Sample_1_ATCTGCCTCGCTTAGA",26.7249552104469,-1.55349762144933 -"Sample_1_ATGAGGGGTACCGTAT",3.61020265695802,-78.5693270712285 -"Sample_1_ATGCGATGTTCTGTTT",80.945472102427,-40.501523063192 -"Sample_1_ATGGGAGAGTTGTAGA",-54.5289366344847,-45.4364970781776 -"Sample_1_ATGTGTGAGCATCATC",-37.2524620729168,0.727402928934028 -"Sample_1_ATGTGTGAGGTCATCT",-102.583646440746,-15.6404187358272 -"Sample_1_ATGTGTGGTGTATGGG",59.4279900210566,-43.7357933222758 -"Sample_1_ATTACTCAGGTCATCT",-88.9595079013688,-51.7859879500922 -"Sample_1_ATTACTCGTGGAAAGA",-10.0331355750302,-39.3786241991799 -"Sample_1_ATTATCCAGTAGCGGT",-99.4523342580585,-41.1641766229367 -"Sample_1_ATTATCCCACCCATGG",99.1454103004416,-51.9709261690494 -"Sample_1_ATTCTACCAAGCGATG",15.1052661774388,1.88338591907821 -"Sample_1_ATTCTACCATTAACCG",55.6048624395233,3.69496692599974 -"Sample_1_ATTCTACGTCTAGTCA",-68.1512318399952,-25.774883561374 -"Sample_1_ATTCTACGTGGTAACG",5.33265975955595,-86.4178332124257 -"Sample_1_ATTGGACCAGCGTTCG",8.68705571497222,-75.8993955425359 -"Sample_1_ATTGGACCAGCTTCGG",24.2137416601002,-35.6573077362638 -"Sample_1_ATTGGACGTAGCGTGA",18.6184124437392,121.323553433773 -"Sample_1_ATTGGACGTCGCATCG",-34.2350594330333,13.8819784276449 -"Sample_1_ATTGGTGAGCCCGAAA",72.8690679028431,3.21854490484315 -"Sample_1_CAACCAATCCTTGGTC",-75.6691620011936,5.80966313240415 -"Sample_1_CAACTAGCATGGTAGG",-68.3239553781166,-28.4109569898787 -"Sample_1_CAACTAGTCGCATGGC",38.6946633284768,-32.8229913967001 -"Sample_1_CAAGAAAAGACTAAGT",-93.4089712101994,-37.0833411066074 -"Sample_1_CAAGAAAAGTTAGCGG",-44.175432091687,1.88114299120221 -"Sample_1_CAAGAAATCTATGTGG",-54.9200704220663,-4.90057173401656 -"Sample_1_CAAGATCGTTACTGAC",-1.24435554745616,-74.8551047662443 -"Sample_1_CAAGATCGTTCAACCA",-65.8818531900931,18.5151131135751 -"Sample_1_CAAGATCGTTCAGCGC",99.2027034036144,-19.7096423236187 -"Sample_1_CAAGATCGTTTAGGAA",18.4644366090961,109.877607154462 -"Sample_1_CAAGTTGGTAACGCGA",13.8932358124337,45.2161750019308 -"Sample_1_CACAAACAGTTCGATC",77.0482663760222,-35.4125721621207 -"Sample_1_CACACAACAATCACAC",29.2324204697468,110.279112276508 -"Sample_1_CACACAAGTCGCGTGT",45.5453848342133,52.1230447447306 -"Sample_1_CACACCTCAAACCTAC",-99.5811946118224,-52.6470771111182 -"Sample_1_CACACCTTCCCATTTA",-54.1968404603842,10.3385029686321 -"Sample_1_CACACTCCAGTACACT",-94.1420811995164,-28.8652251578471 -"Sample_1_CACACTCCATGTAAGA",49.3849344049821,118.361358068096 -"Sample_1_CACACTCTCTAACCGA",18.4628690417452,-42.0525428082993 -"Sample_1_CACAGGCGTGGGTATG",-95.2290417346808,-4.37633085238094 -"Sample_1_CACAGTAAGTGACATA",6.86703733554155,-73.7522160765316 -"Sample_1_CACATAGCAAGCCGCT",21.8049150844356,116.144289651181 -"Sample_1_CACATAGGTAGCAAAT",-84.7958888379223,-48.5692468321888 -"Sample_1_CACATAGTCGCGTTTC",19.910167633114,57.6931844350757 -"Sample_1_CACATTTCATTGCGGC",35.1619902790659,59.9308781672555 -"Sample_1_CACATTTTCACGAAGG",-33.5085271992461,-17.9551913185494 -"Sample_1_CACATTTTCCGTCATC",20.9248142454764,94.790272558094 -"Sample_1_CACCACTCACTCGACG",-12.9442546853694,-22.1367007250013 -"Sample_1_CACCACTTCATACGGT",-107.245021806231,-28.6580104433673 -"Sample_1_CACCACTTCCGCAAGC",-96.3479927598844,5.99904567878808 -"Sample_1_CACCACTTCTTGAGAC",-72.0375865133032,-19.7755087714293 -"Sample_1_CACCAGGCACCTCGTT",8.65697198541077,-87.5416957236263 -"Sample_1_CACCAGGTCAAGGTAA",-22.9542326956308,-39.4728780588307 -"Sample_1_CACCTTGTCTCTAAGG",48.6533911344712,21.4942119823579 -"Sample_1_CAGAGAGCATCACGTA",41.9501780992922,24.0476464770526 -"Sample_1_CAGATCAAGATATGGT",92.0006917999842,-57.8104449102509 -"Sample_1_CAGCAGCGTCGCCATG",89.2288866088545,33.9666705189005 -"Sample_1_CAGCAGCTCGGAATCT",-25.1781877396902,-180.172379114154 -"Sample_1_CAGCAGCTCTGTCCGT",98.460120811641,-46.8503465274886 -"Sample_1_CAGCCGAAGCGACGTA",-80.252245910216,-14.4023851781986 -"Sample_1_CAGCGACAGTAGCGGT",-62.4204719281611,-19.1677241825927 -"Sample_1_CAGCTGGAGTGTACGG",-101.807384132505,16.5146962140631 -"Sample_1_CAGCTGGTCTTTCCTC",-94.1256079118886,-7.3449337165314 -"Sample_1_CAGTAACCATCTCGCT",2.39084412649332,-26.9073450624418 -"Sample_1_CAGTAACTCTGTCCGT",43.8795299664222,89.8083817239305 -"Sample_1_CAGTCCTAGACAAGCC",-65.2769728213838,-38.5824214724259 -"Sample_1_CAGTCCTAGATCTGCT",15.944848526947,94.3937713398826 -"Sample_1_CAGTCCTAGTAGATGT",68.8797674938703,-53.1294254492291 -"Sample_1_CAGTCCTCAGGCGATA",108.805849572648,-15.6050599437167 -"Sample_1_CAGTCCTGTTCAGCGC",61.5456811865942,-38.8536187360517 -"Sample_1_CAGTCCTTCTCTTGAT",74.0706537676026,-50.7416318945981 -"Sample_1_CATATGGTCTGTGCAA",-28.4142880008032,-106.959858618551 -"Sample_1_CATATTCCAGTGACAG",18.0646098344017,54.0842684996561 -"Sample_1_CATATTCGTAAATACG",72.6819487164582,15.2331864285104 -"Sample_1_CATCAAGAGGTGTTAA",34.1564177629286,14.1501411440667 -"Sample_1_CATCAAGCACGACGAA",105.00500940655,-56.0519552789352 -"Sample_1_CATCAAGCAGGCAGTA",-78.0950828225283,21.8699854575576 -"Sample_1_CATCAAGCATGTTGAC",23.6214036686397,-13.0669417342342 -"Sample_1_CATCAAGGTCTCCACT",-78.0458450806905,36.3192383415331 -"Sample_1_CATCAGAAGAAACCAT",35.6912362299611,23.0032254915064 -"Sample_1_CATCAGACACCCTATC",106.647454798405,-23.0087107791001 -"Sample_1_CATCAGAGTCCATGAT",-105.513106681639,-14.5721498990926 -"Sample_1_CATCAGAGTCCGAAGA",2.05936720385753,-90.1592443193042 -"Sample_1_CATCCACGTCCCTTGT",30.3017691505562,18.9144916784843 -"Sample_1_CATCCACGTGCATCTA",37.5391974961984,50.6613695925975 -"Sample_1_CATCCACTCTGAGGGA",-88.8579259552092,13.5108847930589 -"Sample_1_CATCGAAAGGAATCGC",27.5979284414692,56.4807841553933 -"Sample_1_CATCGAACAGGCTGAA",1.86799125822231,103.070080594903 -"Sample_1_CATCGAACAGGGAGAG",99.5047940462687,-26.4074143435186 -"Sample_1_CATCGGGCAAAGGTGC",-68.7657369588581,-33.2084163006205 -"Sample_1_CATGACAAGTGAACGC",-12.992606802756,-38.7457302286425 -"Sample_1_CATGACACACAGACTT",-96.8408762798586,-2.34492636229087 -"Sample_1_CATGACACACCGTTGG",62.3497600757509,-53.2393842073424 -"Sample_1_CATGACACAGCTATTG",-88.1551285814677,32.7654266172187 -"Sample_1_CATGACAGTCTCACCT",64.9220037726509,-44.2570119058111 -"Sample_1_CATGCCTCAATGAAAC",-78.4547066077163,12.5362747781944 -"Sample_1_CATGGCGAGTGTGGCA",-55.8731718881121,-13.6600064837949 -"Sample_1_CATGGCGCACGAGGTA",-72.1640509704095,-16.0554206040636 -"Sample_1_CATGGCGGTACAGTGG",-94.7306762134272,-24.0343464750702 -"Sample_1_CATTATCAGGGTTCCC",-98.5747684081934,30.5245474419595 -"Sample_1_CATTATCCACAGCCCA",2.2004410427837,72.8091578677114 -"Sample_1_CATTATCGTGGTCTCG",91.9172392126225,-19.8097271687446 -"Sample_1_CCAATCCGTCTCTTAT",52.1977982319586,118.156645381478 -"Sample_1_CCAATCCTCTCATTCA",22.6749997933999,114.727360751398 -"Sample_1_CCACCTATCTTGCCGT",-114.980506559627,-47.8905586420292 -"Sample_1_CCACGGACAATCACAC",-80.2826280527516,27.9922515054176 -"Sample_1_CCACGGATCAGGCAAG",-26.3420144236746,-102.821101060123 -"Sample_1_CCACTACAGTGGACGT",51.1645091044886,-24.4347808404398 -"Sample_1_CCAGCGAGTTCGTTGA",69.6697087106234,-5.61822910887695 -"Sample_1_CCATTCGCAAGCCATT",20.0678497836469,82.1928219457813 -"Sample_1_CCCAATCTCAACACCA",91.9163175552906,-10.5942423795151 -"Sample_1_CCCTCCTAGTAGCCGA",-51.5638509850617,5.85298244011083 -"Sample_1_CCCTCCTGTTGTACAC",18.53142768554,117.41483946787 -"Sample_1_CCGGTAGAGTTACGGG",25.0989723385809,112.108768283117 -"Sample_1_CCGGTAGTCGCCTGAG",-90.8938254697322,-41.4603556567202 -"Sample_1_CCGTACTCAGGGTATG",93.9204509362415,-39.8328188990346 -"Sample_1_CCGTACTTCTACTATC",-50.1549691580644,13.8137356329159 -"Sample_1_CCGTGGAAGCTGAAAT",46.4593705605795,55.8244499066981 -"Sample_1_CCGTTCAAGCCCGAAA",-108.639880243686,-24.3778690337121 -"Sample_1_CCTAAAGAGCGATTCT",75.5017768814122,-30.1192751338025 -"Sample_1_CCTAAAGCAAGTAATG",90.5750635415002,-15.8839029157674 -"Sample_1_CCTACACAGCAAATCA",63.1120863363259,6.33839783733239 -"Sample_1_CCTACACTCTTTACGT",-96.4683383211622,37.5746859882782 -"Sample_1_CCTACCACATCGGAAG",-4.27156450266147,-55.6862746077932 -"Sample_1_CCTAGCTAGAGGTACC",7.54669606073318,113.116213922707 -"Sample_1_CCTATTATCAAACCAC",77.1726212342117,11.7549299713048 -"Sample_1_CCTCTGAGTAAGAGGA",-10.2119613042813,-66.046733565841 -"Sample_1_CCTCTGAGTTATTCTC",99.1260425764427,-28.215890197573 -"Sample_1_CCTCTGATCAGCCTAA",-95.2840539993532,-19.4453981017042 -"Sample_1_CCTTACGAGCACCGCT",-110.775313895976,12.3903101660341 -"Sample_1_CCTTACGGTCGCATCG",-61.5388553967481,-27.191130589977 -"Sample_1_CCTTCCCCAAACCTAC",51.7892702884258,110.783669237341 -"Sample_1_CCTTCCCTCACTGGGC",5.6504888212776,101.530894928054 -"Sample_1_CCTTCGAAGCGTTTAC",-62.9261683045177,16.6332689530744 -"Sample_1_CCTTCGACAAGAAGAG",96.8685743193505,-12.7884220354138 -"Sample_1_CGAACATAGTCAAGCG",-60.4614586420157,-22.3628373488761 -"Sample_1_CGAACATCATCAGTCA",67.3319662866425,-65.4665835876145 -"Sample_1_CGAATGTGTCATTAGC",86.6667201315923,28.8188881901917 -"Sample_1_CGACCTTAGCACCGCT",33.1442588172826,19.656879495963 -"Sample_1_CGACCTTAGTTGTAGA",-26.0634248916054,-185.187995336783 -"Sample_1_CGACCTTTCAAGCCTA",2.49962037746292,-60.7012854018199 -"Sample_1_CGACTTCTCATAGCAC",-85.9736924610429,14.0847673385783 -"Sample_1_CGAGCACAGACGCAAC",-112.526504508313,-4.25459175068363 -"Sample_1_CGAGCACAGCTCAACT",105.591402382537,-60.7021858407384 -"Sample_1_CGAGCACAGGCGTACA",0.0379458868383613,111.526022939837 -"Sample_1_CGAGCACTCTGCAAGT",8.43827831375089,104.724519858349 -"Sample_1_CGAGCCACATGCAACT",3.21524996849326,94.829752295574 -"Sample_1_CGAGCCATCGTGGTCG",24.4537820886943,4.79179278264564 -"Sample_1_CGATCGGAGACGCTTT",8.62994735885455,-62.7205139184715 -"Sample_1_CGATCGGGTCCCTTGT",0.0601875907387647,-47.7961117261597 -"Sample_1_CGATGGCTCGGTCCGA",50.1823683994207,114.573697303587 -"Sample_1_CGATGTAAGGACATTA",67.0218373796529,-20.3564129858488 -"Sample_1_CGATTGAGTGGTACAG",-51.5004369785451,-19.6109146608005 -"Sample_1_CGCCAAGAGACCACGA",9.88789783143709,-82.2607886811638 -"Sample_1_CGCCAAGGTGTCCTCT",-100.137821510775,43.6108308295607 -"Sample_1_CGCGGTAAGGCATGGT",-65.4840595381969,15.8097397610804 -"Sample_1_CGCGGTAAGTATCGAA",-113.596119471938,-7.1004278737735 -"Sample_1_CGCGGTACACAGGCCT",-51.0714186749499,-36.8734615618806 -"Sample_1_CGCGGTATCAACGCTA",-62.23988952009,-14.0642479156756 -"Sample_1_CGCGGTATCTCTGCTG",21.0124738795205,11.1606831766894 -"Sample_1_CGCGTTTTCCAGATCA",25.4699326445341,-34.1957968660794 -"Sample_1_CGCTATCCACTGTCGG",7.25839971581409,108.279575460569 -"Sample_1_CGCTATCGTGCAGACA",-101.538424264112,1.52036365968919 -"Sample_1_CGCTATCTCGCCATAA",-81.9601034068398,-20.4826375570332 -"Sample_1_CGCTATCTCTGTACGA",30.1485369260669,12.3828641703412 -"Sample_1_CGCTGGATCCTAGGGC",32.6268281005017,57.2961932363062 -"Sample_1_CGCTTCACATTTCAGG",71.3388161910151,-39.9140252361745 -"Sample_1_CGGACACAGCAGCGTA",24.1255135790624,81.3767621633338 -"Sample_1_CGGACACAGGTGCTTT",-76.2593239078334,-31.9268311196262 -"Sample_1_CGGACACTCCTCGCAT",1.59210091533319,-12.558437715484 -"Sample_1_CGGACGTTCAGGTTCA",73.5123788764046,-21.8721673531114 -"Sample_1_CGGACTGTCTCTGAGA",40.0218095350676,56.8290283139544 -"Sample_1_CGGAGCTCAAACTGTC",57.3863179634226,-54.5427697652133 -"Sample_1_CGGAGCTGTACCGTTA",77.073682491113,-0.107192094431491 -"Sample_1_CGGAGTCTCAGGCCCA",-82.5292165889657,-39.0144627333763 -"Sample_1_CGGCTAGCAATCTGCA",-40.1939904250412,-12.137876715487 -"Sample_1_CGTAGCGCAAACAACA",-52.6721013421538,-48.5583049096821 -"Sample_1_CGTAGCGTCAGCACAT",-67.4134125805507,-2.43783666061628 -"Sample_1_CGTAGCGTCCTAGAAC",-25.1472201045368,-108.529617496888 -"Sample_1_CGTAGGCGTCTGATTG",41.5115581030107,62.118206422445 -"Sample_1_CGTCAGGCAAGCTGTT",-88.9484573531666,-37.4937286881471 -"Sample_1_CGTCAGGTCAAGGTAA",13.0246474879702,81.3391587240135 -"Sample_1_CGTCCATAGCTATGCT",-84.2356262843751,-11.7991883841914 -"Sample_1_CGTCCATCACCCTATC",77.4395801980101,-37.6889685984447 -"Sample_1_CGTCTACCATAGGATA",32.0594068085602,107.849181455084 -"Sample_1_CGTGAGCCATTCTTAC",87.574497154086,18.8453994750794 -"Sample_1_CGTGAGCTCAATCTCT",-25.9253206880344,-181.422339443363 -"Sample_1_CGTTGGGTCAGAGACG",73.0155738753118,-1.23072973003315 -"Sample_1_CGTTGGGTCCTTGCCA",19.635493552422,36.196771436705 -"Sample_1_CTAACTTAGCCCAGCT",88.3231273182039,-29.6117041316754 -"Sample_1_CTAAGACAGTGTCTCA",-109.564101644073,-34.8895662995996 -"Sample_1_CTAAGACCAGATCGGA",89.3480051414753,22.5181725641465 -"Sample_1_CTAAGACGTCGGATCC",-23.8392843896633,-107.41618316483 -"Sample_1_CTAAGACTCCACTGGG",-17.7033230050972,-40.7371988198603 -"Sample_1_CTAATGGCACAGACTT",28.2631982879783,95.0403944978416 -"Sample_1_CTAATGGGTTCAGGCC",-74.825242547856,-16.6864055781686 -"Sample_1_CTACACCCAACGCACC",-105.989555869806,20.3976867259616 -"Sample_1_CTACACCGTTCAGCGC",-55.0270003797119,14.8551313902262 -"Sample_1_CTACACCTCGGTTCGG",65.9686978379732,-37.9310786757601 -"Sample_1_CTACATTCATGCCCGA",-91.6561352550967,17.6494205924067 -"Sample_1_CTACATTGTTTGGCGC",-63.855786409894,25.0899777175854 -"Sample_1_CTACCCAAGGTGCACA",-31.9479258244408,-98.6574863829699 -"Sample_1_CTACCCACACGACTCG",-54.8173471892449,-18.4291789355266 -"Sample_1_CTACCCACAGCGATCC",-43.1313103259049,-10.4302455823775 -"Sample_1_CTACGTCAGAGCTGCA",32.8289307340179,118.503824932828 -"Sample_1_CTACGTCAGGCGATAC",103.132970873363,-29.3708018529749 -"Sample_1_CTACGTCGTCTCAACA",-83.5326375374099,-7.70167737242312 -"Sample_1_CTACGTCTCGTCGTTC",-91.1394518072817,-19.7856451496853 -"Sample_1_CTAGAGTCAATCTACG",-100.80797771878,-4.7124864775369 -"Sample_1_CTAGAGTCAGGAATCG",2.88030768686631,-56.3455279315115 -"Sample_1_CTAGAGTTCAACTCTT",86.3827629341489,-30.9014201397919 -"Sample_1_CTAGTGACAGACGCTC",23.6429680265627,61.0162308604834 -"Sample_1_CTAGTGACAGCGTAAG",22.6306888288042,33.2200279306647 -"Sample_1_CTAGTGACATCTATGG",31.2523022880922,47.8873922051513 -"Sample_1_CTAGTGATCCTTGACC",44.9979217199802,104.180462508084 -"Sample_1_CTCACACTCACATACG",67.1033881382017,-25.340119415659 -"Sample_1_CTCAGAATCGAGGTAG",22.0630371829903,-38.7996208106743 -"Sample_1_CTCAGAATCGGAAATA",11.2607461183804,-60.7361152574052 -"Sample_1_CTCATTAAGTCGTTTG",41.3466872558302,68.3348188153762 -"Sample_1_CTCCTAGGTCATACTG",-35.8163643608322,-18.8150525016782 -"Sample_1_CTCCTAGTCGGCGCTA",14.2077394948381,-73.042259492618 -"Sample_1_CTCGAAAAGAACAATC",40.5469299863055,11.0748441270698 -"Sample_1_CTCGAAAAGACGACGT",4.36410534299475,-8.71779087332824 -"Sample_1_CTCGAAACACAGCGTC",-28.1846353714961,-8.75370388652317 -"Sample_1_CTCGGGAGTTCGGCAC",77.9701637874587,-19.6106535079069 -"Sample_1_CTCGTCACACACTGCG",-110.479165032683,-33.224971542491 -"Sample_1_CTCGTCACACAGGAGT",-101.430396366699,-31.1131665969463 -"Sample_1_CTCGTCAGTTCCACGG",86.090862219763,-57.4660423954041 -"Sample_1_CTCTAATAGTCCGTAT",-105.439149422371,-44.2124358371436 -"Sample_1_CTCTAATAGTGGGTTG",82.8136724282854,-45.3576670125168 -"Sample_1_CTCTACGAGGTGCACA",75.0798191591416,-46.5374681825624 -"Sample_1_CTCTACGTCGAACGGA",106.672354639479,-51.3645420597378 -"Sample_1_CTCTGGTAGCGATATA",70.949055611606,-59.5793184097934 -"Sample_1_CTCTGGTAGGGAACGG",36.1680693049987,84.2738994706962 -"Sample_1_CTCTGGTAGGTCGGAT",95.1644684149337,-23.1210727315561 -"Sample_1_CTCTGGTCACTGCCAG",62.5695626373957,-62.7606636997654 -"Sample_1_CTGAAACGTGCCTGTG",-56.9284716021049,-35.5946620257881 -"Sample_1_CTGAAGTCACGAAGCA",19.8302314346478,105.235228773217 -"Sample_1_CTGAAGTTCCGCAAGC",-101.81311166627,10.9330773578799 -"Sample_1_CTGAAGTTCGCAAACT",12.5532753241426,108.846042670112 -"Sample_1_CTGATAGAGCGTCAAG",56.4577523277187,107.854734887858 -"Sample_1_CTGATAGTCGAATCCA",88.8746743978553,30.3531122245903 -"Sample_1_CTGATCCTCATAACCG",103.400654767811,-45.4022086907442 -"Sample_1_CTGATCCTCGCAAGCC",17.4006933656351,112.106827338301 -"Sample_1_CTGATCCTCTACTATC",22.8300229072592,55.4385490052383 -"Sample_1_CTGCCTAAGAAGGTGA",-71.6211719459897,-31.3958062451981 -"Sample_1_CTGCCTAAGACAGAGA",19.754429736379,-12.5664063180935 -"Sample_1_CTGCCTACAGACAGGT",-77.3669732177227,30.7554717120053 -"Sample_1_CTGCTGTCAACGATCT",-64.8148897281011,-49.2230268958011 -"Sample_1_CTGGTCTCAACCGCCA",89.5855894536367,-60.1690826258468 -"Sample_1_CTGGTCTGTATCAGTC",-97.725571955562,-36.8598341519178 -"Sample_1_CTGGTCTGTTGACGTT",86.8247801626792,16.8484445655348 -"Sample_1_CTGGTCTGTTGTCGCG",24.9539314996514,109.073841257566 -"Sample_1_CTGTGCTCACATGGGA",44.5733552649135,113.280225013188 -"Sample_1_CTGTGCTCACGGCCAT",74.7647208084183,-25.7681822543443 -"Sample_1_CTGTGCTCACTTCGAA",-29.1001582581703,9.31656308664103 -"Sample_1_CTGTGCTTCAGCGATT",72.9179258343805,-9.13456028592391 -"Sample_1_CTTAACTGTGGACGAT",34.2837266105802,87.5312950930211 -"Sample_1_CTTAACTTCAACGGGA",-39.3278147237955,-15.3767154080603 -"Sample_1_CTTACCGAGCGTCAAG",81.1537521655878,-19.6619528438277 -"Sample_1_CTTAGGAAGCCAACAG",27.9647863036704,103.700646673405 -"Sample_1_CTTCTCTAGTATCGAA",83.445866855248,-37.3184317534553 -"Sample_1_CTTCTCTAGTGCGATG",-113.452177855703,-2.87270332444611 -"Sample_1_CTTTGCGTCGGGAGTA",87.1737836536033,0.472527353622993 -"Sample_1_GAAACTCAGTACGTTC",2.54153134308667,3.09627368712726 -"Sample_1_GAAACTCGTCTGCCAG",19.8196248655134,-40.7864254225259 -"Sample_1_GAAATGAAGAAGGGTA",76.9092038064015,-31.9929448691881 -"Sample_1_GAAATGAGTGTTAAGA",88.4084717379339,-37.4372554519761 -"Sample_1_GAACATCTCCTTGGTC",45.8830134650037,73.5209164035884 -"Sample_1_GAACCTACAACACCCG",18.6238993295307,112.387776774019 -"Sample_1_GAACGGAAGCAAATCA",81.444827887974,-12.9591724122245 -"Sample_1_GAAGCAGGTTCAGGCC",-73.1121158468192,1.71350741535643 -"Sample_1_GAAGCAGTCTGTGCAA",-37.0044349198911,-14.558519029108 -"Sample_1_GAATAAGAGCGATTCT",73.2941939063151,-41.794871648456 -"Sample_1_GAATAAGAGTTGAGAT",-80.0883622708181,-31.9288458587512 -"Sample_1_GAATAAGGTACCGTTA",27.4548859975133,116.54046788114 -"Sample_1_GAATAAGTCCACTCCA",-104.652147883666,-36.1378080527199 -"Sample_1_GACACGCCACGACGAA",78.3711252637478,-13.7355913359078 -"Sample_1_GACACGCGTAACGTTC",18.167064587885,6.29542682316371 -"Sample_1_GACAGAGAGGCGTACA",-19.1744126290352,-37.0931077614107 -"Sample_1_GACAGAGTCGAGAACG",0.213933935039773,-84.6459784840058 -"Sample_1_GACCAATGTGAGCGAT",8.25223187571676,-16.6906060841084 -"Sample_1_GACCTGGCACTTCGAA",-90.0976757859401,10.0163916786781 -"Sample_1_GACCTGGCAGTAAGAT",-7.92507674227428,-82.9768008841976 -"Sample_1_GACGGCTGTTGATTGC",-76.5910083096232,-45.9279988114777 -"Sample_1_GACGGCTGTTTGGGCC",75.6680787237432,3.90632545612338 -"Sample_1_GACGGCTTCAACGCTA",87.0125661684193,-33.8713474783129 -"Sample_1_GACGTGCTCACCATAG",1.80984414868613,-81.2963423540617 -"Sample_1_GACGTTAAGAATAGGG",-104.238627829595,-19.7815291584158 -"Sample_1_GACGTTAAGTGTACGG",25.3602991067208,103.816167776644 -"Sample_1_GACTACAAGTACACCT",88.8833409493838,-3.47555368150248 -"Sample_1_GACTACATCGGAATCT",-114.475990847313,7.1432507038467 -"Sample_1_GACTGCGGTTCGGCAC",100.395211610357,-45.7879565309988 -"Sample_1_GACTGCGTCAGTTGAC",29.7031133066755,89.6066282439331 -"Sample_1_GACTGCGTCTGTACGA",6.33376824483545,91.4439037694852 -"Sample_1_GAGCAGAAGTGCTGCC",18.1122240779537,77.4094924223565 -"Sample_1_GAGCAGAGTCTTGATG",-53.597566495501,-31.2230205928445 -"Sample_1_GAGGTGAGTCCTAGCG",-0.332789281174595,76.3671429006851 -"Sample_1_GAGGTGATCCCTAACC",-27.674651522877,-17.679177852333 -"Sample_1_GATCAGTAGAGTTGGC",29.1051509232944,105.807885218048 -"Sample_1_GATCAGTAGTAGCGGT",17.4863727636964,106.062232315131 -"Sample_1_GATCAGTCAAAGCGGT",37.3400455629792,100.772670977008 -"Sample_1_GATCAGTTCCGAGCCA",-87.1818433959744,-22.8190310813082 -"Sample_1_GATCAGTTCCTCGCAT",-73.8378905809448,-36.5776344057006 -"Sample_1_GATCGATGTTCCCGAG",31.7259252774382,98.5290732846427 -"Sample_1_GATCGATTCCACGCAG",-68.9067941068132,-21.7422724847236 -"Sample_1_GATCGCGAGACCTTTG",71.0881448651098,-56.4748839890202 -"Sample_1_GATCGCGCAAGAAAGG",-27.9856410193291,-100.214320430326 -"Sample_1_GATCGCGGTCAACTGT",-55.8174490510322,25.4124566882356 -"Sample_1_GATCGCGTCAAGGTAA",-30.4000862572134,3.18567339129732 -"Sample_1_GATCGTAAGGTACTCT",22.4984847588101,108.964315044767 -"Sample_1_GATGAAAAGACACTAA",30.057566190067,120.20665582599 -"Sample_1_GATGAAATCGTCTGAA",-100.687090699744,-28.2637715969308 -"Sample_1_GATGAGGTCCTAGGGC",-58.7566285254717,-8.19295465982133 -"Sample_1_GATGCTAAGTCCCACG",12.1500615583754,105.2694062681 -"Sample_1_GATGCTACAAAGGAAG",67.3841676005026,-50.8805817077073 -"Sample_1_GATGCTAGTAGAGGAA",105.285911476657,-20.5140587001952 -"Sample_1_GATTCAGAGACAGGCT",19.2793525757126,-15.4166486665881 -"Sample_1_GCAAACTCATCCCATC",0.946359052773698,-45.4440675137108 -"Sample_1_GCAAACTGTGGACGAT",-54.9132634676102,-9.00144695819968 -"Sample_1_GCAATCAAGTAGGCCA",-81.2911618132705,40.4352139123817 -"Sample_1_GCAATCAGTCTAGTCA",-64.6447865904971,11.5268714073005 -"Sample_1_GCAATCATCACCCTCA",-101.722732942239,-10.4939424803166 -"Sample_1_GCAGTTACAACGATGG",-116.038682335267,-40.8419158285935 -"Sample_1_GCAGTTAGTTCAGCGC",47.8994076803578,111.243210607241 -"Sample_1_GCATACATCCTGCCAT",76.7897472392228,-65.9329391985684 -"Sample_1_GCATACATCTTTAGGG",-102.284838685002,29.0279911686523 -"Sample_1_GCATGCGGTCCCTTGT",44.9959648963478,15.3358006134268 -"Sample_1_GCATGTAAGACGCACA",78.9845753665269,-10.8964922112619 -"Sample_1_GCATGTAAGAGTAATC",16.2161171063902,-69.9055663564352 -"Sample_1_GCATGTACACCTCGTT",-11.5465627933874,-80.3263232996423 -"Sample_1_GCCAAATAGTGGAGAA",96.8689586483078,-62.0177600884518 -"Sample_1_GCCTCTACAAACTGCT",27.8198504481635,-28.5398262877958 -"Sample_1_GCGACCACATGCCCGA",78.0415811255323,-60.6445503349515 -"Sample_1_GCGAGAAAGCTACCGC",26.6299670119599,114.933229692503 -"Sample_1_GCGAGAACAAGGCTCC",84.2561654914428,-10.4595375623146 -"Sample_1_GCGAGAACACCAGTTA",-116.971117254982,-16.760503745623 -"Sample_1_GCGCAACAGGGTGTGT",16.7832074541374,32.5388861247093 -"Sample_1_GCGCAACCACCCAGTG",-79.1274660856766,-18.914433228779 -"Sample_1_GCGCAACTCCTATGTT",92.8598155100209,28.4336775832583 -"Sample_1_GCGCAACTCTTGCATT",-66.6456270568596,-44.43909331727 -"Sample_1_GCGCAGTTCCGTCATC",-11.7129595428095,-72.2165461184207 -"Sample_1_GCGCCAACACCGTTGG",-2.78329746501661,75.6026703600806 -"Sample_1_GCGCGATTCCGTAGTA",-92.860368436736,18.1885617362233 -"Sample_1_GCGGGTTGTGCGCTTG",19.2525474945726,32.8571937983823 -"Sample_1_GCTCCTAGTAGAAGGA",-2.4092116369576,-68.4969260431308 -"Sample_1_GCTCTGTCAAGTCTAC",-94.0664037592111,-41.8499967597752 -"Sample_1_GCTGCAGAGTGGGATC",92.0510057824889,-7.57220258825287 -"Sample_1_GCTGCAGCACCACCAG",-70.8086766438947,-25.9172718848118 -"Sample_1_GCTGCAGGTCGATTGT",-96.003964376445,-10.063011707822 -"Sample_1_GCTGCAGTCCTTTACA",33.6840406925399,-32.5999066738946 -"Sample_1_GCTGCAGTCGGAGGTA",-57.2074809033239,21.2261496846613 -"Sample_1_GCTGCGAAGGCTACGA",-10.0258769041147,-80.7431749102576 -"Sample_1_GCTGCGAGTGTCAATC",50.8520159137731,103.456229429355 -"Sample_1_GCTGCGATCTGATACG",-94.7015239973051,-25.4199553891648 -"Sample_1_GCTGGGTCATATGCTG",-87.7794875290278,-46.6662896538979 -"Sample_1_GCTTCCACACATCCGG",86.6225435790178,-64.1752381157158 -"Sample_1_GCTTCCACATCTATGG",-50.8649668106202,1.28510617260686 -"Sample_1_GCTTCCATCTCTGTCG",31.1335815508813,7.16526482448235 -"Sample_1_GCTTGAACAGTATAAG",100.794131301297,-34.1517762565514 -"Sample_1_GCTTGAATCTAACCGA",-85.441439851198,-32.1282244492801 -"Sample_1_GGAAAGCTCCTTGCCA",-74.6674423490349,-9.81100571243423 -"Sample_1_GGAACTTCAGATGGCA",70.8000233986884,-50.8523376197968 -"Sample_1_GGAATAACAAATTGCC",-60.8940111202079,21.296626638242 -"Sample_1_GGAATAACAGTTCATG",17.2588030092792,92.1145442174673 -"Sample_1_GGACATTCACTTCGAA",-92.5782014424573,28.4588549390322 -"Sample_1_GGAGCAACAAGCGCTC",100.517050251447,-15.5627132857746 -"Sample_1_GGAGCAACATCTCCCA",39.4264396162931,16.0866533360982 -"Sample_1_GGAGCAATCGCATGGC",-34.7276419587305,20.9280736327926 -"Sample_1_GGAGCAATCGTAGATC",-35.0548647831686,-6.43456577507556 -"Sample_1_GGATGTTAGTCCGTAT",13.9263579468589,107.45524428527 -"Sample_1_GGATTACCATTGAGCT",-74.2881957365817,-40.8763713759149 -"Sample_1_GGATTACTCACCAGGC",32.8714954778668,16.1537898404927 -"Sample_1_GGCAATTAGAGGTAGA",69.0955980911142,-50.8209485932603 -"Sample_1_GGCAATTAGTGTTGAA",12.0091917263861,-66.3281665889371 -"Sample_1_GGCAATTCAGATTGCT",14.4555742762865,5.48363236745016 -"Sample_1_GGCAATTGTCAAACTC",-82.9270604433234,-15.3564230547981 -"Sample_1_GGCAATTGTGATAAGT",84.9495125026386,-64.5078170930312 -"Sample_1_GGCCGATGTTTCCACC",-76.1241636852535,3.4960944732295 -"Sample_1_GGCGACTCATCCTAGA",-111.646044225337,-15.6904273909138 -"Sample_1_GGCGACTCATCCTTGC",31.0684369069025,52.8413896504872 -"Sample_1_GGCGTGTCACATGACT",-79.8300195717455,-8.42950928487314 -"Sample_1_GGGAATGAGATGGCGT",35.3899206403707,123.170126767582 -"Sample_1_GGGAATGGTCATCCCT",38.2877191495127,96.2301240354644 -"Sample_1_GGGACCTTCTTGGGTA",-10.1887483022453,-73.3014235739358 -"Sample_1_GGGAGATAGTGTTTGC",36.1826412264117,101.727795259158 -"Sample_1_GGGAGATCAGATGGCA",26.1254071200395,7.00416838523734 -"Sample_1_GGGAGATCATACCATG",21.820805342226,101.763750660569 -"Sample_1_GGGAGATGTCAAACTC",-69.823823768513,38.5904964465309 -"Sample_1_GGGATGAGTCGTCTTC",94.0812409735838,-25.5272128359358 -"Sample_1_GGGATGATCAGTTCGA",78.6173656415857,-6.54475395785127 -"Sample_1_GGGCACTAGCAGGTCA",-68.0156349098204,15.0728862343074 -"Sample_1_GGGCACTCAAAGCAAT",8.23026063118468,-4.76877732956658 -"Sample_1_GGGCACTTCCTAGGGC",27.5196104580968,109.820799810151 -"Sample_1_GGGCACTTCTATCGCC",-96.1442543851453,-27.3486127170367 -"Sample_1_GGGCATCCACGGCGTT",-115.492083320262,-23.6995598202125 -"Sample_1_GGGCATCCATCACAAC",8.73897298927894,8.52709769108034 -"Sample_1_GGGCATCGTTCCTCCA",96.510620094667,-46.340482717424 -"Sample_1_GGGCATCTCCGCATCT",69.8785755547525,-32.5532782552146 -"Sample_1_GGTATTGGTGAGGGTT",20.6989651591457,54.8473855738841 -"Sample_1_GGTATTGGTTCCCGAG",20.7482148541534,-1.59972300450745 -"Sample_1_GGTGAAGAGAGGTAGA",7.81713830131196,-37.1732375440912 -"Sample_1_GGTGAAGTCCATGAGT",64.9774696637345,-4.42454793280377 -"Sample_1_GGTGAAGTCCTCATTA",-64.0923163164854,-9.06656605364548 -"Sample_1_GGTGCGTAGAGTACAT",-119.163403235687,-1.61463858146639 -"Sample_1_GGTGCGTAGTACATGA",41.0110779930242,26.2854925736234 -"Sample_1_GGTGTTAGTAGTAGTA",2.4461189521909,-73.3870601579771 -"Sample_1_GTAACGTAGGACAGCT",80.2467930279404,-34.2115444435793 -"Sample_1_GTAACGTTCACTTACT",60.7970889082104,-20.3765237853644 -"Sample_1_GTAACTGCATCGGTTA",-61.0115332868111,9.55171008780368 -"Sample_1_GTAACTGTCCGAACGC",-31.6544606222216,14.9835345328137 -"Sample_1_GTACGTAAGACACTAA",-22.8131452620376,15.4208733201588 -"Sample_1_GTACGTACATGCTGGC",76.7906512311679,-27.6372342803073 -"Sample_1_GTACGTATCCTGTAGA",7.76039172739002,85.4800866590095 -"Sample_1_GTACTTTGTGTCAATC",45.1459917641506,58.3209686092685 -"Sample_1_GTAGGCCAGCGCTCCA",97.9326022753233,13.0647396634175 -"Sample_1_GTAGGCCGTAACGTTC",-93.2923469636818,-34.2490624422257 -"Sample_1_GTATCTTTCCTATGTT",87.5879389878713,-21.2522123880373 -"Sample_1_GTATTCTCAGCTGCTG",86.3131451255589,3.29654385830702 -"Sample_1_GTATTCTGTCCCTACT",24.6054436802075,-18.0794387536479 -"Sample_1_GTATTCTTCCAATGGT",82.9006094814373,-31.3950982154844 -"Sample_1_GTCACAAGTCCGAAGA",33.3893867457499,103.401003522609 -"Sample_1_GTCACAAGTTCCACAA",-92.0259590799675,-23.7411613413077 -"Sample_1_GTCACGGCACCGGAAA",18.7026969865118,101.939506171805 -"Sample_1_GTCACGGGTTGCCTCT",93.1948131932108,-54.0192057209431 -"Sample_1_GTCACGGTCAAGGCTT",14.7380170719964,-69.2208827359807 -"Sample_1_GTCATTTAGCTTTGGT",-101.531134074593,-27.0906862241521 -"Sample_1_GTCATTTTCAGTTTGG",102.250123682199,-20.0173958381047 -"Sample_1_GTCCTCAAGTATCGAA",33.2853884299193,113.556277493254 -"Sample_1_GTCCTCAAGTGCCATT",104.189585697008,-39.730261416787 -"Sample_1_GTCGGGTGTCCTCTTG",90.8706988150748,-0.778576100270044 -"Sample_1_GTCGTAATCCATGAGT",21.4617258961793,-6.84287452174583 -"Sample_1_GTCTCGTAGTTCGCAT",13.6269904730176,101.940718467993 -"Sample_1_GTCTTCGGTATAAACG",59.0273831376365,-56.4428468891928 -"Sample_1_GTGAAGGAGTACGACG",-86.5895993368844,-15.9704026633613 -"Sample_1_GTGAAGGCAATAACGA",-95.2042103449549,-16.5464487874793 -"Sample_1_GTGAAGGCATGTTGAC",87.3590070440011,-7.17489788437415 -"Sample_1_GTGAAGGGTCCTCTTG",25.6639761648572,10.067368188257 -"Sample_1_GTGAAGGTCATGTAGC",-19.247579080189,-38.2217399370797 -"Sample_1_GTGCAGCAGCGATTCT",-11.5865076222696,-34.2780953787697 -"Sample_1_GTGCAGCAGGCTACGA",86.785097289384,-62.9512465021729 -"Sample_1_GTGCAGCTCAAAGACA",15.8678252102768,98.8565936983484 -"Sample_1_GTGCATACATCCGGGT",-103.676397906022,27.8682504930651 -"Sample_1_GTGCATATCACCGTAA",-49.8872396544392,-25.5327658596614 -"Sample_1_GTGCATATCATTATCC",-105.932490560246,15.9968878229249 -"Sample_1_GTGCGGTAGAGGTACC",94.6509880690418,-7.55283889862809 -"Sample_1_GTGCGGTCAAGCCGTC",-90.7096015522292,-31.39663422059 -"Sample_1_GTGCGGTCACATTCGA",10.1769377456069,112.479823145728 -"Sample_1_GTGCTTCAGCCAACAG",-68.3317771295441,-11.6194447960829 -"Sample_1_GTGGGTCTCAAGAAGT",-16.792472310354,-21.9004769793058 -"Sample_1_GTGTGCGTCGGTGTCG",83.1986020990873,7.86696170034237 -"Sample_1_GTGTTAGCATCCTAGA",-88.452767757509,21.8430780474619 -"Sample_1_GTGTTAGGTAGATTAG",92.537261910646,-11.9779633301125 -"Sample_1_GTTACAGCATTAGGCT",21.5490621447104,98.1599143669759 -"Sample_1_GTTCATTCATGCATGT",-93.0922478443389,38.9612753340973 -"Sample_1_GTTCATTTCCAAACAC",23.4906427421745,100.592919407601 -"Sample_1_GTTCTCGTCTAGCACA",-89.1607584801696,-6.79111273061037 -"Sample_1_GTTTCTAAGATTACCC",16.790007494257,12.9735062322475 -"Sample_1_GTTTCTAAGGTGCAAC",31.4678058399035,64.1782070210633 -"Sample_1_GTTTCTACATCACGAT",30.7726830634572,108.74810477887 -"Sample_1_TAAACCGAGTTCGATC",-33.0204509151326,-15.5161881128789 -"Sample_1_TAAACCGGTCTGCGGT",69.8231219676929,-15.9127322541021 -"Sample_1_TAAACCGGTTCGCGAC",48.0453913762656,-29.4446737512752 -"Sample_1_TAAGAGAGTACTTAGC",-47.2766871557781,-35.0578889563935 -"Sample_1_TAAGAGATCCGAAGAG",20.4294895531764,107.92499549458 -"Sample_1_TAAGCGTAGGACTGGT",-62.3288146958465,-32.5359116143821 -"Sample_1_TAAGTGCCAGACAAAT",-95.0897339545199,41.699383831018 -"Sample_1_TAAGTGCTCGCCATAA",-103.270250271776,8.70403996019227 -"Sample_1_TACACGAGTCACTTCC",-69.8333300632374,3.45011169899085 -"Sample_1_TACACGAGTTTCGCTC",0.29700789321978,79.464695897141 -"Sample_1_TACACGATCCCAGGTG",0.807296188299312,-41.8998555411235 -"Sample_1_TACCTATGTCCAACTA",84.5992821863117,17.7479225977144 -"Sample_1_TACCTTAAGCTAACAA",-49.5036818360188,16.2594310936674 -"Sample_1_TACCTTATCTGGCGTG",-51.4430773599616,-52.0205165040753 -"Sample_1_TACGGGCCATCCTAGA",33.4257191419347,62.7015119376484 -"Sample_1_TACGGGCCATTACCTT",0.848941974403348,-6.88144520116696 -"Sample_1_TACGGTAAGGATGGAA",102.599518981234,-1.0731783749302 -"Sample_1_TACTCATTCGGCCGAT",-0.775700765979688,-33.4200767358761 -"Sample_1_TACTCGCCAACTGCGC",-78.2881619274883,-36.5917174439114 -"Sample_1_TACTCGCTCCGAATGT",33.8497336413534,109.941448384541 -"Sample_1_TACTCGCTCGTAGGTT",35.4197657368433,85.5629598756574 -"Sample_1_TACTTACTCCGTTGTC",-91.683462817023,-49.9295219854768 -"Sample_1_TACTTGTGTACTTAGC",-99.4428443933281,-20.3541936219456 -"Sample_1_TAGACCAAGAGAGCTC",-10.9861104948543,-36.3494178856202 -"Sample_1_TAGACCACAGTCCTTC",43.5016238025187,58.2168607638286 -"Sample_1_TAGCCGGCAATGTTGC",68.0654251541094,-31.4637038944023 -"Sample_1_TAGCCGGTCGAGAACG",26.3977079007111,-15.0247572125979 -"Sample_1_TAGGCATCAGTCCTTC",30.0460825631414,23.8030912722632 -"Sample_1_TAGGCATGTTGTACAC",50.3918340040553,122.746381079753 -"Sample_1_TAGTGGTCAAGGGTCA",-87.0318268770668,0.268861095332359 -"Sample_1_TAGTGGTTCAATACCG",4.73063130438575,108.73012949862 -"Sample_1_TAGTGGTTCCTAGTGA",92.3171059876734,-42.982256487209 -"Sample_1_TAGTTGGAGTAGGCCA",-84.4768383182647,-2.3384675663752 -"Sample_1_TATCAGGCAACTGCTA",-60.91572734641,-12.973187388664 -"Sample_1_TATCAGGGTCGGATCC",61.2411846898991,-11.3982818717759 -"Sample_1_TATCTCAAGACTTGAA",-88.463057699058,17.7194403231212 -"Sample_1_TATCTCAAGGGAGTAA",-104.950884783884,-31.2272621408377 -"Sample_1_TATCTCAAGGTCATCT",21.0791967934385,7.3741036604928 -"Sample_1_TATCTCAGTCTACCTC",23.922542630959,-4.09044946362505 -"Sample_1_TATCTCATCATGTAGC",86.1545991212601,-40.0796262852076 -"Sample_1_TATGCCCCAAGCTGTT",97.6218073708061,-43.8613983022346 -"Sample_1_TATGCCCCAATCACAC",34.6529046403231,120.125021792025 -"Sample_1_TATTACCCAAACCCAT",20.5120110426297,80.5372553730692 -"Sample_1_TATTACCCACGAAAGC",12.1525843154599,86.7997369527664 -"Sample_1_TATTACCGTGATGTGG",-86.2652561822593,-53.834563049958 -"Sample_1_TCAACGACAATCTACG",-101.153569294121,-23.7945029091646 -"Sample_1_TCAACGATCCTTTCGG",3.57623296738807,-46.5547618060621 -"Sample_1_TCAATCTGTGCAGACA",2.9999311561525,-67.6828490293222 -"Sample_1_TCAATCTGTGCGATAG",101.314607104043,-9.26824872756021 -"Sample_1_TCACAAGCAATCGAAA",-79.5602654476442,22.7742612542867 -"Sample_1_TCACAAGGTCTCATCC",-40.9807848823392,-27.9818261030965 -"Sample_1_TCACGAACATACTACG",-2.17571217682015,-83.5563086731282 -"Sample_1_TCACGAAGTAGAAAGG",-1.53876914511114,-65.7853174297629 -"Sample_1_TCACGAATCATCGATG",88.5627780073281,-48.7655462961677 -"Sample_1_TCACGAATCGTCACGG",80.6866811459695,-32.7441612004935 -"Sample_1_TCAGATGAGTTGAGTA",29.7784105432304,59.9475976359067 -"Sample_1_TCAGCAAGTGCTTCTC",99.5059553463045,-41.0404009806749 -"Sample_1_TCAGCTCAGACAGGCT",27.5117954821284,-8.67886361554992 -"Sample_1_TCAGCTCGTCGAGTTT",88.5159741850486,1.11767649207787 -"Sample_1_TCAGGATTCATTTGGG",89.7375788828043,-27.4501666438473 -"Sample_1_TCAGGTAAGTCCGGTC",6.38226820898479,-6.68973058022012 -"Sample_1_TCAGGTACAGGATTGG",9.32381959108014,-69.3633093708888 -"Sample_1_TCAGGTAGTCCGTCAG",-61.6159758810862,-24.5696012435399 -"Sample_1_TCAGGTATCGAGGTAG",-87.8132907983953,26.6777535593627 -"Sample_1_TCAGGTATCGCCTGTT",43.6145752906419,21.408412972419 -"Sample_1_TCATTACCAGTATAAG",-79.3529991992238,34.5704178244128 -"Sample_1_TCATTTGAGGCAATTA",99.1710011646898,-11.8451592254055 -"Sample_1_TCATTTGAGTGGTAGC",7.62942490845114,-78.9830606864408 -"Sample_1_TCATTTGGTGTTGAGG",-6.2823170440699,-69.2362237490857 -"Sample_1_TCCACACAGTACACCT",15.4035215367144,98.0373740726823 -"Sample_1_TCCACACCAGTTTACG",-105.339737389286,-1.8736184723574 -"Sample_1_TCCCGATAGACAGGCT",31.7057078942305,87.827384707408 -"Sample_1_TCCCGATCAAGTTCTG",-79.2146860982257,-22.3374427316933 -"Sample_1_TCCCGATTCGTAGATC",42.7037847032551,53.6730221126393 -"Sample_1_TCGAGGCGTAGAAGGA",15.8197726066308,41.5963690697239 -"Sample_1_TCGAGGCGTTTACTCT",-35.5977163039369,-22.1388172513879 -"Sample_1_TCGAGGCTCCTCAACC",67.9465941134845,-46.8169211295018 -"Sample_1_TCGAGGCTCGTTGACA",-99.0061688345487,18.704686840673 -"Sample_1_TCGCGAGGTCAGTGGA",24.3752937575338,-11.0171632200018 -"Sample_1_TCGGGACCAGACGTAG",99.2892355480942,3.75786596017467 -"Sample_1_TCGGGACGTGATAAGT",-100.356912456527,-22.7812329702807 -"Sample_1_TCGGGACTCACCCGAG",34.1574870619448,95.9278452885475 -"Sample_1_TCGGGACTCCGAAGAG",-91.1334744697257,-12.7786266444068 -"Sample_1_TCGGTAACACAGGCCT",66.1261560944269,-19.0950464335362 -"Sample_1_TCGGTAACATTAACCG",-51.1537161633645,-8.33743514698219 -"Sample_1_TCGTACCAGATCTGCT",60.1656492324017,-35.8664152020891 -"Sample_1_TCGTACCGTTTGACAC",-82.3471146605767,-18.8377927334371 -"Sample_1_TCGTACCTCAAACCGT",-56.5297517827507,-43.625792745144 -"Sample_1_TCGTACCTCCCGACTT",76.7715884017567,-49.1539272512718 -"Sample_1_TCGTAGACATCTATGG",-106.875523463696,14.5244976213581 -"Sample_1_TCTATTGAGGCGACAT",92.1089219582889,2.17018446442713 -"Sample_1_TCTATTGGTAGCTCCG",-10.0825549469154,-32.945255403859 -"Sample_1_TCTCATAAGACTTTCG",-5.61927694604169,-62.8032602282821 -"Sample_1_TCTCTAATCCGTAGTA",19.5960099944945,-5.14368962809347 -"Sample_1_TCTGAGAAGAGTGAGA",-108.367225374458,-40.0010711475711 -"Sample_1_TCTGAGACACCACGTG",-2.96002027685107,-71.5899418522058 -"Sample_1_TCTGAGACAGATCTGT",61.4577683045289,-25.634716404268 -"Sample_1_TCTGGAAAGTACGTTC",92.5296318152251,22.2967350678555 -"Sample_1_TCTTCGGAGGGATGGG",89.8698502722338,4.8687730451652 -"Sample_1_TCTTTCCAGACTGGGT",-89.5594317738693,35.2794194341123 -"Sample_1_TCTTTCCGTGCTTCTC",85.3325111813163,-43.3968144294722 -"Sample_1_TGAAAGATCCCTAATT",-107.009609521791,-52.3685964646882 -"Sample_1_TGAAAGATCGTTGCCT",9.05903279853491,-78.1647993546776 -"Sample_1_TGACAACTCCTACAGA",-77.3538521876331,42.8859003264559 -"Sample_1_TGACTTTCAATGGATA",-9.46061485485082,-23.5803826727438 -"Sample_1_TGACTTTTCTAGCACA",93.4187851046033,-48.8934844657646 -"Sample_1_TGAGAGGAGCAATCTC",-72.7327922137852,25.1374127486751 -"Sample_1_TGAGCATGTCAAAGCG",12.8254138855088,12.3325595621691 -"Sample_1_TGAGCCGGTGCCTTGG",-109.042763375915,-47.4869576151049 -"Sample_1_TGAGGGAAGCATGGCA",3.23422930108747,98.405289345455 -"Sample_1_TGAGGGACATGGGAAC",19.2075205471596,3.77751021916394 -"Sample_1_TGAGGGATCACATGCA",75.8404337585329,-41.615366657803 -"Sample_1_TGATTTCTCCTTCAAT",-83.818176489392,-22.6799247213116 -"Sample_1_TGCACCTAGGGCACTA",-114.91423348507,17.1845263656033 -"Sample_1_TGCCCATCAGTAAGCG",70.5036187862386,-27.6705986166317 -"Sample_1_TGCCCTAGTTCAACCA",-84.0279114894905,-27.466520201092 -"Sample_1_TGCCCTATCAGGCAAG",39.5170397437723,67.4257308324694 -"Sample_1_TGCGGGTAGACCTAGG",95.5046729749451,-28.3960097065128 -"Sample_1_TGCGGGTGTTATGTGC",-23.4266593280776,-105.224049213506 -"Sample_1_TGCGTGGGTAATTGGA",-60.7613667208064,-5.93031331009658 -"Sample_1_TGCTACCAGCTCCCAG",52.4743178592293,113.68643514472 -"Sample_1_TGCTGCTGTCCAACTA",36.7573470325984,18.9497471278882 -"Sample_1_TGGACGCAGGAGCGAG",11.8644219460985,28.0434279508868 -"Sample_1_TGGACGCGTAACGCGA",93.254833856725,-4.9235370677289 -"Sample_1_TGGCGCAAGTACGCGA",67.4609632667592,-10.7878994572047 -"Sample_1_TGGCGCAGTTCCCGAG",-80.3589451518111,-27.4026478369211 -"Sample_1_TGGCGCATCCTACAGA",66.5432892727228,-59.7713632659029 -"Sample_1_TGGGAAGAGGAACTGC",19.7085639746591,111.496975630203 -"Sample_1_TGGGAAGTCTATCCTA",-73.2210855383384,-64.0572598177205 -"Sample_1_TGGGCGTCAGTTAACC",94.1006192116099,18.3307143044013 -"Sample_1_TGGTTAGAGCGCCTTG",-79.9298264980925,-3.64442922090799 -"Sample_1_TGGTTAGAGGAATTAC",5.56703830206154,6.86823112841642 -"Sample_1_TGGTTAGGTCAATACC",-58.2148908308031,-16.6010098214988 -"Sample_1_TGGTTCCTCCACTCCA",39.3560071713782,21.0504568015516 -"Sample_1_TGTATTCAGATGTCGG",39.4882926190208,-0.762339780089796 -"Sample_1_TGTATTCCAGCGTCCA",-89.8556317816386,31.7359529717152 -"Sample_1_TGTATTCTCTGATACG",-95.8342261698585,18.9032688475916 -"Sample_1_TGTCCCAAGAGACTAT",-76.1168845711986,47.0834765568265 -"Sample_1_TGTCCCATCTTGTACT",-71.3976857612213,5.92791498427298 -"Sample_1_TGTGGTACATGCAACT",37.7770321034543,111.214777934438 -"Sample_1_TGTGGTAGTGCATCTA",7.47877832686062,-82.6385687135644 -"Sample_1_TGTGGTATCCCGACTT",79.4564263457833,-48.4832215218116 -"Sample_1_TGTGTTTAGGCCCTCA",-62.5392281634132,34.8140561066335 -"Sample_1_TGTGTTTGTTGTGGAG",31.2446672500987,-11.3540363847317 -"Sample_1_TGTGTTTTCATGCTCC",104.462192808945,9.57293984090558 -"Sample_1_TGTTCCGAGCCACCTG",-70.8642172406314,-9.02500858295167 -"Sample_1_TGTTCCGCAGTGACAG",-26.8261983992332,-108.357048045265 -"Sample_1_TTAGGACAGGGTCGAT",11.9093561075982,-77.7064960768155 -"Sample_1_TTAGGACGTGGTACAG",78.3111216687441,-21.9279313814137 -"Sample_1_TTAGGCACATAAAGGT",69.9168052829754,2.42025850919118 -"Sample_1_TTAGTTCTCACTTACT",62.0256238625637,-35.2290811458014 -"Sample_1_TTAGTTCTCTTGAGAC",58.7753493556067,-0.00342296403094438 -"Sample_1_TTATGCTTCACGAAGG",49.9833117774494,111.47284772452 -"Sample_1_TTCGGTCAGCCAGAAC",-98.5126775206649,38.1503801929648 -"Sample_1_TTCGGTCAGTTCCACA",-23.4749761876926,-180.355180746626 -"Sample_1_TTCTACACATTACCTT",0.476728856819792,-60.1828580623216 -"Sample_1_TTCTACATCGCAAGCC",72.9057271904503,-44.4889329480706 -"Sample_1_TTCTCAACACACTGCG",10.4517911512281,-9.98519103441676 -"Sample_1_TTCTCCTGTTCCCGAG",-86.3740675968547,5.76018794062532 -"Sample_1_TTCTCCTTCCGAATGT",83.4692713587906,-26.4565965792906 -"Sample_1_TTCTTAGGTTTAGGAA",29.2928242175691,-32.221196109435 -"Sample_1_TTGAACGAGCCATCGC",-105.449107076847,-19.8874804339698 -"Sample_1_TTGAACGGTAGCTCCG",59.4969209615956,-49.4950531276952 -"Sample_1_TTGAACGGTCTCAACA",-21.0169091927705,-20.5965698524584 -"Sample_1_TTGAACGTCAGTTCGA",44.4672831377357,119.134824018977 -"Sample_1_TTGACTTTCCCTAACC",-37.798847509632,-16.0723547998113 -"Sample_1_TTGCCGTCACCTTGTC",-60.7087164714253,32.062065863826 -"Sample_1_TTGCCGTTCTTGCAAG",68.2814458176197,-35.7278620065549 -"Sample_1_TTGCGTCCAGCCTTGG",29.359886584673,-21.8199369808954 -"Sample_1_TTGCGTCTCACGGTTA",36.0049999041522,65.9722033809119 -"Sample_1_TTGCGTCTCCTATTCA",101.477944745524,-53.8876421438688 -"Sample_1_TTGCGTCTCTTCTGGC",17.547132347216,25.7813159664835 -"Sample_1_TTGGAACGTCCATCCT",-29.1675319530645,9.57160563423889 -"Sample_1_TTGGCAAAGAGATGAG",-81.7625949604017,35.9238775618681 -"Sample_1_TTGGCAAGTATCACCA",54.9836170187976,-31.7832245746247 -"Sample_1_TTGTAGGAGTCGTACT",82.9979562774418,-44.0536255061691 -"Sample_1_TTTATGCAGCCACTAT",80.4258675057975,-14.1181367431881 -"Sample_1_TTTATGCCAGTTAACC",72.2280243731788,-8.68416154803927 -"Sample_1_TTTATGCGTACGACCC",-29.6275697667986,-17.2051421755982 -"Sample_1_TTTGCGCAGGCTACGA",-70.258025564289,33.1350938738881 -"Sample_1_TTTGCGCTCACTATTC",84.4444739828794,-51.9138415862219 -"Sample_1_TTTGGTTTCTGTTTGT",-14.7564531260578,-71.4120515045567 -"Sample_1_TTTGTCACAATTGCTG",41.1346877357786,-47.7802518137939 -"Sample_1_TTTGTCAGTAGGAGTC",-24.7600535581692,-184.251808944544 -"Sample_1_TTTGTCATCCTCATTA",-109.620386523068,-46.4769359901447 -"Sample_2_AAACCTGTCTGCTGTC",-6.4525288516235,-75.0097232655395 -"Sample_2_AAACGGGGTTTGCATG",3.27110429526546,73.8478780647896 -"Sample_2_AAACGGGTCCTTTACA",101.301775692492,-36.2768367316607 -"Sample_2_AAAGTAGCAAACAACA",6.34669306178657,110.766245467581 -"Sample_2_AAAGTAGCATAGAAAC",7.48986955621635,96.6674847000251 -"Sample_2_AAATGCCCATGATCCA",99.7333273532646,-16.7499540271149 -"Sample_2_AAATGCCGTTAGTGGG",29.3890346658633,96.3197486781719 -"Sample_2_AAATGCCTCAGTTAGC",95.4854989813479,-15.1290941883986 -"Sample_2_AACACGTGTCGCTTCT",103.924591455566,-12.019989252666 -"Sample_2_AACCATGGTCTCACCT",16.1480822300527,35.2927784745318 -"Sample_2_AACTCAGGTACCGTAT",-106.204654661095,2.36188332525444 -"Sample_2_AACTCAGGTCCATCCT",-44.7603581205946,-30.4904106271472 -"Sample_2_AACTCAGGTCGCTTCT",96.7891490326365,-8.53689321236637 -"Sample_2_AACTCAGTCCTTTCTC",-92.4871066715164,-1.64502935913403 -"Sample_2_AACTCTTCAAGTAATG",-53.1355751521555,-12.3051303711781 -"Sample_2_AACTCTTCACACATGT",-3.52394995723485,3.305119413562 -"Sample_2_AACTCTTGTGCCTGTG",-4.08604753620476,78.6519890505597 -"Sample_2_AACTCTTTCAAACAAG",-94.9100482014163,30.6701814900464 -"Sample_2_AACTCTTTCCTCGCAT",0.836936325084341,-66.9605009171928 -"Sample_2_AACTGGTGTGCGGTAA",97.6282315896876,-3.49119166569285 -"Sample_2_AACTTTCGTAGCGATG",-102.517148867959,41.8475623944626 -"Sample_2_AAGACCTAGATGTCGG",100.476509190676,-50.6120597729412 -"Sample_2_AAGCCGCAGCTGAACG",-49.4919406039598,-14.9846367035412 -"Sample_2_AAGCCGCGTTCGCGAC",-12.6883238866745,-44.5913445540439 -"Sample_2_AAGGAGCCAATACGCT",80.5933295033896,22.6926751596158 -"Sample_2_AAGGCAGAGAGGTAGA",33.8959544544891,57.8132403293199 -"Sample_2_AAGGCAGAGGAATCGC",15.299767788124,47.3370630952421 -"Sample_2_AAGGTTCGTCATATGC",7.03583198768429,116.918249444328 -"Sample_2_AAGGTTCTCAGTGCAT",103.115546368924,-7.14381412911387 -"Sample_2_AAGTCTGAGAGGTAGA",-30.0943990233001,-104.599294617421 -"Sample_2_AATCCAGCAAGCCCAC",-107.656523342234,-0.297615557511267 -"Sample_2_AATCCAGGTAAGCACG",-0.837673307328785,-50.575019441001 -"Sample_2_AATCCAGTCACAACGT",78.6261869642118,-34.3778852796781 -"Sample_2_AATCGGTCATCACAAC",77.5116739841352,-54.6836435928655 -"Sample_2_ACACCCTCAGCTGTGC",-81.2049250292456,8.26614194366936 -"Sample_2_ACACCCTCATACCATG",-56.9626224033098,-23.9387078741635 -"Sample_2_ACACCCTGTCACAAGG",-31.5239180993709,-17.5860500568289 -"Sample_2_ACACTGAAGCTGAACG",19.3146379836621,47.4930471088998 -"Sample_2_ACACTGATCTTACCTA",-107.901142331263,-6.7994232520312 -"Sample_2_ACAGCCGAGCTACCGC",-39.3415066992897,-9.77093600844127 -"Sample_2_ACAGCTAAGGCAGTCA",-89.5288521096284,-26.3728431873414 -"Sample_2_ACATACGAGAGAACAG",62.6562946827288,-31.8308704095069 -"Sample_2_ACATACGAGGCTAGAC",28.1869712441516,-42.8853251958553 -"Sample_2_ACATACGTCAACCAAC",-70.6418165763854,36.184455840931 -"Sample_2_ACATCAGAGGTCGGAT",-64.8510703141461,-18.6090234714178 -"Sample_2_ACATCAGGTCCGTTAA",-69.8154489336586,17.768759685528 -"Sample_2_ACATGGTGTCGCTTCT",82.9547764490538,-1.82120084416534 -"Sample_2_ACCAGTACACAACTGT",53.9262302293836,21.8102950375508 -"Sample_2_ACCAGTATCCTCGCAT",93.8764948618301,-43.6230210653688 -"Sample_2_ACCAGTATCTGAAAGA",-67.8265296407297,-17.2206867511422 -"Sample_2_ACCGTAACAAGAAGAG",24.1582647241499,119.196432435828 -"Sample_2_ACCGTAACATTGAGCT",29.7990839971188,9.62076423881146 -"Sample_2_ACCGTAATCGCTTGTC",96.1144294464021,-54.817749602091 -"Sample_2_ACCTTTAAGCGATATA",25.8061591331267,99.8085533959674 -"Sample_2_ACGAGCCAGACCTAGG",44.1401514973142,103.949683677447 -"Sample_2_ACGAGCCGTCTAGTCA",-48.4032333180793,0.020837459808558 -"Sample_2_ACGATACAGAGACGAA",77.7893461817996,-43.3114306175634 -"Sample_2_ACGATACCAATACGCT",-67.1562075732557,28.3381143127879 -"Sample_2_ACGATACGTCTGCCAG",51.3698432683072,110.065655811882 -"Sample_2_ACGATGTAGAGCTTCT",71.6703754583058,-24.8859066660522 -"Sample_2_ACGATGTCATTCACTT",79.4738887917349,-24.9608973035703 -"Sample_2_ACGATGTGTCATATCG",10.9683880994842,15.1962410904807 -"Sample_2_ACGCAGCCAAGCGCTC",98.0063208698392,-31.2022693067064 -"Sample_2_ACGCCGAAGGTGTTAA",-82.2316987832242,24.7176913108255 -"Sample_2_ACGGAGAAGACAGAGA",16.0736319675538,38.0623568971284 -"Sample_2_ACGGCCAGTTGATTCG",7.79033423516192,-67.5025376161441 -"Sample_2_ACGTCAAAGGTAGCTG",-103.250008679021,15.4969926279815 -"Sample_2_ACGTCAATCGGAGCAA",-82.3698862060576,42.5026726111902 -"Sample_2_ACGTCAATCGGCATCG",95.23302044888,-10.8177421775051 -"Sample_2_ACTATCTAGCTACCTA",-88.9365161476166,-11.0082026083824 -"Sample_2_ACTGAACGTCAATGTC",41.561575882997,117.367540596761 -"Sample_2_ACTGAACTCCCAACGG",44.7239506741526,100.869359159197 -"Sample_2_ACTGCTCCATTAGCCA",-58.4383695339179,7.327621176187 -"Sample_2_ACTGTCCAGAAGAAGC",61.6636632506279,-55.8529647741823 -"Sample_2_ACTGTCCGTACCGCTG",72.7084882289854,-34.5733454690384 -"Sample_2_ACTTACTAGCCGGTAA",27.5104239076948,50.6751357831567 -"Sample_2_ACTTACTGTACCGGCT",-92.1733612602469,40.049003370839 -"Sample_2_ACTTGTTCAGATGGCA",-82.8071030983856,17.4509264626645 -"Sample_2_ACTTGTTTCACGATGT",-9.62014553680594,-34.9988235924088 -"Sample_2_ACTTTCACACAGTCGC",47.3650649268358,97.3410598341391 -"Sample_2_ACTTTCAGTAAGGATT",62.6577985370445,-60.1372268754843 -"Sample_2_ACTTTCATCAGTTGAC",10.2417349384197,89.9479943033932 -"Sample_2_AGAATAGTCAACACCA",-42.6832225335899,-21.4008469540649 -"Sample_2_AGAGCGAAGGTCATCT",94.9494790560669,8.27222600112952 -"Sample_2_AGAGCGAGTACCGGCT",-99.340308467474,15.025540697604 -"Sample_2_AGAGCTTAGGAGTTTA",-94.8831680566354,2.94048422840448 -"Sample_2_AGAGCTTTCAAGAAGT",-51.9158614423445,-23.0981680080596 -"Sample_2_AGAGTGGTCCAGTAGT",22.0474210684637,84.4015381620561 -"Sample_2_AGAGTGGTCCTTGACC",20.4425165161011,52.9677034465419 -"Sample_2_AGCAGCCAGTTCCACA",24.2972484462188,58.3136521260559 -"Sample_2_AGCCTAACAGTCTTCC",-59.2260522299098,29.6272252639248 -"Sample_2_AGCGTATAGTGCAAGC",96.9542074530337,-50.3239555103725 -"Sample_2_AGCGTATGTGACGGTA",-68.0103181559826,24.3370245807608 -"Sample_2_AGCGTCGCAGCGAACA",82.2679782417993,7.4133958175121 -"Sample_2_AGCTCCTTCAGAGCTT",-104.470659266061,-48.4740852594674 -"Sample_2_AGCTCTCCATTTGCCC",-27.2579597363975,-105.186179364193 -"Sample_2_AGCTTGAAGACTAGAT",-73.5666847591741,-1.17730957001774 -"Sample_2_AGCTTGAGTCTCACCT",15.1550404855462,111.711179493245 -"Sample_2_AGGCCACCAAGCGAGT",-28.7966532017077,-10.8876129177262 -"Sample_2_AGGCCACCAAGTAATG",-72.5519296726627,41.3137415569197 -"Sample_2_AGGCCACCACGGCTAC",21.4855378285747,-15.9483506533046 -"Sample_2_AGGCCACTCTCTTATG",-114.02993486799,-10.3869060627089 -"Sample_2_AGGCCGTGTAGAGGAA",61.3917962557247,-15.7763107674685 -"Sample_2_AGGCCGTTCATCGATG",102.7346349934,-58.363824765824 -"Sample_2_AGGGATGGTCTCAACA",-10.0277550443186,-76.6440116967476 -"Sample_2_AGGGTGACAGTAAGAT",-94.3953998871085,26.8608154762941 -"Sample_2_AGGTCATCACAGACTT",-72.9957684607237,30.7921135275083 -"Sample_2_AGGTCATTCGAGAGCA",-84.5483522160253,-35.3474874409982 -"Sample_2_AGGTCCGCAGCTGTGC",-29.4259411496532,-109.680317331555 -"Sample_2_AGTAGTCTCACTCCTG",40.9528268320491,-3.49629766001245 -"Sample_2_AGTCTTTAGCCTCGTG",2.26711571652419,-30.2318380347856 -"Sample_2_AGTCTTTAGGATTCGG",10.8244031570812,-2.90155170469601 -"Sample_2_AGTGAGGAGGCTCTTA",-104.83057375615,-9.48135490504498 -"Sample_2_AGTGAGGGTGCACCAC",84.6890798633353,19.729071392097 -"Sample_2_AGTGAGGTCTGATACG",18.5028099336788,-8.42039839376076 -"Sample_2_AGTGTCATCCTTTCGG",3.96151152520462,-18.8995428590007 -"Sample_2_AGTGTCATCTGGGCCA",-72.756618404756,-64.9273098052414 -"Sample_2_AGTGTCATCTTACCTA",-0.433555141694827,-71.7884874900621 -"Sample_2_AGTTGGTTCGTCCAGG",87.3226372155537,-75.0638996134925 -"Sample_2_ATAACGCAGTGGCACA",-32.2229703788859,-107.70177858555 -"Sample_2_ATAACGCCAGTCAGAG",2.10782211773733,-44.0325084422093 -"Sample_2_ATAACGCGTAGCTTGT",30.4593744585174,119.2444601775 -"Sample_2_ATAAGAGGTGCCTGGT",-71.0277793314757,10.6827591518542 -"Sample_2_ATAGACCTCTTTACGT",-79.5264663992442,-0.644806732911734 -"Sample_2_ATCACGAAGTGGAGTC",15.1443354873406,-2.68494081217683 -"Sample_2_ATCACGATCAGCAACT",11.4295025530292,94.5065691499601 -"Sample_2_ATCATCTCAACACGCC",9.33063330915068,93.2945915725591 -"Sample_2_ATCATGGAGGTGATAT",-93.7303589766209,-45.6629625477963 -"Sample_2_ATCATGGCAGCCAGAA",-24.7808153266791,-178.677099583028 -"Sample_2_ATCCACCAGTAGATGT",-48.8733213617891,3.34153442900915 -"Sample_2_ATCCACCCATCGATGT",-23.2196477834213,-184.05458938377 -"Sample_2_ATCCACCGTAACGACG",27.7211328955501,-23.6712262185302 -"Sample_2_ATCCGAAAGAAGATTC",-39.5835378082941,-13.391013422615 -"Sample_2_ATCCGAACACTAGTAC",-78.8899209157267,-45.8099871082566 -"Sample_2_ATCCGAAGTACACCGC",-76.3512525713039,-13.9603761472015 -"Sample_2_ATCGAGTCACAACTGT",51.6942932112565,-19.6807747025887 -"Sample_2_ATCTACTCACGAGGTA",-102.351315825433,-35.5842134006868 -"Sample_2_ATCTACTCATGGGACA",33.5792471882079,51.6466765971553 -"Sample_2_ATCTACTTCGGCCGAT",37.0606593750193,74.3879960531629 -"Sample_2_ATCTGCCAGCGCTCCA",86.604983274721,-17.8859261872382 -"Sample_2_ATCTGCCAGTGACATA",-85.8926995950396,34.4514759097933 -"Sample_2_ATCTGCCAGTTGAGTA",-84.6858946629978,-43.5520311179822 -"Sample_2_ATCTGCCCACTTAACG",60.9450582326257,-16.21267194971 -"Sample_2_ATCTGCCCATCCCACT",-75.9601835835745,-17.241366865818 -"Sample_2_ATCTGCCGTCTCTTTA",1.42042558420784,-4.27251893069526 -"Sample_2_ATCTGCCGTTAGTGGG",90.623778788266,30.7487288451068 -"Sample_2_ATCTGCCTCGCTTAGA",26.1525070423625,-0.657910586119107 -"Sample_2_ATGAGGGGTACCGTAT",4.45184670342266,-78.7534913097363 -"Sample_2_ATGCGATGTTCTGTTT",80.9395822473695,-42.5624854484613 -"Sample_2_ATGGGAGAGTTGTAGA",-54.0115017044557,-44.6737655577533 -"Sample_2_ATGTGTGAGCATCATC",-37.1395390930787,0.489504998800926 -"Sample_2_ATGTGTGAGGTCATCT",-103.599473347746,-15.4081813953765 -"Sample_2_ATGTGTGGTGTATGGG",83.7631066001182,-42.1857431819241 -"Sample_2_ATTACTCAGGTCATCT",-88.7508186058357,-51.5783762118858 -"Sample_2_ATTACTCGTGGAAAGA",-10.5054065237403,-38.6928180761434 -"Sample_2_ATTATCCAGTAGCGGT",-99.8509602309104,-41.3278012840475 -"Sample_2_ATTATCCCACCCATGG",98.4518409838836,-51.9617567239108 -"Sample_2_ATTCTACCAAGCGATG",15.725358587378,1.95039664647983 -"Sample_2_ATTCTACCATTAACCG",55.242671488354,3.58105392616613 -"Sample_2_ATTCTACGTCTAGTCA",-75.040794549892,-24.3172197077595 -"Sample_2_ATTCTACGTGGTAACG",5.59708627276863,-86.8905536692169 -"Sample_2_ATTGGACCAGCGTTCG",7.99137220608352,-75.5703219580045 -"Sample_2_ATTGGACCAGCTTCGG",24.087987996595,-35.2423498967455 -"Sample_2_ATTGGACGTAGCGTGA",18.7542734859777,120.931379863157 -"Sample_2_ATTGGACGTCGCATCG",-35.5359299350829,12.9525529310063 -"Sample_2_ATTGGTGAGCCCGAAA",72.3334733550413,3.01124550971814 -"Sample_2_CAACCAATCCTTGGTC",-76.1571210077404,5.35413920964475 -"Sample_2_CAACTAGCATGGTAGG",-67.6912654112031,-28.8146386416819 -"Sample_2_CAACTAGTCGCATGGC",38.791395345542,-32.8482852885715 -"Sample_2_CAAGAAAAGACTAAGT",-93.9905879265052,-36.6812955365586 -"Sample_2_CAAGAAAAGTTAGCGG",-45.1832084941868,1.65736879134832 -"Sample_2_CAAGAAATCTATGTGG",-54.8424793289731,-4.30869607055881 -"Sample_2_CAAGATCGTTACTGAC",-2.00423394361312,-75.3792057384819 -"Sample_2_CAAGATCGTTCAACCA",-65.015102985024,17.6523965549692 -"Sample_2_CAAGATCGTTCAGCGC",102.555674626581,-5.15539326120866 -"Sample_2_CAAGATCGTTTAGGAA",20.2397566077408,109.81166252577 -"Sample_2_CAAGTTGGTAACGCGA",13.2981974150805,45.7923927360539 -"Sample_2_CACAAACAGTTCGATC",76.1487926699372,-35.5647288395561 -"Sample_2_CACACAACAATCACAC",30.5109751711409,102.617993038428 -"Sample_2_CACACAAGTCGCGTGT",45.0714682208814,51.1663463497925 -"Sample_2_CACACCTCAAACCTAC",-99.4661562372183,-52.2289990962234 -"Sample_2_CACACCTTCCCATTTA",-53.6483924781071,10.5206697173287 -"Sample_2_CACACTCCAGTACACT",-83.3869837671664,-45.9864881969844 -"Sample_2_CACACTCCATGTAAGA",49.5140377272212,117.463045810762 -"Sample_2_CACACTCTCTAACCGA",18.4082472427954,-41.294458168249 -"Sample_2_CACAGGCGTGGGTATG",-95.3561748844378,-3.47072652380342 -"Sample_2_CACAGTAAGTGACATA",5.98402852118211,-72.587640409682 -"Sample_2_CACATAGCAAGCCGCT",20.7669066093585,117.860697319683 -"Sample_2_CACATAGGTAGCAAAT",-83.4473066390392,-47.2182427506695 -"Sample_2_CACATAGTCGCGTTTC",18.9746288688938,57.8173600631985 -"Sample_2_CACATTTCATTGCGGC",35.3748890029412,59.0415227573375 -"Sample_2_CACATTTTCCGTCATC",21.329836893422,94.1100182279629 -"Sample_2_CACCACTCACTCGACG",-13.2793313651384,-22.8445526590695 -"Sample_2_CACCACTTCATACGGT",-107.13524398046,-28.5544976588451 -"Sample_2_CACCACTTCCGCAAGC",-95.8239989443492,5.73839952000493 -"Sample_2_CACCACTTCTTGAGAC",-71.9762160630128,-18.2968011438405 -"Sample_2_CACCAGGCACCTCGTT",8.40389569806973,-87.3710554557508 -"Sample_2_CACCAGGTCAAGGTAA",-21.8993614843032,-39.2188759725057 -"Sample_2_CACCTTGTCTCTAAGG",48.3193324078136,21.7502459865607 -"Sample_2_CAGAGAGCATCACGTA",42.5438516817677,23.9540969881877 -"Sample_2_CAGATCAAGATATGGT",92.8391274896576,-58.6629833888206 -"Sample_2_CAGCAGCGTCGCCATG",89.3512439409961,33.2183429600317 -"Sample_2_CAGCAGCTCGGAATCT",-24.489741317223,-180.625519509696 -"Sample_2_CAGCAGCTCTGTCCGT",73.3976681333784,-12.8239684473689 -"Sample_2_CAGCCGAAGCGACGTA",-73.5937940820227,-15.8031429426359 -"Sample_2_CAGCGACAGTAGCGGT",-61.8024694971281,-19.2832387353884 -"Sample_2_CAGCTGGAGTGTACGG",-95.5437740572115,15.2026378986267 -"Sample_2_CAGCTGGTCTTTCCTC",-92.8842829087047,-7.82861137913713 -"Sample_2_CAGTAACCATCTCGCT",2.02056939697367,-27.4830460883362 -"Sample_2_CAGTAACTCTGTCCGT",43.9329675543369,89.5831688221999 -"Sample_2_CAGTCCTAGACAAGCC",-64.843687506069,-37.99053678497 -"Sample_2_CAGTCCTAGATCTGCT",16.8854468974652,92.8682322794311 -"Sample_2_CAGTCCTAGTAGATGT",69.6341457665907,-52.4833767217806 -"Sample_2_CAGTCCTCAGGCGATA",109.196036432124,-15.0830246847583 -"Sample_2_CAGTCCTGTTCAGCGC",61.6945177023332,-39.7203773387964 -"Sample_2_CAGTCCTTCTCTTGAT",75.3618697712686,-42.43316375315 -"Sample_2_CATATGGTCTGTGCAA",-28.2016939534663,-106.146352673083 -"Sample_2_CATATTCCAGTGACAG",16.2575482591645,54.7813841145651 -"Sample_2_CATATTCGTAAATACG",72.1928093402893,15.4428230227137 -"Sample_2_CATCAAGAGGTGTTAA",35.0611165679325,15.3253006805136 -"Sample_2_CATCAAGCACGACGAA",105.186225100431,-57.030842799429 -"Sample_2_CATCAAGCAGGCAGTA",-69.8097633873697,20.9174140153688 -"Sample_2_CATCAAGCATGTTGAC",22.4401457097287,-12.7680676820096 -"Sample_2_CATCAAGGTCTCCACT",-78.3606505919806,35.6523573574702 -"Sample_2_CATCAGAAGAAACCAT",35.7575152974055,22.9497647532886 -"Sample_2_CATCAGACACCCTATC",106.759942587708,-23.77457448364 -"Sample_2_CATCAGAGTCCATGAT",-107.027232116208,-13.8779238615118 -"Sample_2_CATCAGAGTCCGAAGA",1.23824666047926,-90.272586983984 -"Sample_2_CATCCACGTCCCTTGT",29.9434252833509,18.2593110869226 -"Sample_2_CATCCACGTGCATCTA",37.6654468005739,50.5186116258791 -"Sample_2_CATCCACTCTGAGGGA",-88.5420085486677,13.4646920845145 -"Sample_2_CATCGAAAGGAATCGC",26.6559270114917,57.8026033334795 -"Sample_2_CATCGAACAGGCTGAA",2.18272312176757,103.434489174893 -"Sample_2_CATCGAACAGGGAGAG",84.7388056128043,14.6708591994791 -"Sample_2_CATCGGGCAAAGGTGC",-68.5522274893598,-33.9327630167902 -"Sample_2_CATGACAAGTGAACGC",-12.6805147423576,-38.7782526230614 -"Sample_2_CATGACACACAGACTT",-96.4522495010386,-1.98169122859879 -"Sample_2_CATGACACACCGTTGG",62.5236635791816,-52.2390239349882 -"Sample_2_CATGACACAGCTATTG",-87.2928778061353,34.3683002970313 -"Sample_2_CATGACAGTCTCACCT",64.965669109653,-44.0687225007499 -"Sample_2_CATGCCTCAATGAAAC",-78.699769884245,12.0271317317459 -"Sample_2_CATGGCGAGTGTGGCA",-56.9676807380179,-12.6442091926522 -"Sample_2_CATGGCGCACGAGGTA",-71.4069933187253,-14.665998837032 -"Sample_2_CATGGCGGTACAGTGG",-94.2542531610328,-23.312052536826 -"Sample_2_CATTATCAGGGTTCCC",-97.750067905407,30.7731438270352 -"Sample_2_CATTATCCACAGCCCA",1.71592765887126,74.2504589219486 -"Sample_2_CATTATCGTGGTCTCG",91.1108411241359,-20.2136884381643 -"Sample_2_CCAATCCGTCTCTTAT",51.4510460759797,117.42981009992 -"Sample_2_CCAATCCTCTCATTCA",27.8367785461738,108.499342569043 -"Sample_2_CCACCTATCTTGCCGT",-115.387390945346,-48.1894937884592 -"Sample_2_CCACGGACAATCACAC",-80.0063369661726,27.8132799477638 -"Sample_2_CCACGGATCAGGCAAG",-26.1826676454443,-103.927052699434 -"Sample_2_CCACTACAGTGGACGT",51.7186782570878,-23.2723017810809 -"Sample_2_CCAGCGAGTTCGTTGA",70.9252932064042,-5.55969024155602 -"Sample_2_CCATTCGCAAGCCATT",18.9928844399689,83.0686173725665 -"Sample_2_CCCAATCTCAACACCA",91.4167986214178,-10.0307997209324 -"Sample_2_CCCTCCTAGTAGCCGA",-51.6319210064306,5.16722692430543 -"Sample_2_CCCTCCTGTTGTACAC",22.0844064772876,117.662684027123 -"Sample_2_CCGGTAGAGTTACGGG",25.9621919445723,111.947180986424 -"Sample_2_CCGGTAGTCGCCTGAG",-81.5023823853364,-43.4607280622041 -"Sample_2_CCGTACTCAGGGTATG",94.6749010802841,-39.2504845403321 -"Sample_2_CCGTACTTCTACTATC",-49.982595975478,13.7679838438343 -"Sample_2_CCGTGGAAGCTGAAAT",76.4731019849435,9.60531300087465 -"Sample_2_CCGTTCAAGCCCGAAA",-108.421466270595,-23.6509504251792 -"Sample_2_CCTAAAGAGCGATTCT",76.2250162167878,-29.1814340746902 -"Sample_2_CCTAAAGCAAGTAATG",91.2245447281359,-15.0321137374171 -"Sample_2_CCTACACAGCAAATCA",63.2635780925405,6.41627010558507 -"Sample_2_CCTACACTCTTTACGT",-95.1058628565292,35.9352709781193 -"Sample_2_CCTACCACATCGGAAG",-4.13701846469505,-55.6207106893782 -"Sample_2_CCTAGCTAGAGGTACC",29.9769212396074,112.797476489974 -"Sample_2_CCTATTATCAAACCAC",77.9147496661546,11.6407337465333 -"Sample_2_CCTCTGAGTAAGAGGA",-10.9650741192546,-66.6297104285508 -"Sample_2_CCTCTGAGTTATTCTC",77.7404306147982,9.46764098193801 -"Sample_2_CCTCTGATCAGCCTAA",-94.0386088794122,-20.6930518868732 -"Sample_2_CCTTACGAGCACCGCT",-110.471938421913,12.2966843689008 -"Sample_2_CCTTACGGTCGCATCG",-59.9420738867955,-25.1084207902005 -"Sample_2_CCTTCCCCAAACCTAC",47.2927873289247,110.179329522944 -"Sample_2_CCTTCCCTCACTGGGC",5.93534235948758,100.592238214503 -"Sample_2_CCTTCGAAGCGTTTAC",-63.2918050242399,16.6336805498799 -"Sample_2_CCTTCGACAAGAAGAG",96.1059170018883,-12.1873286613464 -"Sample_2_CGAACATAGTCAAGCG",-59.7120988190599,-21.85555409732 -"Sample_2_CGAACATCATCAGTCA",66.4399603747447,-65.2213532419618 -"Sample_2_CGAATGTGTCATTAGC",87.1498599273918,29.4697254552814 -"Sample_2_CGACCTTAGCACCGCT",33.5356320602669,19.4047614742675 -"Sample_2_CGACCTTAGTTGTAGA",-25.4386365687073,-184.76118626362 -"Sample_2_CGACCTTTCAAGCCTA",3.15516239393964,-61.6213694767514 -"Sample_2_CGACTTCTCATAGCAC",-84.7976393091939,12.6891291219454 -"Sample_2_CGAGCACAGACGCAAC",-111.666896221176,-4.74401063834373 -"Sample_2_CGAGCACAGCTCAACT",105.343531444648,-60.2503709686288 -"Sample_2_CGAGCACAGGCGTACA",0.304152307544562,111.248359407506 -"Sample_2_CGAGCACTCTGCAAGT",8.99256061646339,104.80769115617 -"Sample_2_CGAGCCACATGCAACT",2.7709783276886,94.5884176536045 -"Sample_2_CGAGCCATCGTGGTCG",24.3133753538324,3.84366445935397 -"Sample_2_CGATCGGAGACGCTTT",10.4568628305693,-63.1639102912434 -"Sample_2_CGATCGGGTCCCTTGT",-0.883610158835556,-47.9086919213216 -"Sample_2_CGATGGCTCGGTCCGA",50.9565334360902,115.10864674011 -"Sample_2_CGATGTAAGGACATTA",70.1999196631634,-18.3334070003253 -"Sample_2_CGATTGAGTGGTACAG",-52.1969997713084,-20.6110767442028 -"Sample_2_CGCCAAGAGACCACGA",10.8153985059224,-82.1868206552615 -"Sample_2_CGCCAAGGTGTCCTCT",-100.752745280147,44.0469483562986 -"Sample_2_CGCGGTAAGGCATGGT",-69.0092208265765,15.8256270086168 -"Sample_2_CGCGGTAAGTATCGAA",-112.615902098459,-7.98611778365489 -"Sample_2_CGCGGTACACAGGCCT",-51.1281199065671,-37.0279488464777 -"Sample_2_CGCGGTATCAACGCTA",-62.4780374137072,-13.9722794952501 -"Sample_2_CGCGGTATCTCTGCTG",20.6096818918795,11.3089565856211 -"Sample_2_CGCGTTTTCCAGATCA",25.3797045971879,-33.4686842998355 -"Sample_2_CGCTATCCACTGTCGG",7.99587993592499,107.123272898918 -"Sample_2_CGCTATCGTGCAGACA",-99.9921385974769,0.736446283851711 -"Sample_2_CGCTATCTCGCCATAA",-79.0964846583486,-24.9800985914166 -"Sample_2_CGCTATCTCTGTACGA",30.3488783210339,11.7848904769014 -"Sample_2_CGCTGGATCCTAGGGC",32.1847655612033,57.8006940963359 -"Sample_2_CGCTTCACATTTCAGG",71.629018644248,-39.4250637257747 -"Sample_2_CGGACACAGCAGCGTA",24.1609781989057,80.9468271248137 -"Sample_2_CGGACACAGGTGCTTT",-76.5400931588254,-34.6557935097408 -"Sample_2_CGGACACTCCTCGCAT",1.10378702916655,-11.86504246822 -"Sample_2_CGGACGTTCAGGTTCA",73.1702310041421,-22.3956506237465 -"Sample_2_CGGACTGTCTCTGAGA",40.4909885582503,57.3990862838384 -"Sample_2_CGGAGCTCAAACTGTC",57.3190125246668,-52.625362167537 -"Sample_2_CGGAGCTGTACCGTTA",76.8907816528734,1.46091725850701 -"Sample_2_CGGAGTCTCAGGCCCA",-82.6845265514309,-39.5592217552305 -"Sample_2_CGGCTAGCAATCTGCA",-35.4892389904555,-20.9757910517344 -"Sample_2_CGTAGCGCAAACAACA",-52.9311182405501,-48.1297286511301 -"Sample_2_CGTAGCGTCAGCACAT",-66.9394114712585,-3.96503851991901 -"Sample_2_CGTAGCGTCCTAGAAC",-24.9826758164927,-107.779129640975 -"Sample_2_CGTAGGCGTCTGATTG",41.6015537709365,62.4347717802888 -"Sample_2_CGTCAGGCAAGCTGTT",-88.3714981457221,-37.6913615617295 -"Sample_2_CGTCAGGTCAAGGTAA",14.1385632412146,80.8229451689673 -"Sample_2_CGTCCATAGCTATGCT",-83.5152187102333,-11.8524375965732 -"Sample_2_CGTCCATCACCCTATC",77.5889455459165,-38.0423706448052 -"Sample_2_CGTCTACCATAGGATA",38.6286169392532,108.041036799141 -"Sample_2_CGTGAGCCATTCTTAC",87.565035746603,20.1836658645147 -"Sample_2_CGTGAGCTCAATCTCT",-26.0596044318065,-180.689949953284 -"Sample_2_CGTTGGGTCAGAGACG",72.7256259632439,-0.740780563431783 -"Sample_2_CGTTGGGTCCTTGCCA",20.3732313849024,35.8020528513339 -"Sample_2_CTAACTTAGCCCAGCT",86.9252640668774,-29.0828040679646 -"Sample_2_CTAAGACAGTGTCTCA",-110.074592603753,-36.5710961496867 -"Sample_2_CTAAGACCAGATCGGA",84.8201997425297,2.17553556439142 -"Sample_2_CTAAGACGTCGGATCC",-23.4423735100486,-106.811959011746 -"Sample_2_CTAAGACTCCACTGGG",-18.1162749025065,-40.9562432632989 -"Sample_2_CTAATGGCACAGACTT",28.5593257622604,96.0722805204608 -"Sample_2_CTAATGGGTTCAGGCC",-67.6308671026787,-15.4887381805983 -"Sample_2_CTACACCCAACGCACC",-106.307450378886,20.5540766458342 -"Sample_2_CTACACCGTTCAGCGC",-56.3783808322076,14.801016109474 -"Sample_2_CTACACCTCGGTTCGG",66.0385366477794,-37.3279347997629 -"Sample_2_CTACATTCATGCCCGA",-90.904214870309,16.4492318410518 -"Sample_2_CTACATTGTTTGGCGC",-64.8951929749483,24.5994510742337 -"Sample_2_CTACCCAAGGTGCACA",-31.2202542725904,-98.0781768020764 -"Sample_2_CTACCCACAAGGTGTG",29.8275869670576,114.747651826282 -"Sample_2_CTACCCACACGACTCG",-55.5539336614803,-18.7996359833412 -"Sample_2_CTACCCACAGCGATCC",-42.4075061298332,-10.7651934343537 -"Sample_2_CTACCCATCCAAATGC",-29.827958690139,12.3878849416273 -"Sample_2_CTACGTCAGAGCTGCA",34.3526390919,91.114855890264 -"Sample_2_CTACGTCAGGCGATAC",103.176473170504,-29.2248860658444 -"Sample_2_CTACGTCGTCTCAACA",-83.5514738740249,-7.22797932763185 -"Sample_2_CTACGTCTCGTCGTTC",-90.694702426502,-19.672057547178 -"Sample_2_CTAGAGTCAATCTACG",-100.036646353866,-5.07893737412098 -"Sample_2_CTAGAGTCAGGAATCG",2.34040481762294,-56.6876648141038 -"Sample_2_CTAGAGTTCAACTCTT",91.7784253379175,-30.9582330989813 -"Sample_2_CTAGTGACAGACGCTC",23.3090705048924,60.1378572590458 -"Sample_2_CTAGTGACAGCGTAAG",22.3174229358135,33.7961552449248 -"Sample_2_CTAGTGACATCTATGG",31.7475773647868,47.8672392044288 -"Sample_2_CTAGTGATCCTTGACC",45.6552874061882,104.809328289153 -"Sample_2_CTCACACTCACATACG",68.1359856733347,-24.9841861606051 -"Sample_2_CTCAGAATCGGAAATA",11.526651962013,-61.4126639381925 -"Sample_2_CTCATTAAGTCGTTTG",40.7689729518408,67.6267513828879 -"Sample_2_CTCCTAGGTCATACTG",-35.5828669594154,-18.7261619417712 -"Sample_2_CTCCTAGTCGGCGCTA",13.7489777533234,-72.6704219347272 -"Sample_2_CTCGAAAAGAACAATC",39.9220733162818,11.3423930547943 -"Sample_2_CTCGAAAAGACGACGT",3.8536718801077,-8.90134651277785 -"Sample_2_CTCGAAACACAGCGTC",-28.8438198363385,-9.25527046496449 -"Sample_2_CTCGGGAGTTCGGCAC",48.2223062692708,50.6154393115096 -"Sample_2_CTCGTCACACACTGCG",-111.832408136362,-33.7293652781376 -"Sample_2_CTCGTCACACAGGAGT",-100.763745928526,-31.3764588761678 -"Sample_2_CTCGTCAGTTCCACGG",87.3493291627111,-57.5029990220096 -"Sample_2_CTCTAATAGTCCGTAT",-104.536773527548,-44.6663696682499 -"Sample_2_CTCTAATAGTGGGTTG",82.0086298079128,-46.7488934235615 -"Sample_2_CTCTACGAGGTGCACA",75.7227179544463,-46.0929856379538 -"Sample_2_CTCTACGTCGAACGGA",107.667978138167,-51.4775776726787 -"Sample_2_CTCTGGTAGCGATATA",78.3476313380225,-29.508174927512 -"Sample_2_CTCTGGTAGGGAACGG",36.8217251126408,84.0334382175298 -"Sample_2_CTCTGGTAGGTCGGAT",81.9319694913344,2.07866940772649 -"Sample_2_CTCTGGTCACTGCCAG",63.7215836102371,-63.0409312422162 -"Sample_2_CTGAAACGTGCCTGTG",-55.2749995648833,-35.7593821297993 -"Sample_2_CTGAAGTCACGAAGCA",20.4090482954034,107.045314508001 -"Sample_2_CTGAAGTTCCGCAAGC",-100.79410512651,10.9415798631291 -"Sample_2_CTGAAGTTCGCAAACT",13.6340088499703,117.504310199017 -"Sample_2_CTGATAGAGCGTCAAG",55.8202676609929,107.903304879112 -"Sample_2_CTGATCCTCATAACCG",104.350517146273,-44.8997185283634 -"Sample_2_CTGATCCTCGCAAGCC",18.1415460374666,95.1571762328262 -"Sample_2_CTGATCCTCTACTATC",23.8132506657009,55.9749363910118 -"Sample_2_CTGCCTAAGAAGGTGA",-70.8359207577355,-32.6056104548486 -"Sample_2_CTGCCTAAGACAGAGA",20.9023032872108,-12.668358585233 -"Sample_2_CTGCCTACAGACAGGT",-77.5928492913045,30.2159125678065 -"Sample_2_CTGCTGTCAACGATCT",-64.8002424159352,-49.2699463365599 -"Sample_2_CTGGTCTCAACCGCCA",89.9661520505382,-61.0873510997028 -"Sample_2_CTGGTCTGTATCAGTC",-97.2189320981651,-36.9054762124106 -"Sample_2_CTGGTCTGTTGACGTT",84.7147830105523,15.4393050613973 -"Sample_2_CTGGTCTGTTGTCGCG",25.3735786350065,107.680896477244 -"Sample_2_CTGTGCTCACATGGGA",45.4462856781658,111.992387935837 -"Sample_2_CTGTGCTCACGGCCAT",75.2268806589477,-26.1404157466255 -"Sample_2_CTGTGCTTCAGCGATT",71.9329453274023,-10.3984879196972 -"Sample_2_CTTAACTGTGGACGAT",34.2731970092244,90.2554009458703 -"Sample_2_CTTACCGAGCGTCAAG",84.334084406959,-20.8527808816684 -"Sample_2_CTTAGGAAGCCAACAG",28.7721975325572,104.287511462589 -"Sample_2_CTTCTCTAGTATCGAA",81.560554634838,-37.0724173226306 -"Sample_2_CTTCTCTAGTGCGATG",-112.397749936512,-2.26740365273882 -"Sample_2_CTTTGCGTCGGGAGTA",86.0662322289295,-0.0390564139446654 -"Sample_2_GAAACTCAGTACGTTC",2.69891078767332,2.57457646109314 -"Sample_2_GAAACTCGTCTGCCAG",19.8890690552665,-40.2854485719418 -"Sample_2_GAAATGAAGAAGGGTA",76.6192082318326,-32.2277387032318 -"Sample_2_GAAATGAGTGTTAAGA",72.7733015519336,-12.3795428858787 -"Sample_2_GAACATCTCCTTGGTC",46.1648191422208,72.9195024609701 -"Sample_2_GAACCTACAACACCCG",32.8921182935667,107.296708393127 -"Sample_2_GAACGGAAGCAAATCA",97.0120489701654,-17.2367292710436 -"Sample_2_GAAGCAGGTTCAGGCC",-72.8396961795991,2.58618050408834 -"Sample_2_GAAGCAGTCTGTGCAA",-38.1792864621122,-14.2703358088675 -"Sample_2_GAATAAGAGCGATTCT",73.1812936371177,-17.6965686075656 -"Sample_2_GAATAAGAGTTGAGAT",-79.6552008765688,-32.8115276163913 -"Sample_2_GAATAAGGTACCGTTA",49.623260065052,113.606837647264 -"Sample_2_GAATAAGTCCACTCCA",-105.132271363611,-36.9289391574045 -"Sample_2_GACACGCCACGACGAA",78.0806823033396,-13.0807981210196 -"Sample_2_GACACGCGTAACGTTC",16.9266566006491,6.89395056482583 -"Sample_2_GACAGAGAGGCGTACA",-18.3534437032472,-36.6993092662835 -"Sample_2_GACAGAGTCGAGAACG",0.869410500647163,-84.2734982104749 -"Sample_2_GACCAATGTGAGCGAT",7.93807211149628,-16.4053757386465 -"Sample_2_GACCTGGCACTTCGAA",-89.3412144780105,10.9241596104765 -"Sample_2_GACCTGGCAGTAAGAT",-7.42825082068758,-82.5024966888261 -"Sample_2_GACGGCTGTTGATTGC",-76.6461929657446,-46.164875377739 -"Sample_2_GACGGCTGTTTGGGCC",76.2444026659497,3.76462000798629 -"Sample_2_GACGGCTTCAACGCTA",86.7832856716791,-33.1009892772107 -"Sample_2_GACGTGCTCACCATAG",1.78786759624339,-80.7332607534376 -"Sample_2_GACGTTAAGAATAGGG",-103.995065771999,-19.1829989551779 -"Sample_2_GACGTTAAGTGTACGG",25.1566886292795,103.462979806266 -"Sample_2_GACTACAAGTACACCT",88.9276810883942,-2.86615491449312 -"Sample_2_GACTACATCGGAATCT",-114.866434979635,7.5592736046047 -"Sample_2_GACTGCGGTTCGGCAC",98.8471401636628,-46.109134952606 -"Sample_2_GACTGCGTCAGTTGAC",31.3143160998927,91.6805848791753 -"Sample_2_GACTGCGTCTGTACGA",16.8720989220519,90.3468772754852 -"Sample_2_GAGCAGAAGTGCTGCC",18.4423207241408,77.6671070527987 -"Sample_2_GAGCAGAGTCTTGATG",-52.7794198733007,-31.0738199783626 -"Sample_2_GAGGTGAGTCCTAGCG",-0.521994425456297,75.5659659834032 -"Sample_2_GAGGTGATCCCTAACC",-27.3882223450448,-18.7117340460198 -"Sample_2_GATCAGTAGAGTTGGC",29.0447511304343,106.648034298805 -"Sample_2_GATCAGTAGTAGCGGT",17.0272223786607,105.30616671684 -"Sample_2_GATCAGTCAAAGCGGT",37.216556439277,100.430693313827 -"Sample_2_GATCAGTTCCGAGCCA",-86.4500604702495,-22.9449492360712 -"Sample_2_GATCAGTTCCTCGCAT",-73.4956008694731,-37.4320087391278 -"Sample_2_GATCGATGTTCCCGAG",31.6869952855124,98.787660021706 -"Sample_2_GATCGCGAGACCTTTG",71.8157078333267,-56.6310238455046 -"Sample_2_GATCGCGCAAGAAAGG",-28.3256294532793,-99.8075429672565 -"Sample_2_GATCGCGGTCAACTGT",-55.2570516571473,25.3919712351419 -"Sample_2_GATCGCGTCAAGGTAA",-29.9751924967951,3.15792773399594 -"Sample_2_GATCGTAAGGTACTCT",35.1629330125572,107.344037095862 -"Sample_2_GATGAAAAGACACTAA",31.1848865907548,120.083647056409 -"Sample_2_GATGAAATCGTCTGAA",-100.07732957337,-29.0168254728256 -"Sample_2_GATGAGGTCCTAGGGC",-58.1957883789496,-8.56151667779901 -"Sample_2_GATGCTAAGTCCCACG",19.6914125535086,96.5039770962459 -"Sample_2_GATGCTACAAAGGAAG",67.9025914296898,-49.6780074363354 -"Sample_2_GATGCTAGTAGAGGAA",105.200887960654,-20.7647583647186 -"Sample_2_GATTCAGAGACAGGCT",19.4150505572773,-16.5875691691393 -"Sample_2_GCAAACTCATCCCATC",1.52042467632628,-45.2886350756634 -"Sample_2_GCAAACTGTGGACGAT",-55.6383687593508,-9.01891423666433 -"Sample_2_GCAATCAAGTAGGCCA",-80.8425857858397,39.9298892812974 -"Sample_2_GCAATCAGTCTAGTCA",-64.3170981039086,11.4708176887037 -"Sample_2_GCAATCATCACCCTCA",-101.984349377613,-10.6795047431363 -"Sample_2_GCAGTTACAACGATGG",-116.076730900497,-40.8126312407139 -"Sample_2_GCAGTTAGTTCAGCGC",45.5159848061768,108.867769951586 -"Sample_2_GCATACATCCTGCCAT",76.9855983087331,-65.646930033515 -"Sample_2_GCATACATCTTTAGGG",-100.423426980013,29.7834110530596 -"Sample_2_GCATGCGGTCCCTTGT",44.5868095424321,15.4230641059943 -"Sample_2_GCATGTAAGACGCACA",80.6009119544223,-10.6231218214519 -"Sample_2_GCATGTAAGAGTAATC",16.6931381629626,-70.1676478152505 -"Sample_2_GCATGTACACCTCGTT",-12.2157327226914,-79.7586781042474 -"Sample_2_GCCAAATAGTGGAGAA",96.4843963061654,-61.8426212053351 -"Sample_2_GCCTCTACAAACTGCT",27.732057925889,-28.5801919759278 -"Sample_2_GCGACCACATGCCCGA",77.5385053652263,-61.2937787364044 -"Sample_2_GCGAGAAAGCTACCGC",26.8324731823849,115.390310318956 -"Sample_2_GCGAGAACAAGGCTCC",83.5454847145559,-10.6529193542232 -"Sample_2_GCGAGAACACCAGTTA",-116.404687200906,-16.5410993657621 -"Sample_2_GCGCAACAGGGTGTGT",17.093699646006,31.6709918097113 -"Sample_2_GCGCAACCACCCAGTG",-78.3616211584226,-17.9280890710718 -"Sample_2_GCGCAACTCCTATGTT",92.7839254579909,28.0907805531061 -"Sample_2_GCGCAACTCTTGCATT",-66.2015311389229,-44.1796369849533 -"Sample_2_GCGCAGTTCCGTCATC",-11.2098830199787,-71.4791241808875 -"Sample_2_GCGCCAACACCGTTGG",-2.22964876985446,76.0906684143865 -"Sample_2_GCGCGATTCCGTAGTA",-92.468646894338,19.0433118034512 -"Sample_2_GCGGGTTGTGCGCTTG",19.6621778976312,33.4884486731818 -"Sample_2_GCTCCTAGTAGAAGGA",-1.68833700023426,-68.339963251694 -"Sample_2_GCTCTGTCAAGTCTAC",-93.9697050435073,-40.9232577308246 -"Sample_2_GCTGCAGAGTGGGATC",87.2106693869223,-1.02619176478298 -"Sample_2_GCTGCAGCACCACCAG",-71.2910048049991,-25.5325116125562 -"Sample_2_GCTGCAGGTCGATTGT",-95.5505970379653,-9.96331706337319 -"Sample_2_GCTGCAGTCCTTTACA",33.6629116297649,-32.5855738636442 -"Sample_2_GCTGCAGTCGGAGGTA",-57.0313889523808,21.8067860905162 -"Sample_2_GCTGCGAAGGCTACGA",-9.50915027129033,-80.2989119104886 -"Sample_2_GCTGCGAGTGTCAATC",46.1540713724374,98.0801859447487 -"Sample_2_GCTGCGATCTGATACG",-77.5516199938004,-18.7469413443174 -"Sample_2_GCTGGGTCATATGCTG",-86.7685297755065,-47.6861443914275 -"Sample_2_GCTTCCACACATCCGG",87.479068164771,-65.3591599517815 -"Sample_2_GCTTCCACATCTATGG",-51.3265256872801,0.48426147408617 -"Sample_2_GCTTCCATCTCTGTCG",31.0746566706268,6.49993959094539 -"Sample_2_GCTTGAACAGTATAAG",100.245601255469,-32.0381946854063 -"Sample_2_GCTTGAATCTAACCGA",-84.8588717116277,-32.4005683944568 -"Sample_2_GGAAAGCTCCTTGCCA",-74.6140712091129,-10.202642389104 -"Sample_2_GGAACTTCAGATGGCA",73.3236390430566,-52.745350367006 -"Sample_2_GGAATAACAAATTGCC",-60.4950191533717,21.4377510803603 -"Sample_2_GGAATAACAGTTCATG",19.4045587465147,91.4509144282011 -"Sample_2_GGACATTCACTTCGAA",-92.298590904399,31.7346247164231 -"Sample_2_GGAGCAACAAGCGCTC",99.7342077012593,-15.8634551715573 -"Sample_2_GGAGCAACATCTCCCA",39.8820843411981,16.0266070432326 -"Sample_2_GGAGCAATCGCATGGC",-34.440862760619,20.7409088108922 -"Sample_2_GGAGCAATCGTAGATC",-34.7959212733758,-6.31211382314652 -"Sample_2_GGATGTTAGTCCGTAT",12.0082916247802,96.5276526506946 -"Sample_2_GGATTACCATTGAGCT",-73.5093845237671,-40.7679191680236 -"Sample_2_GGATTACTCACCAGGC",32.3663249365128,15.8235283737383 -"Sample_2_GGCAATTAGAGGTAGA",70.3739521848171,-51.9454047466129 -"Sample_2_GGCAATTAGTGTTGAA",11.7192185928041,-65.1365369804194 -"Sample_2_GGCAATTCAGATTGCT",13.3658848909299,4.71619915105575 -"Sample_2_GGCAATTGTCAAACTC",-82.9369389606068,-15.5359074604187 -"Sample_2_GGCAATTGTGATAAGT",84.6403136853249,-63.6819431909947 -"Sample_2_GGCCGATGTTTCCACC",-77.1570226257798,2.25645276272687 -"Sample_2_GGCGACTCATCCTAGA",-112.211324876119,-14.9763265184619 -"Sample_2_GGCGACTCATCCTTGC",31.5066492817018,53.225693874666 -"Sample_2_GGCGTGTCACATGACT",-79.9023939527594,-9.50787325958597 -"Sample_2_GGGAATGAGATGGCGT",35.9087618249432,123.768002740239 -"Sample_2_GGGAATGGTCATCCCT",37.8185389037584,96.6786358635649 -"Sample_2_GGGACCTTCTTGGGTA",-9.68053957515961,-71.0087257189432 -"Sample_2_GGGAGATAGTGTTTGC",38.3861426834919,103.325152102379 -"Sample_2_GGGAGATCAGATGGCA",26.0016424494308,6.74034517078589 -"Sample_2_GGGAGATCATACCATG",22.0950976451944,100.018265277603 -"Sample_2_GGGAGATGTCAAACTC",-69.9951481918666,38.0128204567091 -"Sample_2_GGGATGAGTCGTCTTC",93.3522287213369,-25.008802074976 -"Sample_2_GGGATGATCAGTTCGA",78.9957160099598,-5.95756294091934 -"Sample_2_GGGCACTAGCAGGTCA",-67.9938192014718,14.2739918303358 -"Sample_2_GGGCACTCAAAGCAAT",9.07199079259199,-4.60892545744834 -"Sample_2_GGGCACTTCCTAGGGC",23.0728558833423,101.90804708443 -"Sample_2_GGGCACTTCTATCGCC",-95.5705633611555,-27.594686704374 -"Sample_2_GGGCATCCACGGCGTT",-116.24888326068,-23.6110922423432 -"Sample_2_GGGCATCCATCACAAC",8.39306347241539,8.60026954319619 -"Sample_2_GGGCATCGTTCCTCCA",96.6832537153365,-46.0191045788016 -"Sample_2_GGGCATCTCCGCATCT",69.2020270788433,-33.6541619211202 -"Sample_2_GGTATTGGTGAGGGTT",19.9744148156095,55.5511068636137 -"Sample_2_GGTATTGGTTCCCGAG",20.4012889701106,-1.8613913363381 -"Sample_2_GGTGAAGAGAGGTAGA",7.33548229593401,-37.6386031747911 -"Sample_2_GGTGAAGTCCATGAGT",64.6550787096058,-4.68865574994059 -"Sample_2_GGTGAAGTCCTCATTA",-63.9480435701545,-9.04104724205855 -"Sample_2_GGTGCGTAGAGTACAT",-119.365208560452,-1.59398749846274 -"Sample_2_GGTGCGTAGTACATGA",41.5910037328449,25.728093969996 -"Sample_2_GGTGTTAGTAGTAGTA",1.91037773408966,-72.6454063676299 -"Sample_2_GTAACGTAGGACAGCT",80.0546279640195,-18.624071194113 -"Sample_2_GTAACGTTCACTTACT",60.8157503330177,-19.9104449176751 -"Sample_2_GTAACTGCATCGGTTA",-58.7600251364444,9.42901550025575 -"Sample_2_GTAACTGTCCGAACGC",-32.007208491281,14.6885826442687 -"Sample_2_GTACGTAAGACACTAA",-22.3166161913082,15.595781105365 -"Sample_2_GTACGTACATGCTGGC",84.4748068932099,-21.3069084888602 -"Sample_2_GTACGTATCCTGTAGA",7.24478402806474,84.5535580060658 -"Sample_2_GTACTTTGTGTCAATC",44.7170704125687,57.8496870465139 -"Sample_2_GTAGGCCAGCGCTCCA",97.8764072071296,12.4111130954995 -"Sample_2_GTAGGCCGTAACGTTC",-93.0758324584294,-33.6835481177087 -"Sample_2_GTATCTTTCCTATGTT",87.249241744118,-22.034934821336 -"Sample_2_GTATTCTCAGCTGCTG",58.5730076031239,-8.80228661513022 -"Sample_2_GTATTCTGTCCCTACT",23.7631577381041,-18.1505292786341 -"Sample_2_GTATTCTTCCAATGGT",83.3753061862361,-30.4246105630096 -"Sample_2_GTCACAAGTCCGAAGA",32.6372446785828,104.788332743286 -"Sample_2_GTCACAAGTTCCACAA",-91.2155185724395,-24.0986111503513 -"Sample_2_GTCACGGCACCGGAAA",19.4045062365799,101.734108347748 -"Sample_2_GTCACGGGTTGCCTCT",93.418605718577,-54.8447205744544 -"Sample_2_GTCACGGTCAAGGCTT",12.8123590681709,-67.5309057277209 -"Sample_2_GTCATTTAGCTTTGGT",-102.772102755309,-25.6511636316615 -"Sample_2_GTCATTTTCAGTTTGG",81.6700857086838,-22.5885048985894 -"Sample_2_GTCCTCAAGTATCGAA",33.4594578369065,113.302739525324 -"Sample_2_GTCCTCAAGTGCCATT",104.240812379599,-39.8151753400525 -"Sample_2_GTCGGGTGTCCTCTTG",90.4947408929655,-1.5362586232807 -"Sample_2_GTCGTAATCCATGAGT",22.7587464179254,-5.4857574593025 -"Sample_2_GTCTCGTAGTTCGCAT",14.7176599990136,102.259632813682 -"Sample_2_GTCTTCGGTATAAACG",59.2119056270126,-56.3110785646423 -"Sample_2_GTGAAGGAGTACGACG",-55.4533782542825,-2.04866598809768 -"Sample_2_GTGAAGGCAATAACGA",-94.740957514511,-15.596797962655 -"Sample_2_GTGAAGGCATGTTGAC",88.659800971337,-6.22260766481648 -"Sample_2_GTGAAGGGTCCTCTTG",26.2304132395177,12.1929852825553 -"Sample_2_GTGAAGGTCATGTAGC",-18.203671071134,-38.7184922954221 -"Sample_2_GTGCAGCAGCGATTCT",-11.8366686026697,-34.8092600051271 -"Sample_2_GTGCAGCAGGCTACGA",85.6924235435694,-62.6516778308009 -"Sample_2_GTGCAGCTCAAAGACA",10.8456880720855,101.083334983293 -"Sample_2_GTGCATACATCCGGGT",-103.435501684228,27.1969525592538 -"Sample_2_GTGCATATCACCGTAA",-49.9656227595814,-24.7003373489754 -"Sample_2_GTGCATATCATTATCC",-104.923509129369,15.3530656415388 -"Sample_2_GTGCGGTAGAGGTACC",87.5779036419143,1.89903290367104 -"Sample_2_GTGCGGTCAAGCCGTC",-89.7571884226473,-30.5455926312099 -"Sample_2_GTGCGGTCACATTCGA",14.6560644852829,109.001669285762 -"Sample_2_GTGCTTCAGCCAACAG",-68.1431651069239,-11.9258711164191 -"Sample_2_GTGGGTCTCAAGAAGT",-15.4362758485238,-22.2409731662585 -"Sample_2_GTGTTAGCATCCTAGA",-89.1615401608888,21.3150883520398 -"Sample_2_GTGTTAGGTAGATTAG",87.3689874987761,-24.6088542214132 -"Sample_2_GTTACAGCATTAGGCT",21.9366346005608,97.6663147867288 -"Sample_2_GTTCATTCATGCATGT",-94.3434512775775,38.5880780301384 -"Sample_2_GTTCATTTCCAAACAC",29.351661114297,99.0778631859409 -"Sample_2_GTTCTCGTCTAGCACA",-89.2369576959627,-7.68023614847062 -"Sample_2_GTTTCTAAGATTACCC",16.7677591436186,13.4435361177955 -"Sample_2_GTTTCTAAGGTGCAAC",31.9433291326004,65.202243220514 -"Sample_2_GTTTCTACATCACGAT",31.8009179149608,109.74680186779 -"Sample_2_TAAACCGAGTTCGATC",-32.950900900556,-17.2902390050323 -"Sample_2_TAAACCGGTCTGCGGT",69.6068912903712,-16.8389315820683 -"Sample_2_TAAACCGGTTCGCGAC",48.2614621646377,-29.1812557378163 -"Sample_2_TAAGAGAGTACTTAGC",-46.6553348405134,-35.4511109893061 -"Sample_2_TAAGAGATCCGAAGAG",37.377662935817,108.656158689664 -"Sample_2_TAAGCGTAGGACTGGT",-62.4392017675377,-31.7806963552112 -"Sample_2_TAAGTGCCAGACAAAT",-95.2197810850975,40.2950325611051 -"Sample_2_TAAGTGCTCGCCATAA",-103.996179540478,8.73654745105397 -"Sample_2_TACACGAGTCACTTCC",-71.7533437448931,2.18776599430056 -"Sample_2_TACACGAGTTTCGCTC",-0.276594743991156,79.0223381343049 -"Sample_2_TACACGATCCCAGGTG",0.779307867757285,-40.814352752096 -"Sample_2_TACCTATGTCCAACTA",83.5612257481707,17.4974931602196 -"Sample_2_TACCTTAAGCTAACAA",-48.7116105949198,16.0299240847798 -"Sample_2_TACCTTATCTGGCGTG",-51.8245959919198,-52.3387161259518 -"Sample_2_TACGGGCCATCCTAGA",34.1090090303455,63.3092731648415 -"Sample_2_TACGGGCCATTACCTT",1.11871466202317,-7.1955893880532 -"Sample_2_TACGGTAAGGATGGAA",101.748550143177,-0.750553446598368 -"Sample_2_TACTCATTCGGCCGAT",-1.47709797136556,-33.0832179516847 -"Sample_2_TACTCGCCAACTGCGC",-78.0929641443895,-35.5378984464735 -"Sample_2_TACTCGCTCCGAATGT",34.6256320312196,109.141626502584 -"Sample_2_TACTCGCTCGTAGGTT",35.7549503980319,104.769054548941 -"Sample_2_TACTTACTCCGTTGTC",-91.134034932302,-50.1864539063798 -"Sample_2_TACTTGTGTACTTAGC",-99.191060868605,-19.7312755434906 -"Sample_2_TAGCCGGCAATGTTGC",67.167422767439,-30.0706516164679 -"Sample_2_TAGCCGGTCGAGAACG",26.0682955496106,-15.7634836704397 -"Sample_2_TAGGCATCAGTCCTTC",29.8033448895236,23.842013783567 -"Sample_2_TAGGCATGTTGTACAC",48.5168256500846,122.423280898496 -"Sample_2_TAGTGGTCAAGGGTCA",-87.3719052659842,0.0453109198156667 -"Sample_2_TAGTGGTTCAATACCG",5.13615232045009,108.809722318108 -"Sample_2_TAGTGGTTCCTAGTGA",90.4123246309725,-42.1420758033046 -"Sample_2_TAGTTGGAGTAGGCCA",-83.5980770614458,-1.93095837831749 -"Sample_2_TATCAGGCAACTGCTA",-58.8760049947165,-13.2387252496553 -"Sample_2_TATCAGGGTCGGATCC",61.3687131339005,-10.9151670239153 -"Sample_2_TATCTCAAGACTTGAA",-87.6556102435414,17.7235119677353 -"Sample_2_TATCTCAAGGGAGTAA",-104.952706303663,-31.2902749202207 -"Sample_2_TATCTCAAGGTCATCT",24.8863777394156,5.80840253914759 -"Sample_2_TATCTCAGTCTACCTC",24.0410896748235,-3.96305861934021 -"Sample_2_TATCTCATCATGTAGC",85.4443108189693,-39.3299852384501 -"Sample_2_TATGCCCCAAGCTGTT",52.8394092671251,-22.3011446773551 -"Sample_2_TATGCCCCAATCACAC",35.4757613661822,119.443246015012 -"Sample_2_TATTACCCAAACCCAT",21.3186838626201,81.0503221060575 -"Sample_2_TATTACCCACGAAAGC",11.7747315827449,86.3219203663961 -"Sample_2_TATTACCGTGATGTGG",-85.6413887802547,-54.0877034419219 -"Sample_2_TCAACGACAATCTACG",-84.410147292144,-19.1958576190479 -"Sample_2_TCAACGATCCTTTCGG",4.0085750199043,-45.8368683554758 -"Sample_2_TCAATCTGTGCAGACA",3.26146585988646,-68.6488212328169 -"Sample_2_TCAATCTGTGCGATAG",100.580291014994,-9.26971818095873 -"Sample_2_TCACAAGCAATCGAAA",-80.1926467755532,23.1143243225565 -"Sample_2_TCACAAGGTCTCATCC",-40.9055125680898,-28.054645012534 -"Sample_2_TCACGAACATACTACG",-1.32892235308266,-82.0786009193785 -"Sample_2_TCACGAAGTAGAAAGG",-1.1465221445051,-65.3701103375369 -"Sample_2_TCACGAATCATCGATG",89.3497666095103,-48.5684177839384 -"Sample_2_TCACGAATCGTCACGG",80.6587052109883,-31.9173989104893 -"Sample_2_TCAGATGAGTTGAGTA",31.2366122861682,60.1312120038165 -"Sample_2_TCAGCAAGTGCTTCTC",100.725746806243,-42.1510229486493 -"Sample_2_TCAGCTCAGACAGGCT",28.1582759944223,-8.78282724071465 -"Sample_2_TCAGCTCGTCGAGTTT",85.3422519091202,7.07209373696057 -"Sample_2_TCAGGATTCATTTGGG",88.7905132174184,-25.8744580648788 -"Sample_2_TCAGGTAAGTCCGGTC",6.39865724215061,-7.01630634395852 -"Sample_2_TCAGGTACAGGATTGG",9.56814853838066,-69.3607802588901 -"Sample_2_TCAGGTAGTCCGTCAG",-59.7185379830497,-23.7822459972148 -"Sample_2_TCAGGTATCGAGGTAG",-87.1447170191412,27.5061849603458 -"Sample_2_TCAGGTATCGCCTGTT",43.5634570616142,21.8641244304075 -"Sample_2_TCATTACCAGTATAAG",-78.4849666418217,33.8296186605229 -"Sample_2_TCATTTGAGGCAATTA",99.1757432942231,-11.7648895172816 -"Sample_2_TCATTTGAGTGGTAGC",7.60441126664441,-77.6861425148529 -"Sample_2_TCATTTGGTGTTGAGG",-5.37858638145993,-68.7016519896784 -"Sample_2_TCCACACAGTACACCT",17.2287670703563,97.4596659050745 -"Sample_2_TCCACACCAGTTTACG",-104.770646563726,-2.22628003042431 -"Sample_2_TCCCGATAGACAGGCT",31.1915912247555,88.0937242824338 -"Sample_2_TCCCGATCAAGTTCTG",-77.3729126264878,-19.4290516109265 -"Sample_2_TCCCGATTCGTAGATC",43.3633518594161,53.4807906609921 -"Sample_2_TCGAGGCGTAGAAGGA",15.89391644019,42.3992662797053 -"Sample_2_TCGAGGCGTTTACTCT",-34.9903453599226,-22.6530476022822 -"Sample_2_TCGAGGCTCCTCAACC",68.0859273548035,-44.3299180788181 -"Sample_2_TCGAGGCTCGTTGACA",-99.4871806116713,18.9790263604571 -"Sample_2_TCGCGAGGTCAGTGGA",22.7298288087677,-10.2113787097967 -"Sample_2_TCGGGACCAGACGTAG",98.979717850629,3.51315651049754 -"Sample_2_TCGGGACGTGATAAGT",-98.1083860390185,-23.2685578177329 -"Sample_2_TCGGGACTCACCCGAG",34.6890329470656,96.3993075076134 -"Sample_2_TCGGGACTCCGAAGAG",-92.0912166664162,-11.7216083499963 -"Sample_2_TCGGTAACACAGGCCT",66.4061619780072,-19.4046896889838 -"Sample_2_TCGGTAACATTAACCG",-51.4826935952771,-7.93438559954341 -"Sample_2_TCGTACCAGATCTGCT",59.9666316140367,-35.6797308882655 -"Sample_2_TCGTACCGTTTGACAC",-82.4592245939015,-18.159059512615 -"Sample_2_TCGTACCTCAAACCGT",-56.8218252185744,-42.7597870297126 -"Sample_2_TCGTACCTCCCGACTT",77.2061317560408,-49.1776956415854 -"Sample_2_TCGTAGACATCTATGG",-105.583155315679,14.1762900876145 -"Sample_2_TCTATTGAGGCGACAT",92.3865338208235,2.42343126840394 -"Sample_2_TCTATTGGTAGCTCCG",-10.6967613633994,-33.4435058583849 -"Sample_2_TCTCATAAGACTTTCG",-3.98648700225904,-63.8199925778259 -"Sample_2_TCTCTAATCCGTAGTA",18.9719167110712,-2.61009910116699 -"Sample_2_TCTGAGAAGAGTGAGA",-108.774126308881,-40.0510445534515 -"Sample_2_TCTGAGACACCACGTG",-3.77890576283611,-71.1228816419144 -"Sample_2_TCTGAGACAGATCTGT",61.4163391461616,-25.5750134490412 -"Sample_2_TCTGGAAAGTACGTTC",92.7363724043511,21.7288203051802 -"Sample_2_TCTTCGGAGGGATGGG",88.6836397542122,3.94316179058292 -"Sample_2_TCTTTCCAGACTGGGT",-89.8263075716363,35.2990927555407 -"Sample_2_TCTTTCCGTGCTTCTC",84.1115238948375,-43.021315344751 -"Sample_2_TGAAAGATCCCTAATT",-107.168172045503,-53.1938399416076 -"Sample_2_TGAAAGATCGTTGCCT",8.6573191026505,-77.6632803103283 -"Sample_2_TGACAACTCCTACAGA",-77.4162390920722,42.5991440026418 -"Sample_2_TGACTTTCAATGGATA",-9.24124298593094,-23.8706969916282 -"Sample_2_TGACTTTTCTAGCACA",91.5761258734278,-48.8699178668298 -"Sample_2_TGAGAGGAGCAATCTC",-72.0537311479138,24.9054385265768 -"Sample_2_TGAGCATGTCAAAGCG",13.0179343465917,12.3580995540492 -"Sample_2_TGAGCCGGTGCCTTGG",-108.370551466417,-47.0786581705718 -"Sample_2_TGAGGGAAGCATGGCA",1.79494700993182,96.9774151915082 -"Sample_2_TGAGGGACATGGGAAC",18.2322660804004,4.23469765093634 -"Sample_2_TGAGGGATCACATGCA",76.2458274466313,-40.417001330268 -"Sample_2_TGATTTCTCCTTCAAT",-75.5253838720709,-21.699175874685 -"Sample_2_TGCACCTAGGGCACTA",-115.260841587992,16.7394745575992 -"Sample_2_TGCCCATCAGTAAGCG",70.9787349230763,-26.5037379126136 -"Sample_2_TGCCCTAGTTCAACCA",-83.8952872723247,-27.981119051725 -"Sample_2_TGCCCTATCAGGCAAG",38.2240048908242,68.3659885829009 -"Sample_2_TGCGGGTAGACCTAGG",95.308122206675,-27.2926155210545 -"Sample_2_TGCGGGTGTTATGTGC",-22.4346956949705,-104.529316365057 -"Sample_2_TGCGTGGGTAATTGGA",-60.0539322815894,-5.94839124289982 -"Sample_2_TGCTACCAGCTCCCAG",52.0377538624321,113.160986494111 -"Sample_2_TGCTGCTGTCCAACTA",36.8555427385271,19.4479810259058 -"Sample_2_TGGACGCAGGAGCGAG",12.2519605472498,28.357812555776 -"Sample_2_TGGACGCGTAACGCGA",92.6068888233316,-3.4553409303192 -"Sample_2_TGGCGCAAGTACGCGA",67.7362742604034,-11.2780221426496 -"Sample_2_TGGCGCAGTTCCCGAG",-80.2424622339179,-26.6423427131904 -"Sample_2_TGGCGCATCCTACAGA",65.9310250653277,-61.4297937722023 -"Sample_2_TGGGAAGAGGAACTGC",20.1458534745296,112.9811415059 -"Sample_2_TGGGAAGTCTATCCTA",-73.4416477026454,-64.2118259891127 -"Sample_2_TGGGCGTCAGTTAACC",93.2515108762933,18.9754533866325 -"Sample_2_TGGGCGTGTGTTGGGA",-12.7396826191563,-33.0509785255192 -"Sample_2_TGGTTAGAGCGCCTTG",-78.6314936255316,-3.5714995733403 -"Sample_2_TGGTTAGAGGAATTAC",5.55205098456483,6.22076972246813 -"Sample_2_TGGTTAGGTCAATACC",-57.9678086231846,-18.6316329198611 -"Sample_2_TGGTTCCTCCACTCCA",38.8822787549706,21.1566265228036 -"Sample_2_TGTATTCAGATGTCGG",39.3094200427998,-0.11779770767649 -"Sample_2_TGTATTCCAGCGTCCA",-91.6976842301678,32.4087985286241 -"Sample_2_TGTATTCTCTGATACG",-97.2909696442852,19.2692089236782 -"Sample_2_TGTCCCAAGAGACTAT",-76.1917028297962,46.3509733939443 -"Sample_2_TGTCCCATCTTGTACT",-71.2277263549906,7.03719605429803 -"Sample_2_TGTGGTACATGCAACT",38.4755922316469,110.649563652689 -"Sample_2_TGTGGTAGTGCATCTA",7.37965698906261,-82.2436913193245 -"Sample_2_TGTGGTATCCCGACTT",78.4211295540298,-48.1197835572142 -"Sample_2_TGTGTTTAGGCCCTCA",-62.0979780490524,34.5640562870999 -"Sample_2_TGTGTTTGTTGTGGAG",31.5674072578758,-10.7074529106499 -"Sample_2_TGTGTTTTCATGCTCC",103.181791251673,9.31924163831321 -"Sample_2_TGTTCCGAGCCACCTG",-70.9818638873968,-9.65391823091315 -"Sample_2_TGTTCCGCAGTGACAG",-26.5607514885731,-108.545814188931 -"Sample_2_TTAGGACAGGGTCGAT",12.3402434036034,-77.2428239633987 -"Sample_2_TTAGGACGTGGTACAG",79.2838924519901,-21.7612446853845 -"Sample_2_TTAGGCACATAAAGGT",69.7722982864813,1.80514312454401 -"Sample_2_TTAGTTCTCACTTACT",79.4114331137411,-34.0511581759575 -"Sample_2_TTAGTTCTCTTGAGAC",58.4775026643695,0.95186958134689 -"Sample_2_TTATGCTTCACGAAGG",46.0203398766254,114.855177081385 -"Sample_2_TTCGGTCAGCCAGAAC",-98.409379581475,37.3657355749612 -"Sample_2_TTCGGTCAGTTCCACA",-23.4759375089673,-181.307815391988 -"Sample_2_TTCTACACATTACCTT",1.02441221866705,-60.4818122642092 -"Sample_2_TTCTACATCGCAAGCC",72.6443031688969,-43.6582002469905 -"Sample_2_TTCTCAACACACTGCG",10.2471984104729,-9.43406137653646 -"Sample_2_TTCTCCTGTTCCCGAG",-85.8692429326244,5.33652890379477 -"Sample_2_TTCTCCTTCCGAATGT",83.7774995400815,-24.4272582134512 -"Sample_2_TTCTTAGGTTTAGGAA",28.7613713070895,-32.6031744842218 -"Sample_2_TTGAACGAGCCATCGC",-105.370023688203,-20.6364193146498 -"Sample_2_TTGAACGGTAGCTCCG",61.4474451185502,-51.0270747718354 -"Sample_2_TTGAACGGTCTCAACA",-21.2456528307542,-21.0853647015759 -"Sample_2_TTGAACGTCAGTTCGA",44.0575605322179,119.556393982893 -"Sample_2_TTGCCGTCACCTTGTC",-61.0591547835395,31.1650632562103 -"Sample_2_TTGCCGTTCTTGCAAG",77.853297409322,-10.4734681675182 -"Sample_2_TTGCGTCCAGCCTTGG",30.3044946851838,-22.1583853338452 -"Sample_2_TTGCGTCTCACGGTTA",35.6989271030396,65.2062308794106 -"Sample_2_TTGCGTCTCCTATTCA",102.746901849535,-53.4165055096485 -"Sample_2_TTGCGTCTCTTCTGGC",18.507755467064,25.6626157552336 -"Sample_2_TTGGCAAAGAGATGAG",-82.1358341723831,35.0353180124512 -"Sample_2_TTGGCAAGTATCACCA",54.6984420885341,-31.1504410482024 -"Sample_2_TTGTAGGAGTCGTACT",98.9865392352972,-31.3049763808938 -"Sample_2_TTTATGCAGCCACTAT",80.9720005944224,-15.098702613131 -"Sample_2_TTTATGCCAGTTAACC",72.5045205003509,-5.16552826269934 -"Sample_2_TTTGCGCAGGCTACGA",-69.9399325469725,33.2918514099754 -"Sample_2_TTTGCGCTCACTATTC",84.2104534571048,-51.6704954976529 -"Sample_2_TTTGGTTTCTGTTTGT",-14.6688465439556,-70.6387900408266 -"Sample_2_TTTGTCACAATTGCTG",41.1249754225394,-47.2459737799851 -"Sample_2_TTTGTCAGTAGGAGTC",-24.7248284902326,-183.570077809388 -"Sample_2_TTTGTCATCCTCATTA",-109.16577455521,-45.957964753033 -"Sample_3_AAACCTGTCTGCTGTC",-6.53342582501845,-75.7554017126506 -"Sample_3_AAACGGGGTTTGCATG",2.97782703151662,74.5842091848384 -"Sample_3_AAACGGGTCCTTTACA",102.88021654599,-36.2158560785334 -"Sample_3_AAAGTAGCAAACAACA",7.38019149079018,110.737022274159 -"Sample_3_AAAGTAGCATAGAAAC",6.76296786609676,96.0589015780451 -"Sample_3_AAATGCCCATGATCCA",100.717161148249,-18.4771495790187 -"Sample_3_AAATGCCGTTAGTGGG",26.1530302232822,95.7335087087461 -"Sample_3_AAATGCCTCAGTTAGC",96.7242186977535,-16.1444703209224 -"Sample_3_AACACGTGTCGCTTCT",104.41644276137,-12.269704037059 -"Sample_3_AACCATGGTCTCACCT",16.6955875464369,35.4957728009432 -"Sample_3_AACTCAGGTACCGTAT",-105.990023337244,3.1987733435207 -"Sample_3_AACTCAGGTCCATCCT",-45.1439799449946,-30.8821185899188 -"Sample_3_AACTCAGGTCGCTTCT",96.5938554287403,-8.54302371140413 -"Sample_3_AACTCAGTCCTTTCTC",-92.3593590071097,-0.472791636586194 -"Sample_3_AACTCTTCACACATGT",-3.09426923049973,3.76751305331675 -"Sample_3_AACTCTTGTGCCTGTG",-3.44165643823998,78.742019051146 -"Sample_3_AACTCTTTCAAACAAG",-94.653384877785,31.6893721368118 -"Sample_3_AACTCTTTCCTCGCAT",0.466131076844268,-68.4004414623199 -"Sample_3_AACTGGTGTGCGGTAA",97.5494801171381,-4.32481016022257 -"Sample_3_AACTTTCGTAGCGATG",-102.357253021268,40.8852947430714 -"Sample_3_AAGACCTAGATGTCGG",100.577420213085,-50.1257154916094 -"Sample_3_AAGCCGCAGCTGAACG",-49.4076875198105,-14.538552288199 -"Sample_3_AAGCCGCGTTCGCGAC",-13.1517484391941,-45.3097601693952 -"Sample_3_AAGGAGCCAATACGCT",80.210362322813,23.2183862913361 -"Sample_3_AAGGCAGAGAGGTAGA",29.8250293561398,55.2947362813515 -"Sample_3_AAGGCAGAGGAATCGC",16.0499952428752,48.99237741183 -"Sample_3_AAGGTTCGTCATATGC",6.53370986183017,115.354933639242 -"Sample_3_AAGGTTCTCAGTGCAT",104.264900817933,-7.20807806289955 -"Sample_3_AATCCAGCAAGCCCAC",-108.54599700582,-0.347406424681973 -"Sample_3_AATCCAGGTAAGCACG",-0.777941727507955,-51.6821385324116 -"Sample_3_AATCCAGTCACAACGT",68.2940027579859,-41.1318842598335 -"Sample_3_AATCGGTCATCACAAC",78.2038531337735,-55.4605634784593 -"Sample_3_ACACCCTCAGCTGTGC",-81.5578767126235,8.72625779564934 -"Sample_3_ACACCCTCATACCATG",-56.3059323369967,-24.7722342719754 -"Sample_3_ACACTGAAGCTGAACG",19.8753269854,48.3173485995392 -"Sample_3_ACACTGATCTTACCTA",-108.518744277461,-6.81761366788935 -"Sample_3_ACAGCCGAGCTACCGC",-39.245010349757,-9.44965689824322 -"Sample_3_ACAGCTAAGGCAGTCA",-90.0912485886518,-27.1843042612687 -"Sample_3_ACATACGAGAGAACAG",62.2518255959409,-33.1037811355547 -"Sample_3_ACATACGAGGCTAGAC",27.96639416119,-43.4610908691117 -"Sample_3_ACATACGTCAACCAAC",-71.7478223997174,35.7974257938677 -"Sample_3_ACATCAGAGGTCGGAT",-66.6362114715468,-18.9645108928983 -"Sample_3_ACATCAGGTCCGTTAA",-72.4049918579189,20.6853154224005 -"Sample_3_ACATGGTGTCGCTTCT",96.3345721429239,-53.1262633356513 -"Sample_3_ACCAGTACACAACTGT",54.1083654992594,21.2172298340834 -"Sample_3_ACCAGTATCCTCGCAT",93.3827089987052,-43.8769731872874 -"Sample_3_ACCAGTATCTGAAAGA",-68.8337949732794,-17.6263430745247 -"Sample_3_ACCGTAACAAGAAGAG",22.4519252835626,119.278606684187 -"Sample_3_ACCGTAACATTGAGCT",29.5378239405844,9.24093127643699 -"Sample_3_ACCGTAATCGCTTGTC",96.7572645341494,-56.4396917505663 -"Sample_3_ACCTTTAAGCGATATA",24.9750764396265,99.3113588799126 -"Sample_3_ACGAGCCAGACCTAGG",42.8916759304278,102.79304274124 -"Sample_3_ACGAGCCGTCTAGTCA",-47.7760515300132,0.780771586031268 -"Sample_3_ACGATACAGAGACGAA",72.3324033691989,-47.6562037792089 -"Sample_3_ACGATACCAATACGCT",-67.2830836356255,28.9574530699914 -"Sample_3_ACGATACGTCTGCCAG",50.3710879622804,109.465433143073 -"Sample_3_ACGATGTAGAGCTTCT",64.7309520287718,-56.1468725879551 -"Sample_3_ACGATGTCATTCACTT",80.3972490568816,-26.5324373395636 -"Sample_3_ACGATGTGTCATATCG",10.311916409228,14.9036364065939 -"Sample_3_ACGCAGCCAAGCGCTC",96.4460618531976,-32.4949318295315 -"Sample_3_ACGCCGAAGGTGTTAA",-83.1489871913843,26.5589624277162 -"Sample_3_ACGGAGAAGACAGAGA",15.8312591007813,37.7422796412189 -"Sample_3_ACGGCCAGTTGATTCG",7.7685722988131,-66.7996923526301 -"Sample_3_ACGTCAAAGGTAGCTG",-103.389194607448,16.8523287498579 -"Sample_3_ACGTCAATCGGAGCAA",-81.6003119426651,42.1783274396883 -"Sample_3_ACGTCAATCGGCATCG",94.9483454959256,-9.58001047102965 -"Sample_3_ACTATCTAGCTACCTA",-89.3658556779055,-11.773330978201 -"Sample_3_ACTGAACGTCAATGTC",42.093424739441,116.899553438844 -"Sample_3_ACTGAACTCCCAACGG",44.3025667723085,100.567493184167 -"Sample_3_ACTGCTCCATTAGCCA",-59.9344256216091,8.86842460220328 -"Sample_3_ACTGTCCAGAAGAAGC",61.8841839510907,-56.3308029140651 -"Sample_3_ACTGTCCGTACCGCTG",72.8242784387891,-34.176621847482 -"Sample_3_ACTTACTAGCCGGTAA",26.8015181523886,50.7009774657816 -"Sample_3_ACTTACTGTACCGGCT",-91.6244133745588,40.9241320286555 -"Sample_3_ACTTGTTCAGATGGCA",-83.0615621394674,17.6356600129472 -"Sample_3_ACTTGTTTCACGATGT",-10.1948058377122,-34.2662943725288 -"Sample_3_ACTTTCACACAGTCGC",45.5755289180764,97.6354380156067 -"Sample_3_ACTTTCAGTAAGGATT",63.6233082208945,-58.7082352018574 -"Sample_3_ACTTTCATCAGTTGAC",9.69347473876906,91.2327295784435 -"Sample_3_AGAATAGTCAACACCA",-42.7763211447021,-21.9898333676259 -"Sample_3_AGAGCGAAGGTCATCT",96.3534877194883,7.00782936527182 -"Sample_3_AGAGCGAGTACCGGCT",-99.9874954419899,14.6710466110309 -"Sample_3_AGAGCTTAGGAGTTTA",-95.9835721373276,2.08153952196006 -"Sample_3_AGAGCTTTCAAGAAGT",-52.8188822313235,-24.418447840121 -"Sample_3_AGAGTGGTCCAGTAGT",22.4353584325419,86.1263516763104 -"Sample_3_AGAGTGGTCCTTGACC",19.896619064366,51.8356862308745 -"Sample_3_AGCAGCCAGTTCCACA",23.5055559955808,58.6302842203939 -"Sample_3_AGCCTAACAGTCTTCC",-59.3930043239229,30.4354886276533 -"Sample_3_AGCGTATAGTGCAAGC",97.9102545342549,-49.8480000574003 -"Sample_3_AGCGTATGTGACGGTA",-68.7024328474876,24.2433804521946 -"Sample_3_AGCGTCGCAGCGAACA",83.4909280290634,2.00296299210149 -"Sample_3_AGCTCCTTCAGAGCTT",-104.818405320887,-48.816186215422 -"Sample_3_AGCTCTCCATTTGCCC",-27.3112651288172,-104.125781534878 -"Sample_3_AGCTTGAAGACTAGAT",-75.3158885449292,-0.153078729908755 -"Sample_3_AGCTTGAGTCTCACCT",13.2965826914939,111.830718622633 -"Sample_3_AGGCCACCAAGCGAGT",-29.4760385448534,-10.4723925952259 -"Sample_3_AGGCCACCAAGTAATG",-73.0264792818301,41.8857890510373 -"Sample_3_AGGCCACCACGGCTAC",22.9762537557437,-16.088989077298 -"Sample_3_AGGCCACTCTCTTATG",-113.850928599554,-11.287751783131 -"Sample_3_AGGCCGTGTAGAGGAA",73.4222918894636,8.86126421078473 -"Sample_3_AGGCCGTTCATCGATG",101.960185766549,-57.5038715139939 -"Sample_3_AGGGATGGTCTCAACA",-9.77031693268989,-77.7073397168834 -"Sample_3_AGGGTGACAGTAAGAT",-95.1952471015618,26.6534945030069 -"Sample_3_AGGTCATCACAGACTT",-72.7854365163388,33.0410952423863 -"Sample_3_AGGTCATTCGAGAGCA",-85.3510951814593,-35.1886814836572 -"Sample_3_AGGTCCGCAGCTGTGC",-28.2969075888852,-110.663179953589 -"Sample_3_AGTAGTCCATGTCTCC",4.52634602382579,-65.0003780408521 -"Sample_3_AGTAGTCTCACTCCTG",41.0139435984017,-3.45732488632942 -"Sample_3_AGTCTTTAGCCTCGTG",2.6763933125317,-29.9254375172398 -"Sample_3_AGTCTTTAGGATTCGG",57.9708914737335,-15.6915148763056 -"Sample_3_AGTGAGGAGGCTCTTA",-105.90434559035,-8.86976569906311 -"Sample_3_AGTGAGGGTGCACCAC",84.8125483200526,20.3414515460288 -"Sample_3_AGTGAGGTCTGATACG",17.8027196035404,-8.5829168189313 -"Sample_3_AGTGTCATCCTTTCGG",4.22940361487131,-18.3637121456239 -"Sample_3_AGTGTCATCTGGGCCA",-73.6812281449706,-65.9150601814555 -"Sample_3_AGTGTCATCTTACCTA",-1.69838231082335,-73.204335550954 -"Sample_3_AGTTGGTTCGTCCAGG",86.7994402740049,-74.780798725986 -"Sample_3_ATAACGCCAGTCAGAG",1.62170220656739,-42.8675098475488 -"Sample_3_ATAACGCGTAGCTTGT",9.19852100145243,115.806566865284 -"Sample_3_ATAAGAGGTGCCTGGT",-70.5901307649054,12.2868627040002 -"Sample_3_ATAGACCTCTTTACGT",-81.3047627735721,1.40103261004562 -"Sample_3_ATCACGAAGTGGAGTC",15.3840257786519,-1.48459242373469 -"Sample_3_ATCACGATCAGCAACT",8.29515746641179,94.9255087741299 -"Sample_3_ATCATCTCAACACGCC",7.24325298963391,94.8512177104124 -"Sample_3_ATCATGGAGGTGATAT",-94.4955894009137,-45.8353112557568 -"Sample_3_ATCATGGCAGCCAGAA",-25.0802452533867,-181.409087415038 -"Sample_3_ATCCACCAGTAGATGT",-47.6043330534384,3.5573415517252 -"Sample_3_ATCCACCCATCGATGT",-23.8519836031009,-185.657831788903 -"Sample_3_ATCCACCGTAACGACG",26.6615810399246,-23.200487930443 -"Sample_3_ATCCGAACACTAGTAC",-79.579667253122,-46.2228389409118 -"Sample_3_ATCCGAAGTACACCGC",-75.9574034899847,-12.7386879288006 -"Sample_3_ATCGAGTCACAACTGT",52.2215038125891,-19.9466208784272 -"Sample_3_ATCTACTCACGAGGTA",-101.391248993704,-35.3461376749074 -"Sample_3_ATCTACTCATGGGACA",33.1895912740527,50.5992862474459 -"Sample_3_ATCTACTTCGGCCGAT",36.456668324421,74.2281941283402 -"Sample_3_ATCTGCCAGCGCTCCA",87.2143225265411,-18.1727600122226 -"Sample_3_ATCTGCCAGTGACATA",-86.7714218922204,34.6881003556573 -"Sample_3_ATCTGCCAGTTGAGTA",-85.9922076221634,-43.8485010886183 -"Sample_3_ATCTGCCCACTTAACG",58.8722347767563,-16.5528771181589 -"Sample_3_ATCTGCCCATCCCACT",-76.997729684845,-16.7656428732536 -"Sample_3_ATCTGCCGTCTCTTTA",1.59881995057106,-3.8716692867852 -"Sample_3_ATCTGCCGTTAGTGGG",90.4402556468265,31.5066557074171 -"Sample_3_ATCTGCCTCGCTTAGA",26.631736737705,-1.17842669936245 -"Sample_3_ATGAGGGGTACCGTAT",3.91183486687863,-79.0823706554479 -"Sample_3_ATGCGATGTTCTGTTT",81.0658432606854,-40.1811428044757 -"Sample_3_ATGGGAGAGTTGTAGA",-54.6659463600769,-45.6343354146259 -"Sample_3_ATGTGTGAGCATCATC",-37.5402774380851,1.19298081935046 -"Sample_3_ATGTGTGAGGTCATCT",-103.217486049712,-16.473151366838 -"Sample_3_ATGTGTGGTGTATGGG",59.4548616039501,-43.5174848939079 -"Sample_3_ATTACTCAGGTCATCT",-89.4843628092264,-52.5534631490068 -"Sample_3_ATTACTCGTGGAAAGA",-9.4812057146597,-39.78169881416 -"Sample_3_ATTATCCAGTAGCGGT",-100.311994314766,-41.4774825288331 -"Sample_3_ATTATCCCACCCATGG",94.1893950511475,-58.6797107352718 -"Sample_3_ATTCTACCAAGCGATG",15.3815110114252,1.83603892785739 -"Sample_3_ATTCTACCATTAACCG",55.4808629785354,4.15349618429213 -"Sample_3_ATTCTACGTCTAGTCA",-74.2057132246548,-25.2730056447054 -"Sample_3_ATTCTACGTGGTAACG",5.20817820789603,-86.5158074514725 -"Sample_3_ATTGGACCAGCGTTCG",8.05109383882234,-76.1725071558489 -"Sample_3_ATTGGACCAGCTTCGG",24.6455621334668,-36.2000590604178 -"Sample_3_ATTGGACGTAGCGTGA",8.92051476413541,111.855323035723 -"Sample_3_ATTGGACGTCGCATCG",-34.3518600115207,13.763414039532 -"Sample_3_ATTGGTGAGCCCGAAA",73.1567621845394,2.78169077150842 -"Sample_3_CAACCAATCCTTGGTC",-75.6694991134667,6.02669817339386 -"Sample_3_CAACTAGCATGGTAGG",-68.0165703246197,-28.5144120650688 -"Sample_3_CAAGAAAAGACTAAGT",-94.6688998033937,-37.6544941035989 -"Sample_3_CAAGAAAAGTTAGCGG",-44.0721671558278,1.8897886526533 -"Sample_3_CAAGAAATCTATGTGG",-54.4846542275579,-3.66155305164671 -"Sample_3_CAAGATCGTTACTGAC",-1.40254337846092,-74.4875379310433 -"Sample_3_CAAGATCGTTCAACCA",-65.0766188677903,18.9184076416828 -"Sample_3_CAAGATCGTTCAGCGC",98.1900478319417,-19.2503467696266 -"Sample_3_CAAGATCGTTTAGGAA",17.5528479489605,113.591563383455 -"Sample_3_CAAGTTGGTAACGCGA",13.8567473172151,45.4187660742171 -"Sample_3_CACAAACAGTTCGATC",75.2813623105653,-36.0174882924213 -"Sample_3_CACACAACAATCACAC",9.44403810698285,108.496147626828 -"Sample_3_CACACAAGTCGCGTGT",45.6982496961219,51.7955693683783 -"Sample_3_CACACCTCAAACCTAC",-99.9102738695548,-52.7709099556726 -"Sample_3_CACACCTTCCCATTTA",-53.046601199618,10.6144962491392 -"Sample_3_CACACTCCAGTACACT",-98.9194877422606,-27.3685835810823 -"Sample_3_CACACTCCATGTAAGA",48.8106030822662,118.84210152339 -"Sample_3_CACACTCTCTAACCGA",18.6554457626638,-41.8650520356455 -"Sample_3_CACAGGCGTGGGTATG",-95.2175540770857,-5.0563292751369 -"Sample_3_CACAGTAAGTGACATA",6.51628999559652,-73.8201141021411 -"Sample_3_CACATAGCAAGCCGCT",21.5324993961439,116.606088031661 -"Sample_3_CACATAGGTAGCAAAT",-83.3127195773252,-47.4575693199752 -"Sample_3_CACATAGTCGCGTTTC",19.8295342148845,58.1825405459942 -"Sample_3_CACATTTCATTGCGGC",35.597374736629,58.9993126713419 -"Sample_3_CACATTTTCCGTCATC",20.0322215625414,94.4091733479077 -"Sample_3_CACCACTCACTCGACG",-12.9587053951093,-22.0784224000153 -"Sample_3_CACCACTTCATACGGT",-107.685661862645,-28.5393034587468 -"Sample_3_CACCACTTCCGCAAGC",-96.4961436024537,6.17067022425882 -"Sample_3_CACCACTTCTTGAGAC",-71.3022908775854,-19.8028863475553 -"Sample_3_CACCAGGCACCTCGTT",8.09630390706115,-87.8644382166628 -"Sample_3_CACCAGGTCAAGGTAA",-22.6442122963726,-39.1186297233062 -"Sample_3_CACCTTGTCTCTAAGG",47.7358229222177,21.1431652650824 -"Sample_3_CAGAGAGCATCACGTA",41.6693280322622,24.6145196444493 -"Sample_3_CAGATCAAGATATGGT",92.2673719427587,-58.8853057252762 -"Sample_3_CAGCAGCGTCGCCATG",89.0435746733781,34.8441078862775 -"Sample_3_CAGCAGCTCGGAATCT",-24.2079837778778,-182.526061565005 -"Sample_3_CAGCAGCTCTGTCCGT",99.3637285440326,-50.5093266723931 -"Sample_3_CAGCCGAAGCGACGTA",-80.4067406257576,-14.8961708463758 -"Sample_3_CAGCGACAGTAGCGGT",-61.834422921576,-19.1967006412661 -"Sample_3_CAGCTGGAGTGTACGG",-104.44907230576,17.2293302416148 -"Sample_3_CAGCTGGTCTTTCCTC",-94.2735486593745,-6.52660224492065 -"Sample_3_CAGTAACCATCTCGCT",2.45161366276806,-27.3685974268986 -"Sample_3_CAGTAACTCTGTCCGT",42.7740926271631,89.9151845949091 -"Sample_3_CAGTCCTAGACAAGCC",-65.3639309050866,-38.7485160787668 -"Sample_3_CAGTCCTAGATCTGCT",15.7732277262511,93.7323218988169 -"Sample_3_CAGTCCTAGTAGATGT",68.3529696720278,-52.7575885898123 -"Sample_3_CAGTCCTCAGGCGATA",108.239986913779,-16.4434318147774 -"Sample_3_CAGTCCTGTTCAGCGC",61.315080951629,-38.7401633552549 -"Sample_3_CAGTCCTTCTCTTGAT",72.1946319083621,-50.3744301078273 -"Sample_3_CATATGGTCTGTGCAA",-27.8406302696025,-106.824240945201 -"Sample_3_CATATTCCAGTGACAG",17.4267889771947,54.3408084291042 -"Sample_3_CATATTCGTAAATACG",71.8618447854141,15.4284029579728 -"Sample_3_CATCAAGAGGTGTTAA",34.5353534298888,14.7586634258813 -"Sample_3_CATCAAGCACGACGAA",105.134454778812,-55.5773555403754 -"Sample_3_CATCAAGCAGGCAGTA",-78.1722026125965,22.0285012825127 -"Sample_3_CATCAAGCATGTTGAC",23.8869623904269,-12.7340895597678 -"Sample_3_CATCAAGGTCTCCACT",-78.8941711674863,36.2389018743908 -"Sample_3_CATCAGAAGAAACCAT",36.3018897669474,22.7475545878198 -"Sample_3_CATCAGACACCCTATC",107.206075875804,-22.4020013839342 -"Sample_3_CATCAGAGTCCATGAT",-105.808569645924,-14.3427484185712 -"Sample_3_CATCAGAGTCCGAAGA",1.63523389113218,-90.2281264261263 -"Sample_3_CATCCACGTCCCTTGT",30.6146373646419,19.0255026284708 -"Sample_3_CATCCACGTGCATCTA",37.7724296702747,50.0322402318907 -"Sample_3_CATCCACTCTGAGGGA",-89.520471883538,13.3800355409823 -"Sample_3_CATCGAAAGGAATCGC",27.5988200297641,56.4078778280557 -"Sample_3_CATCGAACAGGCTGAA",1.420826936961,101.999139802788 -"Sample_3_CATCGAACAGGGAGAG",100.760066207759,-26.0791882875234 -"Sample_3_CATCGGGCAAAGGTGC",-69.0107553064008,-33.3878187063993 -"Sample_3_CATGACAAGTGAACGC",-12.5052356295928,-38.0275075822866 -"Sample_3_CATGACACACAGACTT",-97.9168728244508,-1.26591232888471 -"Sample_3_CATGACACACCGTTGG",62.3088102040657,-53.8980732266218 -"Sample_3_CATGACACAGCTATTG",-88.6635659193909,32.6900540997053 -"Sample_3_CATGACAGTCTCACCT",64.3965160007799,-45.2076500355816 -"Sample_3_CATGCCTCAATGAAAC",-78.5198294967372,12.6608896905331 -"Sample_3_CATGGCGAGTGTGGCA",-55.6705527761135,-13.1527279169339 -"Sample_3_CATGGCGCACGAGGTA",-71.3552823542724,-14.3860985191307 -"Sample_3_CATGGCGGTACAGTGG",-95.5423293006431,-23.9464043516251 -"Sample_3_CATTATCAGGGTTCCC",-98.310202736278,31.1982013311159 -"Sample_3_CATTATCCACAGCCCA",1.88066024329097,72.9271762799723 -"Sample_3_CATTATCGTGGTCTCG",91.6710776628359,-19.3228550897404 -"Sample_3_CCAATCCGTCTCTTAT",51.6547636747303,117.894510022162 -"Sample_3_CCAATCCTCTCATTCA",21.0523783546139,115.318578947098 -"Sample_3_CCACCTATCTTGCCGT",-114.868519945013,-47.4881794045485 -"Sample_3_CCACGGACAATCACAC",-80.9283793698385,27.7562521773308 -"Sample_3_CCACGGATCAGGCAAG",-25.5906269717361,-102.509065583307 -"Sample_3_CCACTACAGTGGACGT",51.2665920508865,-24.1723267242354 -"Sample_3_CCAGCGAGTTCGTTGA",69.3837865648004,-6.05367188347245 -"Sample_3_CCATTCGCAAGCCATT",17.7237929740186,88.5901756941554 -"Sample_3_CCCAATCTCAACACCA",92.7016764313357,-10.4034960672956 -"Sample_3_CCCTCCTAGTAGCCGA",-51.6746954277812,6.5314174797818 -"Sample_3_CCCTCCTGTTGTACAC",18.6072259468616,117.409669285243 -"Sample_3_CCGGTAGAGTTACGGG",25.2867304394643,112.246514551549 -"Sample_3_CCGGTAGTCGCCTGAG",-90.9936364861167,-41.5502590058821 -"Sample_3_CCGTACTCAGGGTATG",94.2971576534725,-39.1952778627434 -"Sample_3_CCGTACTTCTACTATC",-50.3484687670588,14.4100613145913 -"Sample_3_CCGTTCAAGCCCGAAA",-108.770788234203,-24.6655425691258 -"Sample_3_CCTAAAGAGCGATTCT",75.4303502604686,-31.2926606052873 -"Sample_3_CCTAAAGCAAGTAATG",91.8038565465915,-15.7003582099181 -"Sample_3_CCTACACAGCAAATCA",63.1553036937715,6.38154488172317 -"Sample_3_CCTACACTCTTTACGT",-96.658251139113,38.0806108030046 -"Sample_3_CCTACCACATCGGAAG",-3.85993322703509,-55.02716721174 -"Sample_3_CCTAGCTAGAGGTACC",7.3872661380691,113.012017700454 -"Sample_3_CCTATTATCAAACCAC",77.3163607501759,11.2823592541627 -"Sample_3_CCTCTGAGTAAGAGGA",-10.8347671660003,-66.1039884270864 -"Sample_3_CCTCTGAGTTATTCTC",98.5831131789231,-29.2901349739112 -"Sample_3_CCTCTGATCAGCCTAA",-95.4779809418095,-19.3709402199238 -"Sample_3_CCTTACGAGCACCGCT",-111.081428756967,12.5863108282457 -"Sample_3_CCTTACGGTCGCATCG",-61.3864019393349,-27.1033463575756 -"Sample_3_CCTTCCCCAAACCTAC",47.4923450774632,109.46798434801 -"Sample_3_CCTTCCCTCACTGGGC",5.85722364273,100.487177709143 -"Sample_3_CCTTCGAAGCGTTTAC",-62.490449986768,17.1423998300786 -"Sample_3_CCTTCGACAAGAAGAG",105.109972684631,-16.0851525538893 -"Sample_3_CGAACATAGTCAAGCG",-60.88444906341,-23.3680629404502 -"Sample_3_CGAACATCATCAGTCA",67.6582044085685,-65.7497909071118 -"Sample_3_CGAATGTGTCATTAGC",87.3225674733353,28.9685562454182 -"Sample_3_CGACCTTAGCACCGCT",32.7011520378575,19.9215373659516 -"Sample_3_CGACCTTAGTTGTAGA",-25.2822214536209,-186.081437151997 -"Sample_3_CGACCTTTCAAGCCTA",3.71548551362359,-62.044370083639 -"Sample_3_CGACTTCTCATAGCAC",-85.4198568551573,15.0214573384893 -"Sample_3_CGAGCACAGACGCAAC",-111.820686297312,-4.36883642062253 -"Sample_3_CGAGCACAGCTCAACT",104.783366348847,-60.4729374866674 -"Sample_3_CGAGCACAGGCGTACA",0.107465265348769,111.922067443357 -"Sample_3_CGAGCACTCTGCAAGT",8.00380302503548,105.090647952343 -"Sample_3_CGAGCCACATGCAACT",4.53606596606417,95.3029331076328 -"Sample_3_CGAGCCATCGTGGTCG",23.9939098554393,4.90987890663755 -"Sample_3_CGATCGGAGACGCTTT",7.85117824042638,-63.3415476140359 -"Sample_3_CGATCGGGTCCCTTGT",-0.16339525558298,-48.328604401576 -"Sample_3_CGATGGCTCGGTCCGA",50.4942663123786,114.065868442487 -"Sample_3_CGATGTAAGGACATTA",67.7847465151341,-20.4259704109353 -"Sample_3_CGATTGAGTGGTACAG",-51.3858373803496,-19.5518602220401 -"Sample_3_CGCCAAGAGACCACGA",10.2736266622439,-82.6599631128108 -"Sample_3_CGCCAAGGTGTCCTCT",-99.6854293511323,43.5001077136074 -"Sample_3_CGCGGTAAGGCATGGT",-65.3671465357504,16.3126871819206 -"Sample_3_CGCGGTAAGTATCGAA",-113.449358073599,-6.52494709684157 -"Sample_3_CGCGGTACACAGGCCT",-51.5842271004605,-37.603297874967 -"Sample_3_CGCGGTATCAACGCTA",-63.2490456769418,-14.0492229687404 -"Sample_3_CGCGGTATCTCTGCTG",21.2576093567601,10.5793447651495 -"Sample_3_CGCGTTTTCCAGATCA",25.1415466701316,-35.2605827335987 -"Sample_3_CGCTATCCACTGTCGG",7.55463419713548,109.45622312041 -"Sample_3_CGCTATCGTGCAGACA",-101.738475712393,1.59873210118377 -"Sample_3_CGCTATCTCGCCATAA",-79.7348676145141,-25.4748929682689 -"Sample_3_CGCTATCTCTGTACGA",29.80863954451,12.3564658123903 -"Sample_3_CGCTGGATCCTAGGGC",32.6846834959287,58.0292860120564 -"Sample_3_CGCTTCACATTTCAGG",71.4377864642352,-41.0150936222807 -"Sample_3_CGGACACAGCAGCGTA",23.7293890873984,81.6020744457628 -"Sample_3_CGGACACAGGTGCTTT",-76.3215028890804,-31.9654846820431 -"Sample_3_CGGACACTCCTCGCAT",2.04444186768243,-12.4319594468485 -"Sample_3_CGGACGTTCAGGTTCA",72.6447067003007,-23.5701953366627 -"Sample_3_CGGACTGTCTCTGAGA",39.9478866412404,56.7643215463921 -"Sample_3_CGGAGCTCAAACTGTC",57.4593697575589,-55.1976680409403 -"Sample_3_CGGAGCTGTACCGTTA",77.0715584043324,-0.545611690168111 -"Sample_3_CGGAGTCTCAGGCCCA",-82.068292273646,-39.7512495048401 -"Sample_3_CGGCTAGCAATCTGCA",-35.4790364266534,-21.0662062540964 -"Sample_3_CGTAGCGCAAACAACA",-52.1430841408096,-47.8290770412401 -"Sample_3_CGTAGCGTCAGCACAT",-67.3616751278797,-2.63108165575549 -"Sample_3_CGTAGCGTCCTAGAAC",-25.3770846772371,-109.501213610926 -"Sample_3_CGTAGGCGTCTGATTG",41.9498138838822,61.9227919765946 -"Sample_3_CGTCAGGCAAGCTGTT",-89.487276813146,-37.2006021818671 -"Sample_3_CGTCAGGTCAAGGTAA",13.0495277627425,81.1710382814784 -"Sample_3_CGTCCATAGCTATGCT",-84.1382271187702,-11.7797016743887 -"Sample_3_CGTCCATCACCCTATC",76.8063894349827,-37.5452574770113 -"Sample_3_CGTCTACCATAGGATA",37.6654645224259,106.887339242924 -"Sample_3_CGTGAGCCATTCTTAC",88.0092352397423,19.1184140434223 -"Sample_3_CGTGAGCTCAATCTCT",-25.6371572292524,-182.197657634856 -"Sample_3_CGTTGGGTCAGAGACG",72.4397307849174,-1.04622467282439 -"Sample_3_CGTTGGGTCCTTGCCA",20.6101158536928,36.6790443393318 -"Sample_3_CTAACTTAGCCCAGCT",88.996409514938,-29.994132432055 -"Sample_3_CTAAGACAGTGTCTCA",-109.365127391216,-34.5130338811555 -"Sample_3_CTAAGACCAGATCGGA",88.817990631284,21.9355523167682 -"Sample_3_CTAAGACGTCGGATCC",-23.8026825966337,-108.52091860263 -"Sample_3_CTAAGACTCCACTGGG",-17.870179339172,-41.4019561812523 -"Sample_3_CTAATGGCACAGACTT",27.6176842194211,95.0861142695858 -"Sample_3_CTAATGGGTTCAGGCC",-68.2309056243527,-13.5943753281275 -"Sample_3_CTACACCCAACGCACC",-106.309649854761,20.9676041803582 -"Sample_3_CTACACCGTTCAGCGC",-55.4184838639385,14.8202082598574 -"Sample_3_CTACACCTCGGTTCGG",65.8725358515572,-38.5712726138259 -"Sample_3_CTACATTCATGCCCGA",-91.8257480367623,17.0612491316134 -"Sample_3_CTACATTGTTTGGCGC",-63.6296785579525,24.7298796039656 -"Sample_3_CTACCCAAGGTGCACA",-31.998060057876,-98.802545186773 -"Sample_3_CTACCCACACGACTCG",-54.8515092312098,-18.6649024153281 -"Sample_3_CTACCCACAGCGATCC",-43.4772282795752,-10.3612778196392 -"Sample_3_CTACCCATCCAAATGC",-29.9656485241795,12.5565216239999 -"Sample_3_CTACGTCAGAGCTGCA",32.2777951547993,118.198903589253 -"Sample_3_CTACGTCAGGCGATAC",102.589543724768,-29.5303303739765 -"Sample_3_CTACGTCGTCTCAACA",-83.1207361683343,-7.80697377110261 -"Sample_3_CTACGTCTCGTCGTTC",-90.347867829881,-18.5901541651698 -"Sample_3_CTAGAGTCAATCTACG",-100.558309863259,-4.84258222164556 -"Sample_3_CTAGAGTCAGGAATCG",2.79551330659174,-56.002878203012 -"Sample_3_CTAGAGTTCAACTCTT",91.6994390347534,-30.9323433437253 -"Sample_3_CTAGTGACAGACGCTC",23.1750666658555,61.4417302520903 -"Sample_3_CTAGTGACAGCGTAAG",22.4274307973213,33.0492796481284 -"Sample_3_CTAGTGACATCTATGG",31.7291770784229,48.206652083575 -"Sample_3_CTAGTGATCCTTGACC",42.6969660473681,103.707324536845 -"Sample_3_CTCACACTCACATACG",67.924183185322,-25.2366818277762 -"Sample_3_CTCAGAATCGGAAATA",11.2345586144755,-62.762388038715 -"Sample_3_CTCATTAAGTCGTTTG",41.9362659090854,69.0974486282774 -"Sample_3_CTCCTAGTCGGCGCTA",14.4146210222187,-72.9602482195161 -"Sample_3_CTCGAAAAGAACAATC",40.8139391436364,10.9891163934093 -"Sample_3_CTCGAAAAGACGACGT",4.71904146913021,-9.11848258870082 -"Sample_3_CTCGAAACACAGCGTC",-28.164373042564,-8.74465308265869 -"Sample_3_CTCGGGAGTTCGGCAC",78.1157906266383,-19.7610680373878 -"Sample_3_CTCGTCACACACTGCG",-110.494133202556,-33.1867361234279 -"Sample_3_CTCGTCACACAGGAGT",-100.823546683854,-30.1509184696456 -"Sample_3_CTCGTCAGTTCCACGG",86.7399240822464,-57.509753587377 -"Sample_3_CTCTAATAGTCCGTAT",-105.071212724415,-43.0863032895608 -"Sample_3_CTCTAATAGTGGGTTG",83.0991118843035,-47.7191948412575 -"Sample_3_CTCTACGAGGTGCACA",75.0715941988386,-47.1192208673678 -"Sample_3_CTCTACGTCGAACGGA",105.917592007786,-51.5026891805787 -"Sample_3_CTCTGGTAGCGATATA",70.907320447803,-59.2755001109637 -"Sample_3_CTCTGGTAGGGAACGG",35.7125945427413,84.9922625890878 -"Sample_3_CTCTGGTAGGTCGGAT",94.9474691646364,-25.0150767055112 -"Sample_3_CTCTGGTCACTGCCAG",62.7709186578919,-62.3122736075324 -"Sample_3_CTGAAACGTGCCTGTG",-57.0310866622596,-35.5651173750797 -"Sample_3_CTGAAGTCACGAAGCA",20.6326645079006,104.404168971439 -"Sample_3_CTGAAGTTCCGCAAGC",-100.896032040301,10.3981479854579 -"Sample_3_CTGAAGTTCGCAAACT",13.8694176391222,116.100996128106 -"Sample_3_CTGATAGAGCGTCAAG",56.5050169912615,107.683303777714 -"Sample_3_CTGATAGTCGAATCCA",87.9002440380193,30.2396265770672 -"Sample_3_CTGATCCTCATAACCG",103.256582439935,-45.479693446882 -"Sample_3_CTGATCCTCGCAAGCC",16.7042580847049,91.2793544172434 -"Sample_3_CTGATCCTCTACTATC",23.393534747282,55.7428084991393 -"Sample_3_CTGCCTAAGAAGGTGA",-71.6078902678931,-31.9716905076033 -"Sample_3_CTGCCTAAGACAGAGA",20.4987775365571,-12.2133943130478 -"Sample_3_CTGCCTACAGACAGGT",-77.1931364944846,31.0368806763674 -"Sample_3_CTGCTGTCAACGATCT",-65.1926415328292,-48.8411689699121 -"Sample_3_CTGGTCTCAACCGCCA",89.6902855962268,-60.4685083447451 -"Sample_3_CTGGTCTGTATCAGTC",-97.4828993185168,-37.6244965314928 -"Sample_3_CTGGTCTGTTGACGTT",87.2583827724732,16.1492223265767 -"Sample_3_CTGGTCTGTTGTCGCG",25.5550601170035,109.232951194725 -"Sample_3_CTGTGCTCACATGGGA",45.5009451484014,114.297801353037 -"Sample_3_CTGTGCTCACGGCCAT",62.1450393055301,-21.6761708599213 -"Sample_3_CTGTGCTTCAGCGATT",71.8765538185029,-10.0955970159023 -"Sample_3_CTTAACTGTGGACGAT",34.2593589454815,87.4916053694953 -"Sample_3_CTTACCGAGCGTCAAG",93.6070204111172,-21.3154729022964 -"Sample_3_CTTAGGAAGCCAACAG",28.7912920124322,104.637540500756 -"Sample_3_CTTCTCTAGTATCGAA",83.9020558707345,-36.6285636857734 -"Sample_3_CTTCTCTAGTGCGATG",-114.238227590784,-2.33343518751294 -"Sample_3_CTTTGCGTCGGGAGTA",87.0997857946238,0.160795275673061 -"Sample_3_GAAACTCAGTACGTTC",2.65549982795137,3.24374714185334 -"Sample_3_GAAACTCGTCTGCCAG",20.3688351589218,-40.8618315629981 -"Sample_3_GAAATGAAGAAGGGTA",74.5675093799712,-44.9231951539173 -"Sample_3_GAAATGAGTGTTAAGA",88.4209904389732,-37.4630151185525 -"Sample_3_GAACATCTCCTTGGTC",45.8295396448744,73.6595227442908 -"Sample_3_GAACCTACAACACCCG",17.6508491929183,112.199385250484 -"Sample_3_GAACGGAAGCAAATCA",96.1601543604355,-18.1014579643524 -"Sample_3_GAAGCAGGTTCAGGCC",-74.0149009776414,1.58629153140914 -"Sample_3_GAAGCAGTCTGTGCAA",-37.0388277854924,-14.010630245147 -"Sample_3_GAATAAGAGCGATTCT",72.8296270427382,-42.9020005435392 -"Sample_3_GAATAAGAGTTGAGAT",-80.118587161814,-31.8955166022893 -"Sample_3_GAATAAGGTACCGTTA",37.0873427440388,114.037517297412 -"Sample_3_GAATAAGTCCACTCCA",-103.947965887453,-35.8003472491528 -"Sample_3_GACACGCCACGACGAA",78.6985877687011,-13.3128327567774 -"Sample_3_GACACGCGTAACGTTC",18.0442010924713,7.27072094616021 -"Sample_3_GACAGAGAGGCGTACA",-18.9628799124322,-36.4223189296244 -"Sample_3_GACAGAGTCGAGAACG",0.547579399897574,-84.520400444432 -"Sample_3_GACCAATGTGAGCGAT",8.55902699976411,-16.8776328491198 -"Sample_3_GACCTGGCACTTCGAA",-90.3097944641308,10.674722814778 -"Sample_3_GACCTGGCAGTAAGAT",-7.56400237617217,-82.9621426917741 -"Sample_3_GACGGCTGTTGATTGC",-76.1896151159668,-46.440872523491 -"Sample_3_GACGGCTGTTTGGGCC",75.9331158198993,4.07171371309562 -"Sample_3_GACGGCTTCAACGCTA",87.0324157687463,-34.2902326835497 -"Sample_3_GACGTGCTCACCATAG",2.16588908456098,-81.5830009966972 -"Sample_3_GACGTTAAGAATAGGG",-104.011252246186,-20.7194910799727 -"Sample_3_GACGTTAAGTGTACGG",25.1394719960526,103.325666569236 -"Sample_3_GACTACAAGTACACCT",88.2403323501978,-4.16198649610187 -"Sample_3_GACTACATCGGAATCT",-114.133402708267,6.85098019783984 -"Sample_3_GACTGCGGTTCGGCAC",101.271903117546,-46.155704272125 -"Sample_3_GACTGCGTCAGTTGAC",10.6815121683106,107.079509421023 -"Sample_3_GACTGCGTCTGTACGA",7.20367788735959,90.8267651048671 -"Sample_3_GAGCAGAAGTGCTGCC",18.0178209225581,77.3338159499386 -"Sample_3_GAGCAGAGTCTTGATG",-53.9900034379364,-31.3142368889742 -"Sample_3_GAGGTGAGTCCTAGCG",0.439403503822016,76.0982780739615 -"Sample_3_GAGGTGATCCCTAACC",-27.5075106888044,-18.098652621173 -"Sample_3_GATCAGTAGAGTTGGC",29.8501672889337,113.598856155245 -"Sample_3_GATCAGTAGTAGCGGT",15.7085637122119,105.152831340333 -"Sample_3_GATCAGTCAAAGCGGT",36.2872685389341,100.154089309312 -"Sample_3_GATCAGTTCCGAGCCA",-87.5001810526537,-23.6560116907791 -"Sample_3_GATCAGTTCCTCGCAT",-73.9048126118487,-36.3459660519939 -"Sample_3_GATCGATGTTCCCGAG",32.3148821279401,98.1707868269855 -"Sample_3_GATCGATTCCACGCAG",-68.723753903812,-21.1340551942887 -"Sample_3_GATCGCGAGACCTTTG",70.6311501255605,-56.40740770091 -"Sample_3_GATCGCGCAAGAAAGG",-27.6854709659145,-100.755413772382 -"Sample_3_GATCGCGGTCAACTGT",-55.2996120045059,25.797070419083 -"Sample_3_GATCGCGTCAAGGTAA",-30.4491482156851,3.53128132560983 -"Sample_3_GATCGTAAGGTACTCT",23.9224214806119,109.328807260157 -"Sample_3_GATGAAAAGACACTAA",29.7579971478685,119.910461995375 -"Sample_3_GATGAAATCGTCTGAA",-101.147700385215,-28.419666430407 -"Sample_3_GATGAGGTCCTAGGGC",-58.5685205216499,-7.95457294627985 -"Sample_3_GATGCTAAGTCCCACG",12.0028359032394,104.5295679656 -"Sample_3_GATGCTACAAAGGAAG",68.024196200951,-50.71479212098 -"Sample_3_GATGCTAGTAGAGGAA",106.47851089877,-20.4087830304485 -"Sample_3_GATTCAGAGACAGGCT",19.0870396855138,-15.9717327260351 -"Sample_3_GCAAACTCATCCCATC",0.312196069993208,-45.0928233688372 -"Sample_3_GCAAACTGTGGACGAT",-55.311172865075,-9.06843112300196 -"Sample_3_GCAATCAAGTAGGCCA",-81.4705894698422,40.1093256145551 -"Sample_3_GCAATCAGTCTAGTCA",-64.0227058836399,12.1411698183884 -"Sample_3_GCAATCATCACCCTCA",-102.835868970465,-10.4606529305185 -"Sample_3_GCAGTTAGTTCAGCGC",48.3010477934818,112.035585502076 -"Sample_3_GCATACATCCTGCCAT",76.8047945229587,-66.2799451829288 -"Sample_3_GCATACATCTTTAGGG",-102.838846678042,28.9338698923746 -"Sample_3_GCATGCGGTCCCTTGT",45.0311683265489,15.7233126230682 -"Sample_3_GCATGTAAGACGCACA",79.6648384649604,-12.0519971475104 -"Sample_3_GCATGTAAGAGTAATC",16.8190683105368,-70.6168234231054 -"Sample_3_GCATGTACACCTCGTT",-12.0899538021108,-80.4674632050904 -"Sample_3_GCCTCTACAAACTGCT",27.4324399552331,-28.3312437968363 -"Sample_3_GCGACCACATGCCCGA",78.6006761688713,-60.8156584239199 -"Sample_3_GCGAGAAAGCTACCGC",26.4500899118626,115.527222102151 -"Sample_3_GCGAGAACAAGGCTCC",84.6350126370627,-11.5937833822568 -"Sample_3_GCGAGAACACCAGTTA",-116.466252612785,-16.8833594863628 -"Sample_3_GCGCAACAGGGTGTGT",16.7269195230701,32.4784724665343 -"Sample_3_GCGCAACCACCCAGTG",-79.6290212961529,-19.3758424346741 -"Sample_3_GCGCAACTCCTATGTT",93.2709147258855,28.2791837854106 -"Sample_3_GCGCAACTCTTGCATT",-67.0686878976644,-44.7156090182451 -"Sample_3_GCGCAGTTCCGTCATC",-11.8083818921129,-73.2269316625024 -"Sample_3_GCGCCAACACCGTTGG",-3.16665095015184,75.1996585025346 -"Sample_3_GCGCGATTCCGTAGTA",-92.7692885273277,19.2643496775053 -"Sample_3_GCGGGTTGTGCGCTTG",19.5993466327173,34.6496136427412 -"Sample_3_GCTCCTAGTAGAAGGA",-2.47182717504017,-69.0224064263406 -"Sample_3_GCTCTGTCAAGTCTAC",-94.4219759889657,-41.9172693753297 -"Sample_3_GCTGCAGAGTGGGATC",91.8840984412868,-7.31216614232484 -"Sample_3_GCTGCAGCACCACCAG",-71.5721197252235,-25.9665947797671 -"Sample_3_GCTGCAGGTCGATTGT",-95.7376785202069,-9.3617646933292 -"Sample_3_GCTGCAGTCCTTTACA",34.2222435934362,-32.5825673973647 -"Sample_3_GCTGCAGTCGGAGGTA",-57.6973766476299,21.4180089677708 -"Sample_3_GCTGCGAAGGCTACGA",-9.90301823088074,-80.6660052378123 -"Sample_3_GCTGCGAGTGTCAATC",48.67453911789,101.03895488011 -"Sample_3_GCTGCGATCTGATACG",-94.0257143736633,-26.8493751927244 -"Sample_3_GCTGGGTCATATGCTG",-87.6764609277742,-46.8911183184048 -"Sample_3_GCTTCCACACATCCGG",87.185916261664,-65.4986737671521 -"Sample_3_GCTTCCACATCTATGG",-51.4961308229519,1.06128038792961 -"Sample_3_GCTTCCATCTCTGTCG",30.6097216998836,6.74045923599239 -"Sample_3_GCTTGAACAGTATAAG",101.26100210319,-35.0614067921714 -"Sample_3_GCTTGAATCTAACCGA",-85.6443075331899,-32.3284483347583 -"Sample_3_GGAAAGCTCCTTGCCA",-74.700796273948,-9.57381072195846 -"Sample_3_GGAACTTCAGATGGCA",72.4429705535544,-50.7100479215016 -"Sample_3_GGAATAACAAATTGCC",-61.3328947785409,21.194484319798 -"Sample_3_GGAATAACAGTTCATG",17.7899333806613,92.2231219051555 -"Sample_3_GGACATTCACTTCGAA",-92.4053389576258,28.158136952437 -"Sample_3_GGAGCAACAAGCGCTC",101.597901284701,-15.4181852602354 -"Sample_3_GGAGCAACATCTCCCA",40.2289705786242,16.3071607141868 -"Sample_3_GGAGCAATCGCATGGC",-34.6605837124684,20.3541431403845 -"Sample_3_GGATGTTAGTCCGTAT",10.8014813550992,97.038004796725 -"Sample_3_GGATTACCATTGAGCT",-73.667432335171,-40.9703575545687 -"Sample_3_GGATTACTCACCAGGC",32.039536152757,15.986251407177 -"Sample_3_GGCAATTAGAGGTAGA",69.8530246931429,-53.6110284725154 -"Sample_3_GGCAATTAGTGTTGAA",11.7668836083992,-66.5054186205482 -"Sample_3_GGCAATTCAGATTGCT",13.7412638954785,4.94698003980291 -"Sample_3_GGCAATTGTCAAACTC",-83.9243154801231,-15.4053197089758 -"Sample_3_GGCAATTGTGATAAGT",85.4500905750761,-65.637295147885 -"Sample_3_GGCCGATGTTTCCACC",-76.222540063045,2.52798734610233 -"Sample_3_GGCGACTCATCCTAGA",-111.538583887438,-15.9161252656628 -"Sample_3_GGCGACTCATCCTTGC",30.0722626954582,52.9214423914709 -"Sample_3_GGCGTGTCACATGACT",-80.6556707746235,-9.07872901002562 -"Sample_3_GGGAATGAGATGGCGT",35.0026226295724,123.899707140681 -"Sample_3_GGGAATGGTCATCCCT",37.7915114453585,96.0788690389646 -"Sample_3_GGGACCTTCTTGGGTA",-9.92099816013442,-73.7091765080334 -"Sample_3_GGGAGATAGTGTTTGC",33.9748987499675,105.297249711119 -"Sample_3_GGGAGATCAGATGGCA",25.8836266672653,8.3027498900487 -"Sample_3_GGGAGATCATACCATG",21.6008633719529,101.998935414978 -"Sample_3_GGGAGATGTCAAACTC",-69.9338377873032,39.1658784331842 -"Sample_3_GGGATGAGTCGTCTTC",94.4757980851928,-25.8915331484327 -"Sample_3_GGGATGATCAGTTCGA",78.3969765910044,-6.42176555796155 -"Sample_3_GGGCACTAGCAGGTCA",-67.8392524541653,14.0864880687631 -"Sample_3_GGGCACTCAAAGCAAT",8.12560417616508,-4.43152026956759 -"Sample_3_GGGCACTTCCTAGGGC",9.1389414490161,107.025880456633 -"Sample_3_GGGCACTTCTATCGCC",-96.7855436377679,-27.6940933513736 -"Sample_3_GGGCATCCACGGCGTT",-115.168620125592,-23.7485186128432 -"Sample_3_GGGCATCCATCACAAC",7.72259268516294,8.29574221891671 -"Sample_3_GGGCATCGTTCCTCCA",97.4656142928689,-46.0368719015216 -"Sample_3_GGGCATCTCCGCATCT",70.0670883658347,-32.4590563340892 -"Sample_3_GGTATTGGTGAGGGTT",20.9317213164813,54.4578547794437 -"Sample_3_GGTATTGGTTCCCGAG",20.7376641884667,-1.21011506740351 -"Sample_3_GGTGAAGAGAGGTAGA",7.66674669103291,-38.0975097881589 -"Sample_3_GGTGAAGTCCATGAGT",64.8674349767649,-4.68477886734592 -"Sample_3_GGTGAAGTCCTCATTA",-64.2140859364271,-8.69104703645848 -"Sample_3_GGTGCGTAGAGTACAT",-118.240679575749,-1.6873962423326 -"Sample_3_GGTGCGTAGTACATGA",40.9412980401209,26.8798668384084 -"Sample_3_GGTGTTAGTAGTAGTA",1.67440826260339,-73.6998922077558 -"Sample_3_GTAACGTAGGACAGCT",80.3555559440587,-33.817117006819 -"Sample_3_GTAACGTTCACTTACT",60.3984841456436,-20.490539835712 -"Sample_3_GTAACTGCATCGGTTA",-61.2899049281096,9.65178011079973 -"Sample_3_GTAACTGTCCGAACGC",-31.9184735560491,15.4026952955367 -"Sample_3_GTACGTAAGACACTAA",-22.7018848115197,15.8886324142413 -"Sample_3_GTACGTACATGCTGGC",76.4184445518236,-33.0175503193229 -"Sample_3_GTACGTATCCTGTAGA",6.83768882293195,85.3413397923371 -"Sample_3_GTACTTTGTGTCAATC",45.0541184402355,57.1715178986744 -"Sample_3_GTAGGCCAGCGCTCCA",98.2533500166295,12.8271774630067 -"Sample_3_GTAGGCCGTAACGTTC",-92.4784503083885,-32.7862108293186 -"Sample_3_GTATCTTTCCTATGTT",88.0820170389299,-22.7510703244859 -"Sample_3_GTATTCTCAGCTGCTG",58.7130157712047,-8.88206198765363 -"Sample_3_GTATTCTGTCCCTACT",25.0740336813593,-17.5876361662439 -"Sample_3_GTATTCTTCCAATGGT",82.6475114770913,-31.960545605642 -"Sample_3_GTCACAAGTCCGAAGA",32.8663475074123,103.350454731842 -"Sample_3_GTCACAAGTTCCACAA",-92.3512564716913,-23.9023980660651 -"Sample_3_GTCACGGCACCGGAAA",17.7991746383847,101.951623978451 -"Sample_3_GTCACGGGTTGCCTCT",93.2470608639745,-55.7449168172779 -"Sample_3_GTCACGGTCAAGGCTT",14.7114106857586,-69.1349006072309 -"Sample_3_GTCATTTAGCTTTGGT",-103.205969477331,-26.627100048673 -"Sample_3_GTCATTTTCAGTTTGG",102.622643116764,-20.063515985643 -"Sample_3_GTCCTCAAGTATCGAA",27.141583182429,110.806552726207 -"Sample_3_GTCGGGTGTCCTCTTG",90.8608229581012,-0.641718337469626 -"Sample_3_GTCGTAATCCATGAGT",22.4235400400692,-8.19218747848075 -"Sample_3_GTCTCGTAGTTCGCAT",13.4698173560522,101.802410951036 -"Sample_3_GTCTTCGGTATAAACG",58.1450211446758,-56.2457061835784 -"Sample_3_GTGAAGGAGTACGACG",-86.8729112210478,-15.9404053710295 -"Sample_3_GTGAAGGCAATAACGA",-95.3481357317678,-16.8254657717017 -"Sample_3_GTGAAGGCATGTTGAC",87.1835029637896,-8.33406025482923 -"Sample_3_GTGAAGGGTCCTCTTG",25.9324168476745,9.4077446181264 -"Sample_3_GTGAAGGTCATGTAGC",-19.5388431901301,-38.9080703382855 -"Sample_3_GTGCAGCAGGCTACGA",87.2718138147517,-62.9444687952254 -"Sample_3_GTGCAGCTCAAAGACA",11.2610483664617,100.938483556103 -"Sample_3_GTGCATACATCCGGGT",-103.979386955008,27.5993313743026 -"Sample_3_GTGCATATCACCGTAA",-50.1811888185221,-25.8179918338532 -"Sample_3_GTGCATATCATTATCC",-106.407192945962,16.6533806696613 -"Sample_3_GTGCGGTAGAGGTACC",95.4893564285541,-11.23937937715 -"Sample_3_GTGCGGTCAAGCCGTC",-90.8007116483174,-31.3883318542963 -"Sample_3_GTGCGGTCACATTCGA",10.0738644302886,112.410373067319 -"Sample_3_GTGCTTCAGCCAACAG",-68.4732038448055,-10.9849228146516 -"Sample_3_GTGGGTCTCAAGAAGT",-17.0598355401503,-21.7284143290503 -"Sample_3_GTGTGCGTCGGTGTCG",45.4212994374258,56.8269045491315 -"Sample_3_GTGTTAGCATCCTAGA",-87.948746771768,22.0232708802103 -"Sample_3_GTGTTAGGTAGATTAG",103.349679231592,-21.6572492131032 -"Sample_3_GTTACAGCATTAGGCT",21.4223738996692,97.7185492506909 -"Sample_3_GTTCATTCATGCATGT",-92.8965741770061,40.5473414029629 -"Sample_3_GTTCATTTCCAAACAC",29.2739035219006,98.790706051144 -"Sample_3_GTTCTCGTCTAGCACA",-89.2018987576555,-6.61775190223223 -"Sample_3_GTTTCTAAGATTACCC",16.2108209398335,13.0373843627089 -"Sample_3_GTTTCTAAGGTGCAAC",31.5889962765649,64.78998062772 -"Sample_3_GTTTCTACATCACGAT",30.7412172660606,111.144858749266 -"Sample_3_TAAACCGAGTTCGATC",-32.6598573410591,-15.7638587174472 -"Sample_3_TAAACCGGTCTGCGGT",68.8253940049951,-15.9886900677785 -"Sample_3_TAAACCGGTTCGCGAC",47.7963554462015,-29.7498369155775 -"Sample_3_TAAGAGAGTACTTAGC",-47.2519464099491,-35.6816986633052 -"Sample_3_TAAGAGATCCGAAGAG",18.1112396266449,110.712706451409 -"Sample_3_TAAGCGTAGGACTGGT",-62.6200607267472,-32.7056800154899 -"Sample_3_TAAGTGCCAGACAAAT",-94.4407688640639,41.8379689911132 -"Sample_3_TAAGTGCTCGCCATAA",-104.485398066334,8.5172676393488 -"Sample_3_TACACGAGTCACTTCC",-69.7879678126978,2.86043568176558 -"Sample_3_TACACGAGTTTCGCTC",-0.499838995667823,79.9760985010356 -"Sample_3_TACACGATCCCAGGTG",0.921655396454602,-41.396284368738 -"Sample_3_TACCTATGTCCAACTA",83.2962961725923,17.1550670511091 -"Sample_3_TACCTTAAGCTAACAA",-49.2691534068374,16.2698988382087 -"Sample_3_TACCTTATCTGGCGTG",-51.124039819293,-52.4772458997565 -"Sample_3_TACGGGCCATCCTAGA",33.7880837169198,62.7134726445382 -"Sample_3_TACGGGCCATTACCTT",0.314804271340214,-6.79234570481297 -"Sample_3_TACGGTAAGGATGGAA",102.695329207998,-1.15755454709175 -"Sample_3_TACTCATTCGGCCGAT",-0.823128317990663,-33.6113929670873 -"Sample_3_TACTCGCCAACTGCGC",-78.6961668763027,-36.8603910576995 -"Sample_3_TACTCGCTCCGAATGT",32.8978665291618,110.067064457684 -"Sample_3_TACTCGCTCGTAGGTT",34.9431706873761,104.441689021196 -"Sample_3_TACTTACTCCGTTGTC",-91.7476918300769,-51.2600727705715 -"Sample_3_TACTTGTGTACTTAGC",-98.7373694443146,-20.4699428357904 -"Sample_3_TAGCCGGCAATGTTGC",68.244002642975,-31.6029126315986 -"Sample_3_TAGCCGGTCGAGAACG",25.72154602304,-15.6263106567453 -"Sample_3_TAGGCATCAGTCCTTC",30.6925846372712,24.4368412075341 -"Sample_3_TAGGCATGTTGTACAC",50.6030573571918,123.201946968492 -"Sample_3_TAGTGGTCAAGGGTCA",-86.2678595081538,0.548013093994668 -"Sample_3_TAGTGGTTCAATACCG",4.02541818356571,108.264899141601 -"Sample_3_TAGTGGTTCCTAGTGA",91.5701004094122,-42.7008351771056 -"Sample_3_TAGTTGGAGTAGGCCA",-84.8044780872508,-2.62571823424126 -"Sample_3_TATCAGGCAACTGCTA",-59.6208701727812,-12.8064117244567 -"Sample_3_TATCAGGGTCGGATCC",60.5288918314858,-10.6437510600876 -"Sample_3_TATCTCAAGACTTGAA",-88.031565172762,15.3940344644509 -"Sample_3_TATCTCAAGGGAGTAA",-105.443115379917,-31.9152681538606 -"Sample_3_TATCTCAAGGTCATCT",21.2598389818499,7.27143094202734 -"Sample_3_TATCTCAGTCTACCTC",23.5403886992541,-3.69306337112817 -"Sample_3_TATCTCATCATGTAGC",86.291910230569,-40.3287524861383 -"Sample_3_TATGCCCCAAGCTGTT",98.0613883564302,-44.1400462814125 -"Sample_3_TATGCCCCAATCACAC",34.1754881430792,119.184767718543 -"Sample_3_TATTACCCAAACCCAT",20.7928816422007,81.2714994540771 -"Sample_3_TATTACCCACGAAAGC",11.7637018241949,86.7803887672538 -"Sample_3_TATTACCGTGATGTGG",-86.8500242118658,-54.1644240247688 -"Sample_3_TCAACGACAATCTACG",-101.630054564422,-24.3667196184548 -"Sample_3_TCAACGATCCTTTCGG",3.3115956666429,-46.0450770909023 -"Sample_3_TCAATCTGTGCAGACA",2.73142593841694,-68.4333413687407 -"Sample_3_TCAATCTGTGCGATAG",101.22258920762,-9.49690614582749 -"Sample_3_TCACAAGCAATCGAAA",-80.4383004545719,22.8844342419333 -"Sample_3_TCACAAGGTCTCATCC",-41.314530085729,-27.6035930909264 -"Sample_3_TCACGAACATACTACG",-2.33107826042492,-83.3706799135997 -"Sample_3_TCACGAAGTAGAAAGG",-1.43728556944989,-65.1491860212806 -"Sample_3_TCACGAATCATCGATG",89.896236708819,-48.9585691917822 -"Sample_3_TCACGAATCGTCACGG",81.823928724591,-32.9136983997686 -"Sample_3_TCAGATGAGTTGAGTA",29.4483179287875,59.7519399384398 -"Sample_3_TCAGCAAGTGCTTCTC",100.513464918013,-41.9652682326998 -"Sample_3_TCAGCTCAGACAGGCT",27.3278500761851,-8.64469065395098 -"Sample_3_TCAGCTCGTCGAGTTT",86.2652981832524,5.94293689701171 -"Sample_3_TCAGGATTCATTTGGG",89.8384299997179,-27.7832464750939 -"Sample_3_TCAGGTAAGTCCGGTC",5.45439442762518,-6.71680551388715 -"Sample_3_TCAGGTACAGGATTGG",9.68311387698833,-69.8502292796489 -"Sample_3_TCAGGTAGTCCGTCAG",-61.9495829748123,-25.2401903032974 -"Sample_3_TCAGGTATCGAGGTAG",-88.0675871778264,26.3991812181507 -"Sample_3_TCAGGTATCGCCTGTT",43.8647951229099,20.712075820844 -"Sample_3_TCATTACCAGTATAAG",-79.5560164446253,33.9308992960857 -"Sample_3_TCATTTGAGGCAATTA",104.667181000786,-15.4070967150847 -"Sample_3_TCATTTGAGTGGTAGC",8.1489196411876,-79.8572619599054 -"Sample_3_TCATTTGGTGTTGAGG",-6.40423636499785,-69.1332384198861 -"Sample_3_TCCACACAGTACACCT",15.2672169074239,97.7613662005966 -"Sample_3_TCCACACCAGTTTACG",-105.628440346404,-1.7545871383782 -"Sample_3_TCCCGATAGACAGGCT",12.0209856678417,106.612850627589 -"Sample_3_TCCCGATCAAGTTCTG",-79.4244388287631,-22.7922102975742 -"Sample_3_TCCCGATTCGTAGATC",42.1290178260787,53.170792341025 -"Sample_3_TCGAGGCGTAGAAGGA",16.5774181326324,42.1101112786712 -"Sample_3_TCGAGGCTCCTCAACC",67.9047428690038,-46.7713970967891 -"Sample_3_TCGAGGCTCGTTGACA",-99.0578936881951,18.3365141545523 -"Sample_3_TCGCGAGGTCAGTGGA",24.42970131987,-10.3997658083381 -"Sample_3_TCGCGAGTCGACAGCC",-29.9514513056116,11.8673117750517 -"Sample_3_TCGGGACCAGACGTAG",99.8225988363058,3.53906448686175 -"Sample_3_TCGGGACGTGATAAGT",-100.029797014036,-22.6468025266522 -"Sample_3_TCGGGACTCACCCGAG",26.9340713771002,93.1682096372902 -"Sample_3_TCGGGACTCCGAAGAG",-90.6620573642266,-13.096472498001 -"Sample_3_TCGGTAACACAGGCCT",65.9300595041133,-20.1246480335983 -"Sample_3_TCGGTAACATTAACCG",-50.7609253655436,-8.06713117524444 -"Sample_3_TCGTACCAGATCTGCT",59.4093887836351,-35.5305756656352 -"Sample_3_TCGTACCGTTTGACAC",-82.4498512585664,-19.1919569938338 -"Sample_3_TCGTACCTCAAACCGT",-56.9555213081211,-43.7741634832887 -"Sample_3_TCGTACCTCCCGACTT",75.7320853943382,-49.2712780350897 -"Sample_3_TCGTAGACATCTATGG",-106.821955673967,15.2439879745504 -"Sample_3_TCTATTGAGGCGACAT",93.1632326034498,2.25627044447417 -"Sample_3_TCTATTGGTAGCTCCG",-10.4309199613591,-32.7737159300297 -"Sample_3_TCTCATAAGACTTTCG",-5.70246008678457,-62.7652853063444 -"Sample_3_TCTCTAATCCGTAGTA",19.3857982833781,-5.60062179089716 -"Sample_3_TCTGAGAAGAGTGAGA",-108.369329921567,-40.3741254395926 -"Sample_3_TCTGAGACACCACGTG",-3.25198999214283,-72.5267844674043 -"Sample_3_TCTGAGACAGATCTGT",60.9852612475827,-25.0529185784501 -"Sample_3_TCTGGAAAGTACGTTC",92.8775099675848,21.8398391428671 -"Sample_3_TCTTCGGAGGGATGGG",90.2007814258912,5.35326177118215 -"Sample_3_TCTTTCCAGACTGGGT",-89.9377034333026,36.147626483444 -"Sample_3_TCTTTCCGTGCTTCTC",85.4377508496155,-43.652315095185 -"Sample_3_TGAAAGATCCCTAATT",-107.088944268062,-52.4815032528673 -"Sample_3_TGAAAGATCGTTGCCT",8.65999945189868,-79.1221705134369 -"Sample_3_TGACAACTCCTACAGA",-77.7304956172872,43.2114798368085 -"Sample_3_TGACTTTCAATGGATA",-9.57658969137847,-23.2576208001449 -"Sample_3_TGACTTTTCTAGCACA",90.8957330656178,-49.2927937208801 -"Sample_3_TGAGAGGAGCAATCTC",-73.0940414102067,25.3572124324876 -"Sample_3_TGAGCATGTCAAAGCG",12.3113795313682,12.1488512384694 -"Sample_3_TGAGCCGGTGCCTTGG",-108.812849645898,-48.0764421592868 -"Sample_3_TGAGGGAAGCATGGCA",3.24340015127872,97.9108350403876 -"Sample_3_TGAGGGACATGGGAAC",18.4238455211989,2.91183455950196 -"Sample_3_TGAGGGATCACATGCA",76.0124826125774,-41.002960501433 -"Sample_3_TGATTTCTCCTTCAAT",-83.1861437247473,-21.8301367654165 -"Sample_3_TGCACCTAGGGCACTA",-114.945997721933,17.3362409792535 -"Sample_3_TGCCCATCAGTAAGCG",69.9664979155959,-27.4137472416377 -"Sample_3_TGCCCTAGTTCAACCA",-84.6389829230544,-28.552640441795 -"Sample_3_TGCCCTATCAGGCAAG",39.1505656315486,67.4295212139196 -"Sample_3_TGCGGGTAGACCTAGG",95.7286182898859,-28.652802259973 -"Sample_3_TGCGGGTGTTATGTGC",-24.0827353362203,-105.66138031078 -"Sample_3_TGCGTGGGTAATTGGA",-60.1703211301641,-5.3258060663503 -"Sample_3_TGCTACCAGCTCCCAG",53.3499922999533,114.239477450302 -"Sample_3_TGCTGCTGTCCAACTA",37.0181387490602,19.249344145293 -"Sample_3_TGGACGCAGGAGCGAG",11.6384017856689,28.7943151856494 -"Sample_3_TGGACGCGTAACGCGA",93.3210162440113,-5.32044613954045 -"Sample_3_TGGCGCAAGTACGCGA",68.3877226978107,-11.1307534312916 -"Sample_3_TGGCGCAGTTCCCGAG",-79.5918079244972,-27.5749403173859 -"Sample_3_TGGCGCATCCTACAGA",65.7377854348986,-59.8746781801185 -"Sample_3_TGGGAAGAGGAACTGC",17.2342868540137,115.545123719725 -"Sample_3_TGGGAAGTCTATCCTA",-74.1837814775384,-64.0608422099456 -"Sample_3_TGGGCGTCAGTTAACC",93.9530039672907,18.4753190869007 -"Sample_3_TGGTTAGAGCGCCTTG",-79.7048442867715,-4.13154113418415 -"Sample_3_TGGTTAGAGGAATTAC",5.04400134772982,6.63827770927291 -"Sample_3_TGGTTAGGTCAATACC",-58.3186595130052,-18.5086501234199 -"Sample_3_TGGTTCCTCCACTCCA",39.9251174640038,21.4267048664052 -"Sample_3_TGTATTCAGATGTCGG",38.4677888263851,0.45379559995881 -"Sample_3_TGTATTCCAGCGTCCA",-89.9409837968765,31.4631838518187 -"Sample_3_TGTATTCTCTGATACG",-97.4964535475715,19.9512822755461 -"Sample_3_TGTCCCAAGAGACTAT",-76.4974824587936,46.9743568333854 -"Sample_3_TGTCCCATCTTGTACT",-71.3511370166084,6.56306451820355 -"Sample_3_TGTGGTACATGCAACT",39.4473402508967,111.535238118147 -"Sample_3_TGTGGTAGTGCATCTA",7.10426450248863,-82.9369997773326 -"Sample_3_TGTGGTATCCCGACTT",78.4081852449478,-48.9819361714931 -"Sample_3_TGTGTTTAGGCCCTCA",-62.7926306601147,35.3720676271338 -"Sample_3_TGTGTTTGTTGTGGAG",30.9741908040504,-11.345936564541 -"Sample_3_TGTGTTTTCATGCTCC",104.208011199622,9.51138169636523 -"Sample_3_TGTTCCGAGCCACCTG",-70.9977533672423,-9.36463167117039 -"Sample_3_TGTTCCGCAGTGACAG",-26.4463024672805,-109.337234420749 -"Sample_3_TTAGGACAGGGTCGAT",11.3632576559864,-77.8601210344822 -"Sample_3_TTAGGACGTGGTACAG",79.1118723233462,-22.3488635469971 -"Sample_3_TTAGGCACATAAAGGT",70.3549633842434,1.84014119312199 -"Sample_3_TTAGTTCTCACTTACT",62.4089756454837,-35.4403546571757 -"Sample_3_TTAGTTCTCTTGAGAC",58.7796979120063,-0.0271685895463855 -"Sample_3_TTATGCTTCACGAAGG",45.5308607643242,115.844641680143 -"Sample_3_TTCGGTCAGCCAGAAC",-97.9682455707372,38.7469458723984 -"Sample_3_TTCGGTCAGTTCCACA",-23.3713590013491,-182.258104685731 -"Sample_3_TTCTACACATTACCTT",0.254504386273566,-59.9047744353503 -"Sample_3_TTCTACATCGCAAGCC",73.8462091860532,-45.3985061646403 -"Sample_3_TTCTCAACACACTGCG",10.7626724833328,-9.98486763477297 -"Sample_3_TTCTCCTGTTCCCGAG",-96.6209728561418,36.2096986658948 -"Sample_3_TTCTCCTTCCGAATGT",83.5031415126444,-26.1442503669057 -"Sample_3_TTCTTAGGTTTAGGAA",28.9495073508921,-32.5626090031457 -"Sample_3_TTGAACGAGCCATCGC",-105.148176516956,-20.8983948049049 -"Sample_3_TTGAACGGTAGCTCCG",59.2134988243557,-49.3808788477427 -"Sample_3_TTGAACGGTCTCAACA",-21.5453363768543,-20.6965751370852 -"Sample_3_TTGAACGTCAGTTCGA",44.1849498140325,119.675581588723 -"Sample_3_TTGACTTTCCCTAACC",-38.6819051287607,-14.9106120985413 -"Sample_3_TTGCCGTCACCTTGTC",-60.4108707109779,32.4359474197325 -"Sample_3_TTGCCGTTCTTGCAAG",68.537181910234,-36.0432421670134 -"Sample_3_TTGCGTCCAGCCTTGG",28.8204346851915,-21.6597873710102 -"Sample_3_TTGCGTCTCACGGTTA",35.4472527099948,65.9557865861556 -"Sample_3_TTGCGTCTCCTATTCA",101.305743819472,-53.8367077174917 -"Sample_3_TTGCGTCTCTTCTGGC",18.12843422474,26.4974362475557 -"Sample_3_TTGGCAAAGAGATGAG",-81.7729811990648,36.2385663619686 -"Sample_3_TTGGCAAGTATCACCA",54.8596085371519,-31.4952664224703 -"Sample_3_TTGTAGGAGTCGTACT",83.2534083744785,-44.0620798998009 -"Sample_3_TTTATGCAGCCACTAT",79.9382868010851,-15.0961227011584 -"Sample_3_TTTATGCCAGTTAACC",71.9859482729672,-8.01500107610772 -"Sample_3_TTTGCGCAGGCTACGA",-70.5263305916464,32.8369668566647 -"Sample_3_TTTGCGCTCACTATTC",84.8029657908958,-52.4615283076168 -"Sample_3_TTTGGTTTCTGTTTGT",-14.948555156303,-71.2189783461626 -"Sample_3_TTTGTCACAATTGCTG",41.354396082226,-47.8530980910947 -"Sample_3_TTTGTCAGTAGGAGTC",-24.485271076986,-185.05512194268 -"Sample_3_TTTGTCATCCTCATTA",-110.062397396883,-46.673339601932 -"Sample_4_AAACCTGTCTGCTGTC",-6.50201196928851,-75.2527091390875 -"Sample_4_AAACGGGGTTTGCATG",2.7100268962179,74.6059157898481 -"Sample_4_AAACGGGTCCTTTACA",101.358113894025,-35.7667211745719 -"Sample_4_AAAGTAGCAAACAACA",5.80952772316213,109.884049819953 -"Sample_4_AAAGTAGCATAGAAAC",8.16788330218331,97.2476867843244 -"Sample_4_AAATGCCCATGATCCA",101.271931838429,-19.4638116259967 -"Sample_4_AAATGCCGTTAGTGGG",29.6737731015455,94.9889197036996 -"Sample_4_AAATGCCTCAGTTAGC",99.078939526178,-1.2369720465038 -"Sample_4_AACACGTGTCGCTTCT",102.892117770203,-11.6705199147961 -"Sample_4_AACCATGGTCTCACCT",17.6260108452077,35.2533145784182 -"Sample_4_AACTCAGGTACCGTAT",-105.81334544497,2.15124185413337 -"Sample_4_AACTCAGGTCCATCCT",-44.6937391471392,-30.2282227247873 -"Sample_4_AACTCAGGTCGCTTCT",96.8637578340588,-1.45266130514182 -"Sample_4_AACTCAGTCCTTTCTC",-91.8168060488331,-0.467874397958568 -"Sample_4_AACTCTTCACACATGT",-3.77657283671984,3.69364592275753 -"Sample_4_AACTCTTGTGCCTGTG",-2.64327097965193,78.4925793055898 -"Sample_4_AACTCTTTCAAACAAG",-90.3358518099833,23.5041049545905 -"Sample_4_AACTCTTTCCTCGCAT",0.049309419338142,-67.2479501114788 -"Sample_4_AACTGGTGTGCGGTAA",96.351340805533,-3.72921231568845 -"Sample_4_AACTTTCGTAGCGATG",-102.938655796961,41.4257706448143 -"Sample_4_AAGACCTAGATGTCGG",90.2573369511172,-10.6102830163562 -"Sample_4_AAGCCGCAGCTGAACG",-49.990198886996,-14.336435027362 -"Sample_4_AAGCCGCGTTCGCGAC",-12.8260169866955,-44.4741414588336 -"Sample_4_AAGGAGCCAATACGCT",80.3862083974976,22.5054104747264 -"Sample_4_AAGGCAGAGAGGTAGA",35.2841134803563,57.6968585879578 -"Sample_4_AAGGCAGAGGAATCGC",15.0901466859856,48.1546440063977 -"Sample_4_AAGGTTCGTCATATGC",6.98738654211499,117.361271749507 -"Sample_4_AAGGTTCTCAGTGCAT",103.140026905917,-6.38760368318256 -"Sample_4_AAGTCTGAGAGGTAGA",-29.9656032706129,-103.851023644309 -"Sample_4_AATCCAGCAAGCCCAC",-108.202033529177,-1.17680633872988 -"Sample_4_AATCCAGGTAAGCACG",-0.87582393354975,-50.0667267777346 -"Sample_4_AATCCAGTCACAACGT",69.4841276964032,-39.2004555899692 -"Sample_4_AATCGGTCATCACAAC",77.7947657326142,-54.3853163279953 -"Sample_4_ACACCCTCAGCTGTGC",-81.1942373340807,7.30787136717204 -"Sample_4_ACACCCTCATACCATG",-56.0324256484827,-24.1573102207089 -"Sample_4_ACACTGAAGCTGAACG",19.5817898903468,47.5220766191852 -"Sample_4_ACACTGATCTTACCTA",-108.659343762051,-5.93696862127728 -"Sample_4_ACAGCCGAGCTACCGC",-38.8786668475398,-10.4754248037512 -"Sample_4_ACAGCTAAGGCAGTCA",-89.2363477923019,-26.4343366900234 -"Sample_4_ACATACGAGAGAACAG",62.7808847265955,-31.650886501446 -"Sample_4_ACATACGAGGCTAGAC",28.2970218575388,-42.6596668770304 -"Sample_4_ACATACGTCAACCAAC",-70.4041653811017,35.8819652268585 -"Sample_4_ACATCAGAGGTCGGAT",-58.5143857545527,-20.6192507701038 -"Sample_4_ACATCAGGTCCGTTAA",-72.7063921953172,19.8245199791908 -"Sample_4_ACATGGTGTCGCTTCT",83.2459073232802,-1.05717358385131 -"Sample_4_ACCAGTACACAACTGT",54.7593226978277,21.9153593969112 -"Sample_4_ACCAGTATCCTCGCAT",73.8013896885949,-13.8603001604269 -"Sample_4_ACCAGTATCTGAAAGA",-67.1661034207964,-16.5469817981407 -"Sample_4_ACCGTAACAAGAAGAG",23.654068238439,118.941525956455 -"Sample_4_ACCGTAACATTGAGCT",30.414980082205,10.6177155023428 -"Sample_4_ACCGTAATCGCTTGTC",95.9594541569007,-55.3666786316898 -"Sample_4_ACCTTTAAGCGATATA",26.2374512974107,99.0808909153211 -"Sample_4_ACGAGCCAGACCTAGG",41.8736671177217,107.359763804646 -"Sample_4_ACGAGCCGTCTAGTCA",-48.1703093899076,-0.218081737866684 -"Sample_4_ACGATACAGAGACGAA",77.9088920509229,-43.2827171189575 -"Sample_4_ACGATACCAATACGCT",-67.2156918051088,27.8692425630977 -"Sample_4_ACGATACGTCTGCCAG",50.8389209159713,109.091197346254 -"Sample_4_ACGATGTAGAGCTTCT",91.8762117295643,-24.1509906311266 -"Sample_4_ACGATGTCATTCACTT",88.1893647204161,-13.9071323595002 -"Sample_4_ACGATGTGTCATATCG",10.803347867076,15.2452694051016 -"Sample_4_ACGCAGCCAAGCGCTC",78.2122187402688,-25.5075121720711 -"Sample_4_ACGCCGAAGGTGTTAA",-82.6976609074374,25.7050418064868 -"Sample_4_ACGGAGAAGACAGAGA",16.1044329517974,38.9531816747155 -"Sample_4_ACGGCCAGTTGATTCG",8.38781277654599,-66.7056786188786 -"Sample_4_ACGTCAAAGGTAGCTG",-103.024090393046,15.150272997327 -"Sample_4_ACGTCAATCGGAGCAA",-82.9024907945615,42.1537924866759 -"Sample_4_ACGTCAATCGGCATCG",90.1648441300309,-4.47753024526399 -"Sample_4_ACTATCTAGCTACCTA",-88.3730294405064,-10.4021260415393 -"Sample_4_ACTGAACGTCAATGTC",41.8416785534663,116.127820144678 -"Sample_4_ACTGAACTCCCAACGG",45.3068873926853,102.532263241647 -"Sample_4_ACTGCTCCATTAGCCA",-58.6868868127512,7.34552841064456 -"Sample_4_ACTGTCCAGAAGAAGC",63.8110339697325,-56.8354763112036 -"Sample_4_ACTGTCCGTACCGCTG",76.7242827496607,-18.7613460909258 -"Sample_4_ACTTACTAGCCGGTAA",27.3210402413062,51.3679826476721 -"Sample_4_ACTTACTGTACCGGCT",-92.1446745472019,39.0702849586389 -"Sample_4_ACTTGTTCAGATGGCA",-67.7203450472749,-4.94940667804298 -"Sample_4_ACTTGTTTCACGATGT",-9.38273923487702,-34.1777281820926 -"Sample_4_ACTTTCACACAGTCGC",47.1416036758822,97.4437493840149 -"Sample_4_ACTTTCAGTAAGGATT",63.9627818542167,-60.5559127919247 -"Sample_4_ACTTTCATCAGTTGAC",11.5540346310015,89.7097233902147 -"Sample_4_AGAATAGTCAACACCA",-42.4468038669484,-21.6927745986027 -"Sample_4_AGAGCGAAGGTCATCT",96.0535500355768,7.47742785243687 -"Sample_4_AGAGCGAGTACCGGCT",-100.504421802599,14.6565212992308 -"Sample_4_AGAGCTTAGGAGTTTA",-95.2189248727594,3.93113795490831 -"Sample_4_AGAGCTTTCAAGAAGT",-51.6524857412026,-23.2492543689162 -"Sample_4_AGAGTGGTCCAGTAGT",21.925697306323,84.6218161684733 -"Sample_4_AGAGTGGTCCTTGACC",20.7444068747451,52.4044455255093 -"Sample_4_AGCAGCCAGTTCCACA",36.2519955604779,57.5365673454297 -"Sample_4_AGCCTAACAGTCTTCC",-58.6393071862318,29.0599884221292 -"Sample_4_AGCGTATAGTGCAAGC",95.9682757802001,-50.6071953081397 -"Sample_4_AGCGTATGTGACGGTA",-67.0011598266809,24.5865877416843 -"Sample_4_AGCGTCGCAGCGAACA",81.8941349385436,8.03776361919861 -"Sample_4_AGCTCCTTCAGAGCTT",-103.605671029021,-47.6961344128484 -"Sample_4_AGCTCTCCATTTGCCC",-27.793319933101,-103.527337361673 -"Sample_4_AGCTTGAAGACTAGAT",-73.6456388194671,-0.523472917322079 -"Sample_4_AGCTTGAGTCTCACCT",18.6337545927031,109.287788787733 -"Sample_4_AGGCCACCAAGCGAGT",-29.2976934981792,-11.6249357369455 -"Sample_4_AGGCCACCAAGTAATG",-72.3522139600949,40.9841765306337 -"Sample_4_AGGCCACCACGGCTAC",21.9302480098722,-16.5055925699759 -"Sample_4_AGGCCACTCTCTTATG",-114.573793584146,-10.6147288304693 -"Sample_4_AGGCCGTGTAGAGGAA",72.9511225745262,8.74505144520364 -"Sample_4_AGGCCGTTCATCGATG",103.18913816229,-58.569538461264 -"Sample_4_AGGGATGGTCTCAACA",-10.1075227699848,-76.6994292126926 -"Sample_4_AGGGTGACAGTAAGAT",-94.3149343099978,26.5307222238082 -"Sample_4_AGGTCATCACAGACTT",-73.1553279766447,30.4824614951355 -"Sample_4_AGGTCATTCGAGAGCA",-84.1413935204647,-35.5511542257633 -"Sample_4_AGGTCCGCAGCTGTGC",-29.0622533398603,-110.095128070895 -"Sample_4_AGTAGTCTCACTCCTG",67.1525669300176,-29.5812763462416 -"Sample_4_AGTCTTTAGCCTCGTG",1.86115181790523,-29.6420579365901 -"Sample_4_AGTCTTTAGGATTCGG",10.1256513576761,-4.30558580474264 -"Sample_4_AGTGAGGAGGCTCTTA",-105.598330379154,-8.19881704671682 -"Sample_4_AGTGAGGGTGCACCAC",84.3994912440798,20.2783698062934 -"Sample_4_AGTGAGGTCTGATACG",18.5972408242213,-8.0504001829773 -"Sample_4_AGTGTCATCCTTTCGG",3.76055375276533,-19.2095243864948 -"Sample_4_AGTGTCATCTGGGCCA",-72.5649002922269,-65.4493058494417 -"Sample_4_AGTGTCATCTTACCTA",-1.68017127917465,-70.7929771682995 -"Sample_4_AGTTGGTTCGTCCAGG",86.975789498017,-75.4978737961156 -"Sample_4_ATAACGCAGTGGCACA",-32.0975752049638,-107.313888746389 -"Sample_4_ATAACGCCAGTCAGAG",1.34903756277658,-43.739664562783 -"Sample_4_ATAACGCGTAGCTTGT",33.9386548768191,101.589703047824 -"Sample_4_ATAAGAGGTGCCTGGT",-71.1787825915139,12.5922883881267 -"Sample_4_ATAGACCTCTTTACGT",-79.6049281303483,-0.07117791749059 -"Sample_4_ATCACGAAGTGGAGTC",14.0652832200246,-2.53668606522005 -"Sample_4_ATCACGATCAGCAACT",12.1397199467562,95.1971847034983 -"Sample_4_ATCATCTCAACACGCC",9.57549015306774,92.5428929180738 -"Sample_4_ATCATGGAGGTGATAT",-93.5688511066785,-45.5242819369669 -"Sample_4_ATCATGGCAGCCAGAA",-25.7024917778991,-178.380776746628 -"Sample_4_ATCCACCAGTAGATGT",-48.7049669776907,3.50997320095328 -"Sample_4_ATCCACCCATCGATGT",-23.9791599443471,-183.997684658304 -"Sample_4_ATCCACCGTAACGACG",27.6480781725491,-23.2373999957149 -"Sample_4_ATCCGAAAGAAGATTC",-41.5254089828748,-14.8825245196164 -"Sample_4_ATCCGAACACTAGTAC",-78.9846979684657,-45.3132201528808 -"Sample_4_ATCCGAAGTACACCGC",-75.9884866859465,-13.950975906493 -"Sample_4_ATCGAGTCACAACTGT",51.3438154009247,-19.4008651333346 -"Sample_4_ATCTACTCACGAGGTA",-79.6519219991146,-35.1368065117345 -"Sample_4_ATCTACTCATGGGACA",34.5518946841832,51.8437849472158 -"Sample_4_ATCTACTTCGGCCGAT",37.0442581445901,73.9418253396152 -"Sample_4_ATCTGCCAGCGCTCCA",86.1872500885706,-19.0659499027385 -"Sample_4_ATCTGCCAGTGACATA",-85.4261977517991,33.8560933161068 -"Sample_4_ATCTGCCAGTTGAGTA",-84.8098426502464,-43.149317494675 -"Sample_4_ATCTGCCCACTTAACG",76.9833669070282,8.58681765963335 -"Sample_4_ATCTGCCCATCCCACT",-76.09067613128,-16.6860992171551 -"Sample_4_ATCTGCCGTCTCTTTA",1.21187093883957,-5.67720539797491 -"Sample_4_ATCTGCCGTTAGTGGG",89.9139404755933,32.3678572598145 -"Sample_4_ATCTGCCTCGCTTAGA",25.9782957074663,-0.879621827783685 -"Sample_4_ATGAGGGGTACCGTAT",3.57075164111826,-78.2930181078527 -"Sample_4_ATGCGATGTTCTGTTT",80.3715838855086,-41.1177788488411 -"Sample_4_ATGGGAGAGTTGTAGA",-54.8147779358984,-44.6757441240947 -"Sample_4_ATGTGTGAGCATCATC",-36.9237704359469,0.0847815977311433 -"Sample_4_ATGTGTGAGGTCATCT",-101.731609363661,-15.2390572953099 -"Sample_4_ATGTGTGGTGTATGGG",59.4693407863994,-42.9187528480025 -"Sample_4_ATTACTCAGGTCATCT",-87.9432135889831,-51.2726365070512 -"Sample_4_ATTACTCGTGGAAAGA",-9.15572265630424,-39.9366520502819 -"Sample_4_ATTATCCAGTAGCGGT",-99.696847438742,-41.9510042922468 -"Sample_4_ATTATCCCACCCATGG",97.426467979605,-52.6708730043153 -"Sample_4_ATTCTACCAAGCGATG",17.428649821636,2.65140608971734 -"Sample_4_ATTCTACCATTAACCG",56.2553660724565,3.02795494986384 -"Sample_4_ATTCTACGTCTAGTCA",-74.804133380653,-25.6357807381413 -"Sample_4_ATTCTACGTGGTAACG",6.16424129332165,-86.8273195550456 -"Sample_4_ATTGGACCAGCGTTCG",8.99768125577786,-74.6226938713656 -"Sample_4_ATTGGACCAGCTTCGG",23.970045885341,-36.1930261248781 -"Sample_4_ATTGGACGTAGCGTGA",19.0906057149638,121.264656104217 -"Sample_4_ATTGGACGTCGCATCG",-35.515372658268,12.9684144408154 -"Sample_4_ATTGGTGAGCCCGAAA",73.1601008045325,3.75940246711735 -"Sample_4_CAACCAATCCTTGGTC",-76.7349104575044,5.89215131175373 -"Sample_4_CAACTAGCATGGTAGG",-68.3861777189979,-27.3481618955101 -"Sample_4_CAACTAGTCGCATGGC",37.9327354774639,-32.6797460126869 -"Sample_4_CAAGAAAAGACTAAGT",-93.3011841892868,-36.6474697420332 -"Sample_4_CAAGAAAAGTTAGCGG",-45.3768729216324,1.6112912062865 -"Sample_4_CAAGAAATCTATGTGG",-54.8804192015868,-6.01563396977752 -"Sample_4_CAAGATCGTTACTGAC",-2.30296752386008,-75.4783404776886 -"Sample_4_CAAGATCGTTCAACCA",-66.0874586854123,18.2515215461648 -"Sample_4_CAAGATCGTTCAGCGC",84.5382060381682,-11.7942731276223 -"Sample_4_CAAGATCGTTTAGGAA",17.5670892387683,109.51474473963 -"Sample_4_CAAGTTGGTAACGCGA",15.4868721884338,45.6113402794929 -"Sample_4_CACAAACAGTTCGATC",60.2248755455273,-17.707457129107 -"Sample_4_CACACAACAATCACAC",29.2984639002206,110.040807075114 -"Sample_4_CACACAAGTCGCGTGT",46.5437856497997,53.7158365212723 -"Sample_4_CACACCTCAAACCTAC",-99.9514755774925,-51.8517037472984 -"Sample_4_CACACCTTCCCATTTA",-53.5124271002858,10.0811540354834 -"Sample_4_CACACTCCAGTACACT",-94.1022313973625,-29.0686667996971 -"Sample_4_CACACTCCATGTAAGA",50.2272766717843,117.744853414013 -"Sample_4_CACACTCTCTAACCGA",17.7453706012199,-41.715390332252 -"Sample_4_CACAGGCGTGGGTATG",-94.5595062767987,-3.68993328408922 -"Sample_4_CACAGTAAGTGACATA",6.70029570252657,-73.2230425690173 -"Sample_4_CACATAGCAAGCCGCT",37.1912165636835,108.230778112098 -"Sample_4_CACATAGGTAGCAAAT",-84.8837951897494,-49.4613770404083 -"Sample_4_CACATAGTCGCGTTTC",19.3245220433119,58.4962057331713 -"Sample_4_CACATTTCATTGCGGC",35.4197441922176,60.5067627332517 -"Sample_4_CACATTTTCCGTCATC",22.2642169137203,93.5734720808596 -"Sample_4_CACCACTCACTCGACG",-12.369667338486,-23.4566800878123 -"Sample_4_CACCACTTCATACGGT",-78.8619646709137,-16.1371099108946 -"Sample_4_CACCACTTCCGCAAGC",-95.1782225019634,4.69542948430699 -"Sample_4_CACCACTTCTTGAGAC",-71.6632247168429,-18.0432191182253 -"Sample_4_CACCAGGCACCTCGTT",8.43497236085867,-86.9935729085497 -"Sample_4_CACCAGGTCAAGGTAA",-22.3956014436947,-39.6011355582876 -"Sample_4_CACCTTGTCTCTAAGG",48.2929726421173,21.7176111227166 -"Sample_4_CAGAGAGCATCACGTA",42.7985962871173,24.1993097810084 -"Sample_4_CAGATCAAGATATGGT",92.5757659858005,-57.0499058337408 -"Sample_4_CAGCAGCGTCGCCATG",51.7828495689899,104.634886439287 -"Sample_4_CAGCAGCTCGGAATCT",-24.539313598854,-179.737281495289 -"Sample_4_CAGCAGCTCTGTCCGT",72.4974860458622,-7.60329479287682 -"Sample_4_CAGCCGAAGCGACGTA",-81.0208420049616,-14.8065586969624 -"Sample_4_CAGCGACAGTAGCGGT",-60.4717075507877,-20.5978879232507 -"Sample_4_CAGCTGGAGTGTACGG",-95.2354322347689,15.403599665484 -"Sample_4_CAGCTGGTCTTTCCTC",-92.8647807839819,-7.72952670586375 -"Sample_4_CAGTAACCATCTCGCT",2.39003943031582,-26.5688115332919 -"Sample_4_CAGTAACTCTGTCCGT",43.4641677213901,89.9649129432912 -"Sample_4_CAGTCCTAGACAAGCC",-65.2404168874297,-37.7485330787245 -"Sample_4_CAGTCCTAGATCTGCT",21.0940371077757,92.9641508839659 -"Sample_4_CAGTCCTAGTAGATGT",84.5404707754901,-46.1858691407182 -"Sample_4_CAGTCCTCAGGCGATA",109.340994715657,-14.8673976791672 -"Sample_4_CAGTCCTGTTCAGCGC",62.3206917617232,-38.921044294702 -"Sample_4_CAGTCCTTCTCTTGAT",81.1483597736768,-43.1243783175687 -"Sample_4_CATATGGTCTGTGCAA",-27.0528828846945,-106.051566825192 -"Sample_4_CATATTCCAGTGACAG",16.5842367360244,54.6764465438065 -"Sample_4_CATATTCGTAAATACG",72.9412391056046,15.0881971275901 -"Sample_4_CATCAAGAGGTGTTAA",35.5088924388044,15.1677431763321 -"Sample_4_CATCAAGCACGACGAA",105.174627103682,-57.1350214443747 -"Sample_4_CATCAAGCAGGCAGTA",-69.5864540851604,20.8245598855954 -"Sample_4_CATCAAGCATGTTGAC",23.4861520392899,-12.3747180723409 -"Sample_4_CATCAAGGTCTCCACT",-78.1536364939733,35.5370588004431 -"Sample_4_CATCAGAAGAAACCAT",35.4617843687553,23.792205780849 -"Sample_4_CATCAGACACCCTATC",106.807311685973,-23.4273871995841 -"Sample_4_CATCAGAGTCCATGAT",-106.950122107254,-13.9603701659585 -"Sample_4_CATCAGAGTCCGAAGA",0.782407603743992,-90.3500983181181 -"Sample_4_CATCCACGTCCCTTGT",30.1253324094917,18.0338782207667 -"Sample_4_CATCCACGTGCATCTA",38.6847079835765,50.0721509838795 -"Sample_4_CATCCACTCTGAGGGA",-87.8791090229449,13.8396543913479 -"Sample_4_CATCGAAAGGAATCGC",26.3337675176488,57.3681605181633 -"Sample_4_CATCGAACAGGCTGAA",1.66790384915002,102.912026220256 -"Sample_4_CATCGAACAGGGAGAG",100.162244266728,-26.3725826264942 -"Sample_4_CATCGGGCAAAGGTGC",-69.47445400129,-33.1698965487428 -"Sample_4_CATGACAAGTGAACGC",75.949588551288,12.2345263984008 -"Sample_4_CATGACACACAGACTT",-96.5612128999326,-1.7466366045722 -"Sample_4_CATGACACACCGTTGG",61.5892231759958,-51.3348330500117 -"Sample_4_CATGACACAGCTATTG",-85.9401046172157,32.9774739795475 -"Sample_4_CATGACAGTCTCACCT",65.2055248924882,-43.2354924791526 -"Sample_4_CATGCCTCAATGAAAC",-79.0286991808961,11.457468878723 -"Sample_4_CATGGCGAGTGTGGCA",-55.9439234885935,-12.9160898474418 -"Sample_4_CATGGCGCACGAGGTA",-71.7256992041469,-17.0040088912778 -"Sample_4_CATGGCGGTACAGTGG",-94.1589790057701,-23.0379300847905 -"Sample_4_CATTATCAGGGTTCCC",-98.4315407825488,30.2467768954197 -"Sample_4_CATTATCCACAGCCCA",1.58126666243907,74.3187838185491 -"Sample_4_CATTATCGTGGTCTCG",82.1177681851183,5.36254107541365 -"Sample_4_CCAATCCGTCTCTTAT",52.2129740422095,117.13541827315 -"Sample_4_CCAATCCTCTCATTCA",25.8704410459254,106.779179102026 -"Sample_4_CCACCTATCTTGCCGT",-114.695017927592,-48.0274493606093 -"Sample_4_CCACGGACAATCACAC",-80.4499400194488,27.2889212310435 -"Sample_4_CCACGGATCAGGCAAG",-25.8732952235427,-103.611507244529 -"Sample_4_CCACTACAGTGGACGT",50.2411239270253,-22.2467881415559 -"Sample_4_CCAGCGAGTTCGTTGA",70.2725557864486,-5.21047156878531 -"Sample_4_CCATTCGCAAGCCATT",19.8165100683079,81.9416863285025 -"Sample_4_CCCAATCTCAACACCA",91.7152944978147,-10.2177456148341 -"Sample_4_CCCTCCTAGTAGCCGA",-51.3101951135078,4.3456697216688 -"Sample_4_CCCTCCTGTTGTACAC",22.5016157303346,116.126114539557 -"Sample_4_CCGGTAGAGTTACGGG",36.8500507437865,113.487508953671 -"Sample_4_CCGGTAGTCGCCTGAG",-81.4971240552977,-43.1340240624523 -"Sample_4_CCGTACTCAGGGTATG",93.7586895785825,-38.4796573318157 -"Sample_4_CCGTACTTCTACTATC",-49.3542123056086,13.2227275543039 -"Sample_4_CCGTTCAAGCCCGAAA",-108.409864800984,-23.6025450313099 -"Sample_4_CCTAAAGAGCGATTCT",75.1828795427839,-27.9580553539853 -"Sample_4_CCTAAAGCAAGTAATG",91.2466141894488,-14.9422927979171 -"Sample_4_CCTACACTCTTTACGT",-95.1291265745675,36.514136585059 -"Sample_4_CCTACCACATCGGAAG",-4.48479515521927,-54.8037854414647 -"Sample_4_CCTAGCTAGAGGTACC",32.4122385713042,100.11324512072 -"Sample_4_CCTATTATCAAACCAC",76.6592623836458,11.5048495137555 -"Sample_4_CCTCTGAGTAAGAGGA",-10.1482257815013,-66.0030479575842 -"Sample_4_CCTCTGAGTTATTCTC",99.5947551022541,-26.9422395856335 -"Sample_4_CCTCTGATCAGCCTAA",-94.1384497251027,-20.6094615860796 -"Sample_4_CCTTACGAGCACCGCT",-110.028091761082,11.8943407973329 -"Sample_4_CCTTACGGTCGCATCG",-60.4172180494711,-26.6466381542957 -"Sample_4_CCTTCCCCAAACCTAC",52.1271814027126,104.925805473506 -"Sample_4_CCTTCCCTCACTGGGC",5.4364917117344,102.011496997931 -"Sample_4_CCTTCGAAGCGTTTAC",-62.960170717112,15.3301769033488 -"Sample_4_CCTTCGACAAGAAGAG",96.7924147418242,-11.6110027952228 -"Sample_4_CGAACATAGTCAAGCG",-59.4365743707332,-21.729184385092 -"Sample_4_CGAACATCATCAGTCA",66.3435412764413,-65.0191949959725 -"Sample_4_CGAATGTGTCATTAGC",86.5171109157542,29.6384194140378 -"Sample_4_CGACCTTAGCACCGCT",33.3821073405346,19.9266001574313 -"Sample_4_CGACCTTAGTTGTAGA",-26.1442365375205,-183.753641978703 -"Sample_4_CGACCTTTCAAGCCTA",2.48649949525869,-60.535120579673 -"Sample_4_CGACTTCTCATAGCAC",-84.9023837503005,12.8170440398753 -"Sample_4_CGAGCACAGACGCAAC",-110.638198163394,-5.5089085408396 -"Sample_4_CGAGCACAGCTCAACT",105.379104721729,-60.9834448344218 -"Sample_4_CGAGCACAGGCGTACA",-0.329189696194402,112.287352234272 -"Sample_4_CGAGCACTCTGCAAGT",10.764657085047,103.372283870557 -"Sample_4_CGAGCCACATGCAACT",2.41186260197151,94.1217350775579 -"Sample_4_CGAGCCATCGTGGTCG",24.5769963571039,3.13096177949926 -"Sample_4_CGATCGGAGACGCTTT",9.13648629974822,-60.5648302334094 -"Sample_4_CGATCGGGTCCCTTGT",-0.896944394991901,-48.7869270464038 -"Sample_4_CGATGGCTCGGTCCGA",51.9513935671568,114.454621980653 -"Sample_4_CGATGTAAGGACATTA",68.4729691666967,-19.6047784890206 -"Sample_4_CGATTGAGTGGTACAG",-51.9849471458718,-20.4999743559626 -"Sample_4_CGCCAAGAGACCACGA",10.4203697316509,-81.7756583808096 -"Sample_4_CGCCAAGGTGTCCTCT",-100.771754035661,43.9959242046787 -"Sample_4_CGCGGTAAGGCATGGT",-69.4944721185432,14.9976133028747 -"Sample_4_CGCGGTAAGTATCGAA",-113.560149295662,-8.12230008154118 -"Sample_4_CGCGGTACACAGGCCT",-50.6155347233928,-36.1650888464957 -"Sample_4_CGCGGTATCAACGCTA",-60.4624692735797,-14.6132201544746 -"Sample_4_CGCGGTATCTCTGCTG",21.1671062250755,12.0225731644399 -"Sample_4_CGCGTTTTCCAGATCA",25.4090041115124,-33.2578171489106 -"Sample_4_CGCTATCCACTGTCGG",9.29959434847489,101.180167930305 -"Sample_4_CGCTATCGTGCAGACA",-100.006207792598,0.70182930288887 -"Sample_4_CGCTATCTCGCCATAA",-76.4854209713817,-23.303889419553 -"Sample_4_CGCTATCTCTGTACGA",29.4480911016843,13.074809065217 -"Sample_4_CGCTGGATCCTAGGGC",39.4924687405134,64.5931573181506 -"Sample_4_CGCTTCACATTTCAGG",72.8761720848099,-30.1689086343813 -"Sample_4_CGGACACAGCAGCGTA",23.1054727030899,81.6203367338869 -"Sample_4_CGGACACAGGTGCTTT",-75.8247247782798,-34.3999479348831 -"Sample_4_CGGACACTCCTCGCAT",2.49840191975946,-12.1165284233872 -"Sample_4_CGGACGTTCAGGTTCA",82.578364672061,0.475839121799077 -"Sample_4_CGGACTGTCTCTGAGA",41.7192121866244,55.3389272707524 -"Sample_4_CGGAGCTCAAACTGTC",57.4605773080573,-53.8869232535838 -"Sample_4_CGGAGCTGTACCGTTA",79.1605848891303,0.510410536679691 -"Sample_4_CGGAGTCTCAGGCCCA",-82.9644307178853,-39.6833081202914 -"Sample_4_CGTAGCGCAAACAACA",-52.4666256536252,-48.2956302207214 -"Sample_4_CGTAGCGTCAGCACAT",-67.1379452219648,-4.35678252111851 -"Sample_4_CGTAGCGTCCTAGAAC",-24.9120985897645,-107.08097922305 -"Sample_4_CGTAGGCGTCTGATTG",42.602544277163,60.4480420175055 -"Sample_4_CGTCAGGCAAGCTGTT",-88.8731074125714,-38.0315195219348 -"Sample_4_CGTCAGGTCAAGGTAA",13.3468293296923,80.8368610598721 -"Sample_4_CGTCCATAGCTATGCT",-82.9977569032053,-12.0663656835702 -"Sample_4_CGTCCATCACCCTATC",74.2475900185001,-15.8264462480146 -"Sample_4_CGTCTACCATAGGATA",38.5312603057582,107.347892340954 -"Sample_4_CGTGAGCCATTCTTAC",87.2984876443999,20.0136133214619 -"Sample_4_CGTGAGCTCAATCTCT",-26.3775113590511,-179.360814325809 -"Sample_4_CGTTGGGTCAGAGACG",72.9188589434305,0.0665297951567099 -"Sample_4_CGTTGGGTCCTTGCCA",20.812175491605,37.8696499006858 -"Sample_4_CTAACTTAGCCCAGCT",88.3900975860475,-28.3775588017693 -"Sample_4_CTAAGACAGTGTCTCA",-109.972878112718,-36.4779436012174 -"Sample_4_CTAAGACCAGATCGGA",88.1775453783936,21.2320221608654 -"Sample_4_CTAAGACGTCGGATCC",-23.0968783014981,-107.412631459294 -"Sample_4_CTAAGACTCCACTGGG",-17.5277902675666,-40.4091591105888 -"Sample_4_CTAATGGCACAGACTT",30.6038168350885,96.5257868101868 -"Sample_4_CTAATGGGTTCAGGCC",-57.7865001082396,-23.4770752303204 -"Sample_4_CTACACCCAACGCACC",-105.276191077348,19.5682087857376 -"Sample_4_CTACACCGTTCAGCGC",-56.5433568287689,14.7328059138005 -"Sample_4_CTACACCTCGGTTCGG",67.4786299087248,-35.1234987769637 -"Sample_4_CTACATTCATGCCCGA",-92.2338585353802,16.6764801845549 -"Sample_4_CTACATTGTTTGGCGC",-64.8418841528597,24.5816180882639 -"Sample_4_CTACCCAAGGTGCACA",-31.0248405402116,-97.7999071142106 -"Sample_4_CTACCCACAAGGTGTG",28.1928458250287,100.125146980729 -"Sample_4_CTACCCACACGACTCG",-54.7730980617966,-19.0634949305797 -"Sample_4_CTACCCACAGCGATCC",-41.0712619238135,-11.3318150356304 -"Sample_4_CTACGTCAGAGCTGCA",33.8650837687056,91.4899185423021 -"Sample_4_CTACGTCAGGCGATAC",99.816044920251,-28.331377407254 -"Sample_4_CTACGTCGTCTCAACA",-82.0730358101367,-6.73003665571968 -"Sample_4_CTACGTCTCGTCGTTC",-89.9343811935625,-18.307871782992 -"Sample_4_CTAGAGTCAATCTACG",-100.236350107285,-5.63261907839369 -"Sample_4_CTAGAGTCAGGAATCG",2.00041553837751,-56.6190009466202 -"Sample_4_CTAGAGTTCAACTCTT",80.027219692709,-23.7670924733934 -"Sample_4_CTAGTGACAGACGCTC",24.4138040909109,60.9768661421166 -"Sample_4_CTAGTGACAGCGTAAG",37.3887111330326,62.6619315221081 -"Sample_4_CTAGTGACATCTATGG",32.2545245871321,47.6373741800287 -"Sample_4_CTAGTGATCCTTGACC",50.7083443592455,104.4800843127 -"Sample_4_CTCACACTCACATACG",67.5270753861458,-24.8635431411027 -"Sample_4_CTCAGAATCGGAAATA",11.3277646844147,-61.9900369807661 -"Sample_4_CTCATTAAGTCGTTTG",40.9897487104279,67.3095610461126 -"Sample_4_CTCCTAGTCGGCGCTA",13.2504622253462,-73.0957228234279 -"Sample_4_CTCGAAAAGAACAATC",39.9268001867403,10.7725648624169 -"Sample_4_CTCGAAAAGACGACGT",4.16413615051651,-9.5696749321016 -"Sample_4_CTCGAAACACAGCGTC",-27.9700412826538,-9.91292147461424 -"Sample_4_CTCGGGAGTTCGGCAC",47.9458930977508,50.8023746137523 -"Sample_4_CTCGTCACACACTGCG",-111.74423866245,-33.9564386021962 -"Sample_4_CTCGTCACACAGGAGT",-101.232079847818,-30.7994219357804 -"Sample_4_CTCGTCAGTTCCACGG",87.1128169282987,-57.8232531299042 -"Sample_4_CTCTAATAGTCCGTAT",-104.799011593537,-44.2787136647692 -"Sample_4_CTCTAATAGTGGGTTG",82.5103758093176,-46.5079929483426 -"Sample_4_CTCTACGAGGTGCACA",77.5757864768066,-46.6959943049022 -"Sample_4_CTCTACGTCGAACGGA",107.631943519362,-51.4579318208942 -"Sample_4_CTCTGGTAGCGATATA",77.236881683728,-29.7758344467344 -"Sample_4_CTCTGGTAGGGAACGG",36.4972303979979,83.6948152925191 -"Sample_4_CTCTGGTAGGTCGGAT",96.0744519101193,-22.8397854891075 -"Sample_4_CTCTGGTCACTGCCAG",63.4263936468339,-62.8076813310375 -"Sample_4_CTGAAACGTGCCTGTG",-56.3927411021898,-35.4808882423988 -"Sample_4_CTGAAGTCACGAAGCA",20.9693899255525,105.01035948971 -"Sample_4_CTGAAGTTCCGCAAGC",-100.244657420647,10.4690498698792 -"Sample_4_CTGAAGTTCGCAAACT",13.75127275449,117.637618961357 -"Sample_4_CTGATAGAGCGTCAAG",56.0063537345424,107.24372188717 -"Sample_4_CTGATCCTCATAACCG",104.349484837061,-44.8585839534658 -"Sample_4_CTGATCCTCGCAAGCC",17.2726584977442,89.063283483585 -"Sample_4_CTGATCCTCTACTATC",30.4688169829171,57.4825869415713 -"Sample_4_CTGCCTAAGAAGGTGA",-71.9257163165462,-30.5779376287316 -"Sample_4_CTGCCTAAGACAGAGA",20.5426261446742,-11.4829645150672 -"Sample_4_CTGCCTACAGACAGGT",-77.316413825171,30.2336942103939 -"Sample_4_CTGCTGTCAACGATCT",-64.3095894170871,-49.6935797279121 -"Sample_4_CTGGTCTCAACCGCCA",90.9683757435854,-57.9076060102667 -"Sample_4_CTGGTCTGTATCAGTC",-97.3260077133555,-36.7131551323816 -"Sample_4_CTGGTCTGTTGACGTT",87.1905309050108,16.4086164163678 -"Sample_4_CTGGTCTGTTGTCGCG",36.9301707246077,112.423326795699 -"Sample_4_CTGTGCTCACATGGGA",45.7459176030555,111.797605131318 -"Sample_4_CTGTGCTCACGGCCAT",74.1718326431949,-24.3871263760249 -"Sample_4_CTGTGCTTCAGCGATT",73.9811290487947,-8.32773195011256 -"Sample_4_CTTAACTGTGGACGAT",31.9893799752456,91.9457804720012 -"Sample_4_CTTACCGAGCGTCAAG",82.2433696371614,5.23070284678413 -"Sample_4_CTTAGGAAGCCAACAG",30.3322753441762,101.586418212954 -"Sample_4_CTTCTCTAGTATCGAA",83.4629655780302,-37.0294394824251 -"Sample_4_CTTCTCTAGTGCGATG",-112.829556913868,-2.73183504113803 -"Sample_4_CTTTGCGTCGGGAGTA",88.406458876779,2.69353287753491 -"Sample_4_GAAACTCAGTACGTTC",2.69680383189324,2.29842366689397 -"Sample_4_GAAACTCGTCTGCCAG",19.4383288818237,-40.0864042194207 -"Sample_4_GAAATGAAGAAGGGTA",59.4840592364311,-18.7185622209399 -"Sample_4_GAAATGAGTGTTAAGA",71.6238067921521,-12.8487759742433 -"Sample_4_GAACATCTCCTTGGTC",46.3013717595502,72.9808909642166 -"Sample_4_GAACCTACAACACCCG",19.8710886342687,108.790277384779 -"Sample_4_GAACGGAAGCAAATCA",97.8693805169897,-18.3400641116888 -"Sample_4_GAAGCAGGTTCAGGCC",-72.1127372339752,1.54877167165268 -"Sample_4_GAAGCAGTCTGTGCAA",-37.90450239322,-14.7622739244874 -"Sample_4_GAATAAGAGCGATTCT",73.2253539140735,-17.5274040472503 -"Sample_4_GAATAAGAGTTGAGAT",-79.0405759120296,-33.4136764487422 -"Sample_4_GAATAAGGTACCGTTA",42.9332977198643,108.533913755413 -"Sample_4_GAATAAGTCCACTCCA",-104.618832837653,-36.5524564084633 -"Sample_4_GACACGCCACGACGAA",79.9671749139355,-13.534075620429 -"Sample_4_GACACGCGTAACGTTC",17.5029605362541,6.00772017413147 -"Sample_4_GACAGAGAGGCGTACA",-17.5078021734619,-36.8505644678296 -"Sample_4_GACAGAGTCGAGAACG",0.623441878611637,-83.505084675523 -"Sample_4_GACCAATGTGAGCGAT",7.47970469987878,-17.0657971825051 -"Sample_4_GACCTGGCACTTCGAA",-90.3213813427955,9.54277155181087 -"Sample_4_GACCTGGCAGTAAGAT",-7.99024177211658,-82.0447973975149 -"Sample_4_GACGGCTGTTGATTGC",-77.9221706524135,-45.6839053199761 -"Sample_4_GACGGCTGTTTGGGCC",75.2196944967379,3.41975173186423 -"Sample_4_GACGGCTTCAACGCTA",80.6482141933191,-22.0343738720793 -"Sample_4_GACGTGCTCACCATAG",1.44808803548457,-81.3741761580601 -"Sample_4_GACGTTAAGAATAGGG",-97.7429386120551,-20.6942816966775 -"Sample_4_GACGTTAAGTGTACGG",41.2182165871494,114.750603590926 -"Sample_4_GACTACAAGTACACCT",89.392830113997,-3.98024868757286 -"Sample_4_GACTACATCGGAATCT",-114.69715097487,7.59177996707593 -"Sample_4_GACTGCGGTTCGGCAC",100.33564019976,-45.2462107244667 -"Sample_4_GACTGCGTCAGTTGAC",30.0368192602754,90.4340247678151 -"Sample_4_GACTGCGTCTGTACGA",7.5254242989165,90.7466134144719 -"Sample_4_GAGCAGAAGTGCTGCC",19.0017827739641,78.5686466002048 -"Sample_4_GAGCAGAGTCTTGATG",-52.2283320517005,-31.0083159028058 -"Sample_4_GAGGTGAGTCCTAGCG",-0.303013851365884,74.9881443364821 -"Sample_4_GAGGTGATCCCTAACC",-28.1481349657643,-17.9116254796152 -"Sample_4_GATCAGTAGAGTTGGC",47.6814403272061,112.436300315244 -"Sample_4_GATCAGTAGTAGCGGT",17.6432292648026,106.364012872261 -"Sample_4_GATCAGTCAAAGCGGT",38.3340294867059,100.305121301237 -"Sample_4_GATCAGTTCCGAGCCA",-87.6682023307386,-22.5900323311825 -"Sample_4_GATCAGTTCCTCGCAT",-65.1623922243063,-22.1565638164741 -"Sample_4_GATCGATGTTCCCGAG",32.5397887423208,96.5482128832351 -"Sample_4_GATCGATTCCACGCAG",-67.464594851024,-21.6973526193816 -"Sample_4_GATCGCGAGACCTTTG",72.1569551707529,-56.6396751669807 -"Sample_4_GATCGCGCAAGAAAGG",-28.6196708228422,-99.6616763574074 -"Sample_4_GATCGCGGTCAACTGT",-55.1151061627121,25.0495332116948 -"Sample_4_GATCGCGTCAAGGTAA",-29.9103356704776,3.42665443187294 -"Sample_4_GATCGTAAGGTACTCT",35.5117022046867,103.568349502896 -"Sample_4_GATGAAAAGACACTAA",29.8955518060444,121.626818286757 -"Sample_4_GATGAAATCGTCTGAA",-98.8611318487807,-30.3618744797661 -"Sample_4_GATGAGGTCCTAGGGC",-58.6088231120222,-7.44429234468428 -"Sample_4_GATGCTAAGTCCCACG",19.1625523072656,96.6863934617412 -"Sample_4_GATGCTACAAAGGAAG",66.52680824565,-50.0384707544202 -"Sample_4_GATGCTAGTAGAGGAA",103.737992564543,-20.8370330586319 -"Sample_4_GATTCAGAGACAGGCT",18.6615568338371,-15.3342406229173 -"Sample_4_GCAAACTCATCCCATC",0.537659267109488,-44.7313108183383 -"Sample_4_GCAAACTGTGGACGAT",-55.5952411324017,-10.3993895900424 -"Sample_4_GCAATCAAGTAGGCCA",-80.4500646126745,39.3037235988351 -"Sample_4_GCAATCAGTCTAGTCA",-65.1630757875361,11.7655134742458 -"Sample_4_GCAATCATCACCCTCA",-102.04972922595,-11.0701942055772 -"Sample_4_GCAGTTACAACGATGG",-116.072141213988,-40.8359654497241 -"Sample_4_GCAGTTAGTTCAGCGC",47.5683710228571,110.559683415191 -"Sample_4_GCATACATCCTGCCAT",76.8773853089025,-65.1210939481813 -"Sample_4_GCATACATCTTTAGGG",-100.890235932572,29.6107685560425 -"Sample_4_GCATGCGGTCCCTTGT",44.9881189575588,14.6354616669293 -"Sample_4_GCATGTAAGACGCACA",79.6791239462835,-10.2092946714618 -"Sample_4_GCATGTAAGAGTAATC",17.0206945388936,-69.936134930272 -"Sample_4_GCATGTACACCTCGTT",-11.8259454439692,-79.6377597525781 -"Sample_4_GCCAAATAGTGGAGAA",96.8965698229209,-62.0721363534149 -"Sample_4_GCCTCTACAAACTGCT",27.57537145728,-27.6650111110192 -"Sample_4_GCGACCACATGCCCGA",77.8595441535741,-61.0411186313013 -"Sample_4_GCGAGAAAGCTACCGC",41.160645836337,108.493893862756 -"Sample_4_GCGAGAACAAGGCTCC",84.1239816648041,-10.1038921186194 -"Sample_4_GCGAGAACACCAGTTA",-117.420358142328,-16.8447233713031 -"Sample_4_GCGCAACAGGGTGTGT",17.4663400262197,31.5963350215045 -"Sample_4_GCGCAACCACCCAGTG",-79.1869050374881,-18.2677192802594 -"Sample_4_GCGCAACTCCTATGTT",92.1352467493162,29.3796826365869 -"Sample_4_GCGCAACTCTTGCATT",-65.8930557465747,-44.1084599267305 -"Sample_4_GCGCAGTTCCGTCATC",-10.9527634940404,-71.2140781781615 -"Sample_4_GCGCCAACACCGTTGG",-2.18608328062923,75.5964803932519 -"Sample_4_GCGCGATTCCGTAGTA",-94.823919293176,17.9204104290703 -"Sample_4_GCGGGTTGTGCGCTTG",19.8260037963784,31.9380485627844 -"Sample_4_GCTCCTAGTAGAAGGA",-2.89762104843517,-68.4177164047338 -"Sample_4_GCTCTGTCAAGTCTAC",-93.7624491853174,-40.4903750347654 -"Sample_4_GCTGCAGAGTGGGATC",91.2551300457035,-7.18480197833547 -"Sample_4_GCTGCAGCACCACCAG",-70.5388955355956,-25.4438803415337 -"Sample_4_GCTGCAGGTCGATTGT",-96.1412270085463,-10.3830268146001 -"Sample_4_GCTGCAGTCCTTTACA",36.813043549988,-32.5121886138246 -"Sample_4_GCTGCAGTCGGAGGTA",-56.7305993886279,22.3525216447621 -"Sample_4_GCTGCGAAGGCTACGA",-9.48962931637472,-74.7208737057384 -"Sample_4_GCTGCGAGTGTCAATC",49.2106372751729,101.169808096073 -"Sample_4_GCTGCGATCTGATACG",-100.529594148171,-14.2443274619347 -"Sample_4_GCTGGGTCATATGCTG",-40.6459881317122,-12.1467859573799 -"Sample_4_GCTTCCACACATCCGG",86.6848302415298,-64.0024380275402 -"Sample_4_GCTTCCACATCTATGG",-50.4733601159244,0.938865547450784 -"Sample_4_GCTTCCATCTCTGTCG",30.1108988081501,6.64216146828579 -"Sample_4_GCTTGAACAGTATAAG",100.215595591486,-31.8683869066382 -"Sample_4_GCTTGAATCTAACCGA",-84.5452979754074,-32.2166065502577 -"Sample_4_GGAAAGCTCCTTGCCA",-72.9314113630811,-10.0023029947818 -"Sample_4_GGAACTTCAGATGGCA",73.3537110198919,-52.4172399724136 -"Sample_4_GGAATAACAAATTGCC",-60.1867911353679,21.548413372388 -"Sample_4_GGAATAACAGTTCATG",18.6523411299579,91.1364371939507 -"Sample_4_GGACATTCACTTCGAA",-91.0749174398248,23.4393152761187 -"Sample_4_GGAGCAACAAGCGCTC",100.507361231319,-15.5236306373938 -"Sample_4_GGAGCAACATCTCCCA",39.566641480107,15.6253881002093 -"Sample_4_GGAGCAATCGCATGGC",-34.9885833989584,20.7454597423101 -"Sample_4_GGAGCAATCGTAGATC",-34.9497355848186,-6.41486295657001 -"Sample_4_GGATGTTAGTCCGTAT",14.1205066648571,107.457394894513 -"Sample_4_GGATTACCATTGAGCT",-73.502483959759,-40.2033557861993 -"Sample_4_GGATTACTCACCAGGC",31.6811231002447,16.918870635989 -"Sample_4_GGCAATTAGAGGTAGA",73.6701444252222,-38.0431333223359 -"Sample_4_GGCAATTAGTGTTGAA",11.8414129182605,-64.9220311222995 -"Sample_4_GGCAATTCAGATTGCT",14.1329562962917,5.0270666786371 -"Sample_4_GGCAATTGTCAAACTC",-82.4766116624579,-16.8307291191623 -"Sample_4_GGCAATTGTGATAAGT",83.7826027444221,-63.4595766377151 -"Sample_4_GGCCGATGTTTCCACC",-74.9113567478452,2.18993835598818 -"Sample_4_GGCGACTCATCCTAGA",-112.307311649008,-14.7714620067654 -"Sample_4_GGCGACTCATCCTTGC",31.506900313626,53.5883666971801 -"Sample_4_GGCGTGTCACATGACT",-79.9202444514253,-10.1633530359592 -"Sample_4_GGGAATGAGATGGCGT",35.5538116702834,123.251826294816 -"Sample_4_GGGAATGGTCATCCCT",37.5135921013217,97.253447947209 -"Sample_4_GGGACCTTCTTGGGTA",-10.0496555669333,-72.5744946289469 -"Sample_4_GGGAGATAGTGTTTGC",38.2044584983516,103.308355626411 -"Sample_4_GGGAGATCAGATGGCA",27.0931071153265,7.43500020809954 -"Sample_4_GGGAGATCATACCATG",20.0764141483215,101.715412675553 -"Sample_4_GGGAGATGTCAAACTC",-69.4081951625553,38.384080551979 -"Sample_4_GGGATGAGTCGTCTTC",93.304085797364,-23.7438131491383 -"Sample_4_GGGATGATCAGTTCGA",79.008796543445,-6.03628544952304 -"Sample_4_GGGCACTAGCAGGTCA",-68.5037531524766,16.0603012370224 -"Sample_4_GGGCACTCAAAGCAAT",7.71266268100619,-5.41925057290535 -"Sample_4_GGGCACTTCCTAGGGC",30.6509189803008,108.226539109005 -"Sample_4_GGGCACTTCTATCGCC",-84.4798748678771,-22.8529341885639 -"Sample_4_GGGCATCCACGGCGTT",-116.597135482197,-23.566194805194 -"Sample_4_GGGCATCCATCACAAC",8.26591380262678,8.91570965395614 -"Sample_4_GGGCATCGTTCCTCCA",90.812707003662,-44.8609871085258 -"Sample_4_GGGCATCTCCGCATCT",83.0772071294795,-15.7144920468336 -"Sample_4_GGTATTGGTGAGGGTT",25.2010449155464,56.9946396447041 -"Sample_4_GGTATTGGTTCCCGAG",19.6999231831764,-1.23023382543799 -"Sample_4_GGTGAAGAGAGGTAGA",7.38288729852858,-37.769005666921 -"Sample_4_GGTGAAGTCCATGAGT",65.7237251659603,-3.84942384263008 -"Sample_4_GGTGAAGTCCTCATTA",-63.4015552267603,-9.70231722374442 -"Sample_4_GGTGCGTAGAGTACAT",-118.766561755699,-1.51109624445162 -"Sample_4_GGTGCGTAGTACATGA",41.6304962161829,26.0417127420827 -"Sample_4_GGTGTTAGTAGTAGTA",1.59643519324956,-72.3954963898037 -"Sample_4_GTAACGTAGGACAGCT",80.093112941697,-19.1120140403912 -"Sample_4_GTAACGTTCACTTACT",60.1247615090459,-20.8499828732922 -"Sample_4_GTAACTGTCCGAACGC",-32.0905528562925,14.3051716935849 -"Sample_4_GTACGTAAGACACTAA",-22.3963654596064,15.3301121945927 -"Sample_4_GTACGTACATGCTGGC",77.3774183026562,-27.66910870461 -"Sample_4_GTACGTATCCTGTAGA",7.41767183688433,85.0542282455491 -"Sample_4_GTACTTTGTGTCAATC",46.137635051719,56.2467008702954 -"Sample_4_GTAGGCCAGCGCTCCA",98.1553220927596,13.433976011316 -"Sample_4_GTAGGCCGTAACGTTC",-93.2873937499409,-33.3750910806008 -"Sample_4_GTATCTTTCCTATGTT",87.7622944161301,-20.8096975389899 -"Sample_4_GTATTCTCAGCTGCTG",59.8520192886307,-9.97850862717477 -"Sample_4_GTATTCTGTCCCTACT",23.9249965511669,-17.9261495197747 -"Sample_4_GTATTCTTCCAATGGT",85.8567792945139,-28.6385564513874 -"Sample_4_GTCACAAGTCCGAAGA",33.0430675196908,105.178393912472 -"Sample_4_GTCACAAGTTCCACAA",-90.9288552924833,-24.4422100611478 -"Sample_4_GTCACGGCACCGGAAA",21.4887800137722,104.847095038687 -"Sample_4_GTCACGGGTTGCCTCT",93.2378307583077,-53.2296157820181 -"Sample_4_GTCACGGTCAAGGCTT",12.7910355710307,-66.9754296882614 -"Sample_4_GTCATTTAGCTTTGGT",-101.866550561288,-26.4704919598299 -"Sample_4_GTCATTTTCAGTTTGG",86.7295439135202,-23.3904642565606 -"Sample_4_GTCCTCAAGTATCGAA",33.6814556383532,114.166748835428 -"Sample_4_GTCCTCAAGTGCCATT",103.864614327642,-39.3383714693959 -"Sample_4_GTCGGGTGTCCTCTTG",89.1513853034542,-0.90148980094309 -"Sample_4_GTCGTAATCCATGAGT",22.3966626327563,-6.34832893702685 -"Sample_4_GTCTCGTAGTTCGCAT",15.3841429807784,102.210830604689 -"Sample_4_GTCTTCGGTATAAACG",60.8401440880469,-54.895016504819 -"Sample_4_GTGAAGGAGTACGACG",-55.0791345278209,-2.52846825794416 -"Sample_4_GTGAAGGCAATAACGA",-94.6707237700326,-15.5128742702767 -"Sample_4_GTGAAGGCATGTTGAC",87.0390338985534,-7.95644213370176 -"Sample_4_GTGAAGGGTCCTCTTG",26.3539300279667,12.1592411716 -"Sample_4_GTGAAGGTCATGTAGC",-16.2647476340869,-36.817518453683 -"Sample_4_GTGCAGCAGGCTACGA",85.7238956595751,-62.3202859209538 -"Sample_4_GTGCAGCTCAAAGACA",16.6728976870135,96.6944925129097 -"Sample_4_GTGCATACATCCGGGT",-102.573074732313,26.8212196718874 -"Sample_4_GTGCATATCACCGTAA",-49.3833688499415,-25.3165841886063 -"Sample_4_GTGCATATCATTATCC",-104.995485530403,15.0166178081522 -"Sample_4_GTGCGGTAGAGGTACC",84.4556711774737,0.913665811145362 -"Sample_4_GTGCGGTCAAGCCGTC",-89.8150827327295,-30.7779715516705 -"Sample_4_GTGCGGTCACATTCGA",15.0368779807331,108.881525047387 -"Sample_4_GTGCTTCAGCCAACAG",-67.3719727253161,-12.5077369925473 -"Sample_4_GTGGGTCTCAAGAAGT",-15.4051105086227,-22.2598633963933 -"Sample_4_GTGTTAGCATCCTAGA",-89.4434748639362,21.953884262339 -"Sample_4_GTGTTAGGTAGATTAG",91.5680743362369,-18.7315447220913 -"Sample_4_GTTACAGCATTAGGCT",22.6439690087066,97.4754868124176 -"Sample_4_GTTCATTCATGCATGT",-94.2146192833824,37.7204081261428 -"Sample_4_GTTCATTTCCAAACAC",28.7414749029923,98.3990189758277 -"Sample_4_GTTCTCGTCTAGCACA",-79.7380600442946,-2.46707044899729 -"Sample_4_GTTTCTAAGATTACCC",16.9762169228541,13.8283657111771 -"Sample_4_GTTTCTAAGGTGCAAC",30.768222153744,64.1181094278875 -"Sample_4_GTTTCTACATCACGAT",32.7002293543948,111.084471231661 -"Sample_4_TAAACCGAGTTCGATC",-32.5730610896022,-16.8351068227229 -"Sample_4_TAAACCGGTCTGCGGT",68.6963953508387,-16.0024796344667 -"Sample_4_TAAACCGGTTCGCGAC",48.5802920767186,-28.9188525101698 -"Sample_4_TAAGAGAGTACTTAGC",-47.1290155005842,-34.9968847121376 -"Sample_4_TAAGAGATCCGAAGAG",44.7971781955473,108.107823324821 -"Sample_4_TAAGCGTAGGACTGGT",-62.6332642280526,-32.0105777685312 -"Sample_4_TAAGTGCCAGACAAAT",-95.1964947559643,40.726247498894 -"Sample_4_TAAGTGCTCGCCATAA",-102.944379270862,8.58478896477557 -"Sample_4_TACACGAGTCACTTCC",-69.5984495493061,2.29911367221705 -"Sample_4_TACACGAGTTTCGCTC",0.0656142775436462,79.2206035449322 -"Sample_4_TACACGATCCCAGGTG",1.5457551402509,-41.0496236696627 -"Sample_4_TACCTATGTCCAACTA",84.3221221985278,17.2975370173077 -"Sample_4_TACCTTAAGCTAACAA",-48.8035395455302,15.5060769331773 -"Sample_4_TACCTTATCTGGCGTG",-51.7576596432711,-52.6294988846813 -"Sample_4_TACGGGCCATCCTAGA",35.4418784571174,62.7841569279434 -"Sample_4_TACGGGCCATTACCTT",1.49694787208666,-6.54185580107383 -"Sample_4_TACGGTAAGGATGGAA",101.496125635941,-0.657647664436965 -"Sample_4_TACTCATTCGGCCGAT",-1.71874595643531,-32.9088683030179 -"Sample_4_TACTCGCCAACTGCGC",-77.4726707223448,-36.0602297476113 -"Sample_4_TACTCGCTCCGAATGT",35.2862719848752,109.345453381776 -"Sample_4_TACTCGCTCGTAGGTT",35.1265979164345,102.450500710005 -"Sample_4_TACTTACTCCGTTGTC",-91.0559777580294,-50.3122156803179 -"Sample_4_TACTTGTGTACTTAGC",-99.0953061244793,-19.2229700052933 -"Sample_4_TAGCCGGCAATGTTGC",82.4222223535624,-15.3931371669053 -"Sample_4_TAGCCGGTCGAGAACG",26.7090917756788,-14.512606437426 -"Sample_4_TAGGCATCAGTCCTTC",30.3454429355596,23.8902994755515 -"Sample_4_TAGGCATGTTGTACAC",50.2530695118451,122.154823843577 -"Sample_4_TAGTGGTCAAGGGTCA",-85.6443203006855,0.493971659155987 -"Sample_4_TAGTGGTTCAATACCG",5.96565684935076,107.474891746135 -"Sample_4_TAGTGGTTCCTAGTGA",90.6739052151824,-43.2775986975989 -"Sample_4_TAGTTGGAGTAGGCCA",-83.3375801142509,-1.98649202155298 -"Sample_4_TATCAGGCAACTGCTA",-59.1992378095617,-14.0735034688868 -"Sample_4_TATCAGGGTCGGATCC",60.9636569760806,-12.1851216623348 -"Sample_4_TATCTCAAGACTTGAA",-88.7542946721151,17.3320454829655 -"Sample_4_TATCTCAAGGGAGTAA",-98.2821060412974,-31.1973046226951 -"Sample_4_TATCTCAAGGTCATCT",20.6140813220694,7.21027229930847 -"Sample_4_TATCTCAGTCTACCTC",19.0749072044382,1.87070310889339 -"Sample_4_TATCTCATCATGTAGC",86.992013119019,2.39293497784898 -"Sample_4_TATGCCCCAAGCTGTT",72.2085609825351,-21.3296233431636 -"Sample_4_TATGCCCCAATCACAC",34.4685786305268,120.225610859017 -"Sample_4_TATTACCCAAACCCAT",20.8027481741118,79.6361937060909 -"Sample_4_TATTACCCACGAAAGC",12.5900268080093,86.3343355435021 -"Sample_4_TATTACCGTGATGTGG",-85.9173082601174,-54.1714687076204 -"Sample_4_TCAACGACAATCTACG",-85.7583636193863,-17.9330571713293 -"Sample_4_TCAACGATCCTTTCGG",3.97056586898305,-46.9512495141023 -"Sample_4_TCAATCTGTGCAGACA",2.38855608533341,-68.1131072791778 -"Sample_4_TCAATCTGTGCGATAG",98.2719688676193,-9.79049143207125 -"Sample_4_TCACAAGCAATCGAAA",-80.7850075423666,22.9926571293232 -"Sample_4_TCACAAGGTCTCATCC",-41.713662982561,-28.2647725966089 -"Sample_4_TCACGAACATACTACG",-1.94411507173576,-82.2488541701953 -"Sample_4_TCACGAAGTAGAAAGG",-1.85858703122996,-64.5981965955079 -"Sample_4_TCACGAATCATCGATG",88.6012323979328,-48.4096528851145 -"Sample_4_TCACGAATCGTCACGG",80.9044517399537,-31.4839290345034 -"Sample_4_TCAGATGAGTTGAGTA",30.7250277758249,60.4515832613439 -"Sample_4_TCAGCAAGTGCTTCTC",99.1234149717922,-40.7752240644363 -"Sample_4_TCAGCTCAGACAGGCT",28.0965472962611,-8.21071304212017 -"Sample_4_TCAGCTCGTCGAGTTT",85.2224030637638,7.24270576980732 -"Sample_4_TCAGGATTCATTTGGG",88.8982159548734,-26.3673292651202 -"Sample_4_TCAGGTAAGTCCGGTC",5.63497161142134,-7.19169321220239 -"Sample_4_TCAGGTACAGGATTGG",8.87481666762923,-69.9789387325921 -"Sample_4_TCAGGTAGTCCGTCAG",-59.4334750086086,-24.4662142857426 -"Sample_4_TCAGGTATCGAGGTAG",-87.199131814674,27.5468592568034 -"Sample_4_TCAGGTATCGCCTGTT",43.4704761848821,21.6578507076575 -"Sample_4_TCATTACCAGTATAAG",-78.3281182613093,34.3998464895528 -"Sample_4_TCATTTGAGGCAATTA",90.3835757716527,-8.40510817557984 -"Sample_4_TCATTTGAGTGGTAGC",7.41169298392844,-77.2576669978819 -"Sample_4_TCATTTGGTGTTGAGG",-5.9667787035273,-68.320451117873 -"Sample_4_TCCACACAGTACACCT",16.7602093287358,98.4976157254604 -"Sample_4_TCCACACCAGTTTACG",-104.353913485603,-2.44890239829069 -"Sample_4_TCCCGATAGACAGGCT",31.969739121731,87.6646187295758 -"Sample_4_TCCCGATCAAGTTCTG",-79.0399048678493,-21.6779263457081 -"Sample_4_TCCCGATTCGTAGATC",43.2801829967026,53.2716147607342 -"Sample_4_TCGAGGCGTAGAAGGA",15.6518624161155,41.670683074143 -"Sample_4_TCGAGGCTCCTCAACC",68.0463262322101,-44.3390479889014 -"Sample_4_TCGAGGCTCGTTGACA",-97.9800441412954,18.2106466865464 -"Sample_4_TCGCGAGGTCAGTGGA",22.8860883424994,-11.1090264953923 -"Sample_4_TCGGGACCAGACGTAG",99.3062472265174,3.26785059416844 -"Sample_4_TCGGGACGTGATAAGT",-98.2204687145224,-23.3965598106911 -"Sample_4_TCGGGACTCACCCGAG",34.0633122642724,96.1153159610855 -"Sample_4_TCGGGACTCCGAAGAG",-92.1227036740743,-11.465333005179 -"Sample_4_TCGGTAACACAGGCCT",65.474750047088,-18.4179534373541 -"Sample_4_TCGGTAACATTAACCG",-51.6695738160875,-8.32050904143521 -"Sample_4_TCGTACCAGATCTGCT",83.2080317797839,-29.6140763671699 -"Sample_4_TCGTACCGTTTGACAC",-83.4476158882665,-18.0808096706522 -"Sample_4_TCGTACCTCAAACCGT",-56.6259361203943,-42.7998038956099 -"Sample_4_TCGTACCTCCCGACTT",79.0167611223322,-46.6152798432805 -"Sample_4_TCGTAGACATCTATGG",-106.328009437783,13.9719008738955 -"Sample_4_TCTATTGAGGCGACAT",95.5568119477972,-0.194309229790789 -"Sample_4_TCTCATAAGACTTTCG",-4.35165739624993,-63.5072994593597 -"Sample_4_TCTCTAATCCGTAGTA",19.585121897955,-4.61461293337715 -"Sample_4_TCTGAGAAGAGTGAGA",-109.189982006542,-39.7828200599016 -"Sample_4_TCTGAGACACCACGTG",-3.13361846974438,-71.1553734310115 -"Sample_4_TCTGAGACAGATCTGT",61.0942320314937,-24.570147259202 -"Sample_4_TCTGGAAAGTACGTTC",92.9507165858099,22.5686934553251 -"Sample_4_TCTTCGGAGGGATGGG",89.7878855130556,4.0373888895106 -"Sample_4_TCTTTCCAGACTGGGT",-90.6084705137242,34.2577511405426 -"Sample_4_TCTTTCCGTGCTTCTC",86.7833100227857,-43.2357423972224 -"Sample_4_TGAAAGATCCCTAATT",-107.192754580418,-53.4692164652518 -"Sample_4_TGAAAGATCGTTGCCT",9.06555795305941,-76.769228741344 -"Sample_4_TGACAACTCCTACAGA",-76.9394677619423,42.3820424670084 -"Sample_4_TGACTTTCAATGGATA",-10.5677157413613,-23.2753783240248 -"Sample_4_TGACTTTTCTAGCACA",93.1814727654072,-48.6892338278855 -"Sample_4_TGAGAGGAGCAATCTC",-71.9502183552531,24.9231854624221 -"Sample_4_TGAGCATGTCAAAGCG",14.264382783678,12.7608651911785 -"Sample_4_TGAGCCGGTGCCTTGG",-107.912721616906,-46.5234858454862 -"Sample_4_TGAGGGAAGCATGGCA",1.85366770598557,97.1576222663702 -"Sample_4_TGAGGGACATGGGAAC",18.067720764812,4.73378159849093 -"Sample_4_TGAGGGATCACATGCA",76.6735963183674,-40.2812695302423 -"Sample_4_TGATTTCTCCTTCAAT",-75.4373151762263,-21.2391530542476 -"Sample_4_TGCACCTAGGGCACTA",-115.24169440975,16.518676332814 -"Sample_4_TGCCCATCAGTAAGCG",71.0446486117182,-26.9245682783232 -"Sample_4_TGCCCTAGTTCAACCA",-83.7197434068375,-28.252337147429 -"Sample_4_TGCCCTATCAGGCAAG",40.2045252679513,68.5352773751353 -"Sample_4_TGCGGGTAGACCTAGG",77.4624930507204,-25.2446794511477 -"Sample_4_TGCGGGTGTTATGTGC",-22.6746601684482,-104.752745285424 -"Sample_4_TGCGTGGGTAATTGGA",-60.8864346832461,-6.39458280395423 -"Sample_4_TGCTACCAGCTCCCAG",52.767477972397,113.242700013051 -"Sample_4_TGCTGCTGTCCAACTA",37.8811732918457,19.2023245123958 -"Sample_4_TGGACGCAGGAGCGAG",12.2093298091376,28.5960634329834 -"Sample_4_TGGACGCGTAACGCGA",92.7134613661334,-3.41933043188479 -"Sample_4_TGGCGCAAGTACGCGA",67.4885797253738,-11.9163347547451 -"Sample_4_TGGCGCAGTTCCCGAG",-80.4007507371799,-27.7277845379737 -"Sample_4_TGGCGCATCCTACAGA",65.7346485341691,-61.3065789319184 -"Sample_4_TGGGAAGAGGAACTGC",20.2006306917451,114.346627000487 -"Sample_4_TGGGAAGTCTATCCTA",-72.9744734963406,-63.8077671731043 -"Sample_4_TGGGCGTCAGTTAACC",92.9297871287718,18.9293428545852 -"Sample_4_TGGTTAGAGCGCCTTG",-79.4418822605112,-2.90185982276675 -"Sample_4_TGGTTAGAGGAATTAC",5.75999094463066,6.5281992750207 -"Sample_4_TGGTTAGGTCAATACC",-58.4186345281424,-16.9347938044765 -"Sample_4_TGGTTCCTCCACTCCA",39.6998307984878,20.6299678928732 -"Sample_4_TGTATTCAGATGTCGG",38.9649948939168,0.0896489071742557 -"Sample_4_TGTATTCCAGCGTCCA",-91.2072653665903,32.3488629721073 -"Sample_4_TGTATTCTCTGATACG",-95.8450893654835,18.584064120248 -"Sample_4_TGTCCCAAGAGACTAT",-76.0035301025882,46.6094959883339 -"Sample_4_TGTCCCATCTTGTACT",-70.9502133649966,6.1256968309874 -"Sample_4_TGTGGTACATGCAACT",39.0231879371899,111.100349443958 -"Sample_4_TGTGGTAGTGCATCTA",8.69790250542485,-82.3022487183165 -"Sample_4_TGTGGTATCCCGACTT",79.9180600666027,-48.2101707584807 -"Sample_4_TGTGTTTAGGCCCTCA",-61.9688777248932,35.2201550523449 -"Sample_4_TGTGTTTGTTGTGGAG",31.8976977789644,-11.4457702274288 -"Sample_4_TGTGTTTTCATGCTCC",103.432497607229,9.46592467845352 -"Sample_4_TGTTCCGAGCCACCTG",-71.6188714789653,-10.1807163099787 -"Sample_4_TGTTCCGCAGTGACAG",-25.9025725445662,-105.121285152537 -"Sample_4_TTAGGACAGGGTCGAT",12.1695794198911,-76.9538528430804 -"Sample_4_TTAGGACGTGGTACAG",78.6378202851815,-21.0289604386727 -"Sample_4_TTAGGCACATAAAGGT",70.5884120262782,1.99718621705118 -"Sample_4_TTAGTTCTCACTTACT",78.9696210967864,-35.6962106186803 -"Sample_4_TTAGTTCTCTTGAGAC",58.3443260560534,1.24438811950121 -"Sample_4_TTATGCTTCACGAAGG",46.1664089069499,115.5023618494 -"Sample_4_TTCGGTCAGCCAGAAC",-97.874871176213,37.7667405138911 -"Sample_4_TTCGGTCAGTTCCACA",-23.7984980427793,-179.233918322032 -"Sample_4_TTCTACACATTACCTT",1.2293288083248,-61.1921200039799 -"Sample_4_TTCTACATCGCAAGCC",72.2978993050035,-44.4006596393413 -"Sample_4_TTCTCAACACACTGCG",10.6678815847069,-9.07435436872709 -"Sample_4_TTCTCCTGTTCCCGAG",-86.0243414078266,5.41555316417833 -"Sample_4_TTCTCCTTCCGAATGT",83.6735825673034,-24.8326388002498 -"Sample_4_TTCTTAGGTTTAGGAA",28.7604648217987,-33.3681901917148 -"Sample_4_TTGAACGAGCCATCGC",-105.834271593022,-19.0697391035314 -"Sample_4_TTGAACGGTAGCTCCG",59.8833296688335,-49.4709112823173 -"Sample_4_TTGAACGGTCTCAACA",-20.6559637104643,-20.9426992749324 -"Sample_4_TTGAACGTCAGTTCGA",44.8903160390013,118.193023579604 -"Sample_4_TTGCCGTCACCTTGTC",-61.4610560425356,31.3349416334463 -"Sample_4_TTGCCGTTCTTGCAAG",68.9717455742605,-35.0364469614905 -"Sample_4_TTGCGTCCAGCCTTGG",29.264265187182,-21.2478817891865 -"Sample_4_TTGCGTCTCACGGTTA",37.2639658289417,65.8394463343302 -"Sample_4_TTGCGTCTCCTATTCA",101.704460465481,-54.0214284701712 -"Sample_4_TTGCGTCTCTTCTGGC",18.1380535259604,26.1494185570594 -"Sample_4_TTGGCAAAGAGATGAG",-82.278467898077,34.8639118945697 -"Sample_4_TTGGCAAGTATCACCA",55.4298213441246,-30.9374037776469 -"Sample_4_TTGTAGGAGTCGTACT",73.7358116830203,-21.5132750430236 -"Sample_4_TTTATGCAGCCACTAT",78.8234877862164,-16.488632232144 -"Sample_4_TTTATGCCAGTTAACC",72.5483138919536,-5.00826196127093 -"Sample_4_TTTGCGCAGGCTACGA",-69.4009796416618,34.0950798596322 -"Sample_4_TTTGCGCTCACTATTC",84.3794411911428,-51.1965452292239 -"Sample_4_TTTGGTTTCTGTTTGT",-15.33180940905,-70.9397637653258 -"Sample_4_TTTGTCACAATTGCTG",41.1054762875932,-47.1635836554135 -"Sample_4_TTTGTCAGTAGGAGTC",-25.0668815616648,-182.853327871521 -"Sample_4_TTTGTCATCCTCATTA",-108.489553284536,-45.9492414258316 diff --git a/sample_data_for_tsne/umap.csv b/sample_data_for_tsne/umap.csv deleted file mode 100644 index a437e8de7..000000000 --- a/sample_data_for_tsne/umap.csv +++ /dev/null @@ -1,3116 +0,0 @@ -"Sample_1_AAACCTGTCTGCTGTC",-0.463559031486511,0.27128928899765 -"Sample_1_AAACGGGGTTTGCATG",-3.87942218780518,8.66335678100586 -"Sample_1_AAACGGGTCCTTTACA",4.32222509384155,5.85532712936401 -"Sample_1_AAAGTAGCAAACAACA",-2.52073574066162,7.39792203903198 -"Sample_1_AAAGTAGCATAGAAAC",-2.83736324310303,7.09420871734619 -"Sample_1_AAATGCCCATGATCCA",2.83420872688293,6.36483526229858 -"Sample_1_AAATGCCGTTAGTGGG",-1.20449864864349,7.43769502639771 -"Sample_1_AAATGCCTCAGTTAGC",2.44457602500916,6.51109457015991 -"Sample_1_AACACGTGTCGCTTCT",3.0569281578064,6.97420263290405 -"Sample_1_AACCATGGTCTCACCT",-1.00512826442719,4.89711999893188 -"Sample_1_AACTCAGGTACCGTAT",1.63045561313629,-10.2235765457153 -"Sample_1_AACTCAGGTCCATCCT",-0.514523267745972,-8.9795503616333 -"Sample_1_AACTCAGGTCGCTTCT",2.7136561870575,7.42743873596191 -"Sample_1_AACTCAGTCCTTTCTC",1.52632403373718,-10.1818990707397 -"Sample_1_AACTCTTCAAGTAATG",2.9951708316803,-7.35612678527832 -"Sample_1_AACTCTTCACACATGT",-0.302775233983994,2.80618619918823 -"Sample_1_AACTCTTGTGCCTGTG",-3.84051656723022,8.87424945831299 -"Sample_1_AACTCTTTCAAACAAG",2.9142427444458,-10.4455261230469 -"Sample_1_AACTCTTTCCTCGCAT",-0.765527725219727,0.791369080543518 -"Sample_1_AACTGGTGTGCGGTAA",2.92453765869141,6.73920774459839 -"Sample_1_AACTTTCGTAGCGATG",2.16546869277954,-11.2901582717896 -"Sample_1_AAGACCTAGATGTCGG",4.41119146347046,6.45614290237427 -"Sample_1_AAGCCGCAGCTGAACG",2.89784550666809,-7.08886766433716 -"Sample_1_AAGCCGCGTTCGCGAC",1.7858579158783,-4.43002367019653 -"Sample_1_AAGGAGCCAATACGCT",2.30580568313599,8.25133419036865 -"Sample_1_AAGGCAGAGAGGTAGA",-0.490111291408539,6.93663835525513 -"Sample_1_AAGGCAGAGGAATCGC",-2.19970774650574,6.22334337234497 -"Sample_1_AAGGTTCGTCATATGC",-2.30830264091492,8.37007999420166 -"Sample_1_AAGGTTCTCAGTGCAT",2.9704282283783,6.91797733306885 -"Sample_1_AAGTCTGAGAGGTAGA",2.35080862045288,-4.71541213989258 -"Sample_1_AATCCAGCAAGCCCAC",1.57670319080353,-10.2813453674316 -"Sample_1_AATCCAGGTAAGCACG",-0.579985201358795,1.02347385883331 -"Sample_1_AATCCAGTCACAACGT",2.78580284118652,4.79348230361938 -"Sample_1_AATCGGTCATCACAAC",4.20543670654297,5.05584049224854 -"Sample_1_ACACCCTCAGCTGTGC",2.89558959007263,-9.03116321563721 -"Sample_1_ACACCCTCATACCATG",1.22310066223145,-7.33824348449707 -"Sample_1_ACACCCTGTCACAAGG",4.37484931945801,-8.38545608520508 -"Sample_1_ACACTGAAGCTGAACG",-1.99516558647156,6.16904783248901 -"Sample_1_ACACTGATCTTACCTA",1.20480942726135,-9.90668106079102 -"Sample_1_ACAGCCGAGCTACCGC",-0.381799399852753,-10.9794597625732 -"Sample_1_ACAGCTAAGGCAGTCA",-0.0859443992376328,-8.84190940856934 -"Sample_1_ACATACGAGAGAACAG",1.7825425863266,5.01163482666016 -"Sample_1_ACATACGAGGCTAGAC",1.35231447219849,2.41255879402161 -"Sample_1_ACATACGTCAACCAAC",4.16772651672363,-9.82475566864014 -"Sample_1_ACATCAGAGGTCGGAT",1.66140508651733,-7.63435506820679 -"Sample_1_ACATCAGGTCCGTTAA",3.62950134277344,-9.52772235870361 -"Sample_1_ACATGGTGTCGCTTCT",4.87885332107544,6.55304479598999 -"Sample_1_ACCAGTACACAACTGT",-1.2013772726059,6.50358581542969 -"Sample_1_ACCAGTATCCTCGCAT",4.86340761184692,5.87212419509888 -"Sample_1_ACCAGTATCTGAAAGA",1.89495253562927,-7.85774993896484 -"Sample_1_ACCGTAACAAGAAGAG",-1.87007737159729,8.63883876800537 -"Sample_1_ACCGTAACATTGAGCT",0.52234297990799,4.50984239578247 -"Sample_1_ACCGTAATCGCTTGTC",4.89007759094238,5.91274833679199 -"Sample_1_ACCTTTAAGCGATATA",-1.86044549942017,7.94542264938354 -"Sample_1_ACGAGCCAGACCTAGG",-1.99892485141754,9.27174377441406 -"Sample_1_ACGAGCCGTCTAGTCA",-0.87776255607605,-9.37428665161133 -"Sample_1_ACGATACAGAGACGAA",3.76665472984314,4.80968570709229 -"Sample_1_ACGATACCAATACGCT",4.11595726013184,-9.97762012481689 -"Sample_1_ACGATACGTCTGCCAG",-0.239020630717278,9.14091396331787 -"Sample_1_ACGATGTAGAGCTTCT",3.40364956855774,4.16323041915894 -"Sample_1_ACGATGTCATTCACTT",3.12285161018372,5.89504671096802 -"Sample_1_ACGATGTGTCATATCG",-0.21198607981205,4.21640062332153 -"Sample_1_ACGCAGCCAAGCGCTC",3.37458658218384,5.70337057113647 -"Sample_1_ACGCCGAAGGTGTTAA",3.40716099739075,-9.86660766601562 -"Sample_1_ACGGAGAAGACAGAGA",-0.854538023471832,4.93078136444092 -"Sample_1_ACGGCCAGTTGATTCG",-1.43822741508484,1.23847079277039 -"Sample_1_ACGTCAAAGGTAGCTG",2.3867359161377,-9.03950023651123 -"Sample_1_ACGTCAATCGGAGCAA",3.88439106941223,-9.98717594146729 -"Sample_1_ACGTCAATCGGCATCG",4.09003639221191,6.83379936218262 -"Sample_1_ACTATCTAGCTACCTA",1.3778247833252,-9.50332736968994 -"Sample_1_ACTGAACGTCAATGTC",-1.31208682060242,8.78950786590576 -"Sample_1_ACTGAACTCCCAACGG",-1.715008020401,9.34720039367676 -"Sample_1_ACTGCTCCATTAGCCA",3.59497141838074,-9.22525024414062 -"Sample_1_ACTGTCCAGAAGAAGC",3.47580194473267,3.53710198402405 -"Sample_1_ACTGTCCGTACCGCTG",3.10143852233887,4.90060234069824 -"Sample_1_ACTTACTAGCCGGTAA",-0.796901047229767,6.40911626815796 -"Sample_1_ACTTACTGTACCGGCT",3.41064858436584,-10.4163045883179 -"Sample_1_ACTTGTTCAGATGGCA",2.44819450378418,-7.75481367111206 -"Sample_1_ACTTGTTTCACGATGT",1.68618011474609,8.50193023681641 -"Sample_1_ACTTTCACACAGTCGC",-1.58031845092773,8.89341640472412 -"Sample_1_ACTTTCAGTAAGGATT",3.63461065292358,3.61460614204407 -"Sample_1_ACTTTCATCAGTTGAC",-2.96750545501709,8.35685729980469 -"Sample_1_AGAATAGTCAACACCA",0.611754596233368,-10.0023012161255 -"Sample_1_AGAGCGAAGGTCATCT",2.75124311447144,7.61185932159424 -"Sample_1_AGAGCGAGTACCGGCT",2.41421580314636,-9.17477893829346 -"Sample_1_AGAGCTTAGGAGTTTA",0.892576396465302,-9.83121204376221 -"Sample_1_AGAGCTTTCAAGAAGT",1.82870221138,-7.26447677612305 -"Sample_1_AGAGTGGTCCAGTAGT",-2.29790854454041,8.93339920043945 -"Sample_1_AGAGTGGTCCTTGACC",-2.19172215461731,6.55649757385254 -"Sample_1_AGCAGCCAGTTCCACA",-1.50989127159119,6.82952260971069 -"Sample_1_AGCCTAACAGTCTTCC",3.98323559761047,-8.7066125869751 -"Sample_1_AGCGTATAGTGCAAGC",4.71624374389648,6.30585432052612 -"Sample_1_AGCGTATGTGACGGTA",3.66984796524048,-9.25009441375732 -"Sample_1_AGCGTCGCAGCGAACA",2.36180734634399,7.81974792480469 -"Sample_1_AGCTCCTTCAGAGCTT",0.290029168128967,-11.4322910308838 -"Sample_1_AGCTCTCCATTTGCCC",2.37827014923096,-4.85275459289551 -"Sample_1_AGCTTGAAGACTAGAT",2.33547353744507,-8.53888416290283 -"Sample_1_AGCTTGAGTCTCACCT",-2.96331191062927,7.83878087997437 -"Sample_1_AGGCCACCAAGCGAGT",4.79220294952393,-9.06209850311279 -"Sample_1_AGGCCACCAAGTAATG",4.32549381256104,-9.20642566680908 -"Sample_1_AGGCCACCACGGCTAC",0.608061075210571,2.32701349258423 -"Sample_1_AGGCCACTCTCTTATG",1.36667680740356,-10.2177753448486 -"Sample_1_AGGCCGTGTAGAGGAA",3.80969762802124,5.14175653457642 -"Sample_1_AGGCCGTTCATCGATG",5.14268684387207,6.6541543006897 -"Sample_1_AGGGATGGTCTCAACA",-0.559553146362305,0.06999322026968 -"Sample_1_AGGGTGACAGTAAGAT",2.48358726501465,-9.95826053619385 -"Sample_1_AGGTCATCACAGACTT",4.25397348403931,-9.62050151824951 -"Sample_1_AGGTCATTCGAGAGCA",0.133194997906685,-8.35497570037842 -"Sample_1_AGGTCCGCAGCTGTGC",2.11953234672546,-4.55000448226929 -"Sample_1_AGTAGTCTCACTCCTG",1.67542850971222,4.74582529067993 -"Sample_1_AGTCTTTAGCCTCGTG",-1.05278253555298,2.26723885536194 -"Sample_1_AGTCTTTAGGATTCGG",-0.934557914733887,4.58750152587891 -"Sample_1_AGTGAGGAGGCTCTTA",1.2304470539093,-9.87077236175537 -"Sample_1_AGTGAGGGTGCACCAC",2.18969774246216,8.21578788757324 -"Sample_1_AGTGAGGTCTGATACG",0.658913910388947,3.1486120223999 -"Sample_1_AGTGTCATCCTTTCGG",-0.80131596326828,2.04837608337402 -"Sample_1_AGTGTCATCTGGGCCA",-1.01643002033234,-8.09378528594971 -"Sample_1_AGTGTCATCTTACCTA",-0.566655457019806,0.61968857049942 -"Sample_1_AGTTGGTTCGTCCAGG",4.33685684204102,5.429358959198 -"Sample_1_ATAACGCAGTGGCACA",2.5239634513855,-4.92396926879883 -"Sample_1_ATAACGCCAGTCAGAG",0.371875822544098,2.07680678367615 -"Sample_1_ATAACGCGTAGCTTGT",-1.60940623283386,7.8127613067627 -"Sample_1_ATAAGAGGTGCCTGGT",3.53421878814697,-10.0646486282349 -"Sample_1_ATAGACCTCTTTACGT",1.67540168762207,-9.05197238922119 -"Sample_1_ATCACGAAGTGGAGTC",-0.478406876325607,3.94769144058228 -"Sample_1_ATCACGATCAGCAACT",-2.78579711914062,6.89767646789551 -"Sample_1_ATCATCTCAACACGCC",-2.87770104408264,6.81166648864746 -"Sample_1_ATCATGGAGGTGATAT",-0.571381688117981,-10.2016582489014 -"Sample_1_ATCATGGCAGCCAGAA",-9.65121555328369,-2.74186873435974 -"Sample_1_ATCCACCAGTAGATGT",-0.778923690319061,-9.53645420074463 -"Sample_1_ATCCACCCATCGATGT",-9.58588218688965,-2.67464256286621 -"Sample_1_ATCCACCGTAACGACG",1.49840748310089,3.00287699699402 -"Sample_1_ATCCGAAAGAAGATTC",2.42451906204224,-10.7047071456909 -"Sample_1_ATCCGAACACTAGTAC",-0.443780660629272,-10.2637128829956 -"Sample_1_ATCCGAAGTACACCGC",1.71249973773956,-8.08173179626465 -"Sample_1_ATCGAGTCACAACTGT",3.53052830696106,6.44539260864258 -"Sample_1_ATCTACTCACGAGGTA",0.171856760978699,-9.60459995269775 -"Sample_1_ATCTACTCATGGGACA",0.487343281507492,6.31212997436523 -"Sample_1_ATCTACTTCGGCCGAT",-1.27239167690277,8.23403739929199 -"Sample_1_ATCTGCCAGCGCTCCA",2.41461968421936,5.79222536087036 -"Sample_1_ATCTGCCAGTGACATA",3.23415112495422,-8.87864112854004 -"Sample_1_ATCTGCCAGTTGAGTA",-0.203844889998436,-9.76652526855469 -"Sample_1_ATCTGCCCACTTAACG",1.18754458427429,5.20887327194214 -"Sample_1_ATCTGCCCATCCCACT",1.2210282087326,-8.01252555847168 -"Sample_1_ATCTGCCGTCTCTTTA",-0.52409040927887,2.92110419273376 -"Sample_1_ATCTGCCGTTAGTGGG",0.853027939796448,8.41493511199951 -"Sample_1_ATCTGCCTCGCTTAGA",0.624929487705231,3.6301646232605 -"Sample_1_ATGAGGGGTACCGTAT",-1.31970632076263,0.115629270672798 -"Sample_1_ATGCGATGTTCTGTTT",3.36940979957581,5.52611446380615 -"Sample_1_ATGGGAGAGTTGTAGA",3.09861612319946,-6.92862510681152 -"Sample_1_ATGTGTGAGCATCATC",1.22756969928741,-10.415002822876 -"Sample_1_ATGTGTGAGGTCATCT",0.815921008586884,-9.03110218048096 -"Sample_1_ATGTGTGGTGTATGGG",3.31167316436768,4.20487689971924 -"Sample_1_ATTACTCAGGTCATCT",-0.54111236333847,-10.6409482955933 -"Sample_1_ATTACTCGTGGAAAGA",1.77862846851349,-4.24353885650635 -"Sample_1_ATTATCCAGTAGCGGT",-0.0804253444075584,-10.2319936752319 -"Sample_1_ATTATCCCACCCATGG",4.83283758163452,6.19515705108643 -"Sample_1_ATTCTACCAAGCGATG",-0.445504128932953,3.66627717018127 -"Sample_1_ATTCTACCATTAACCG",1.09785544872284,5.87448215484619 -"Sample_1_ATTCTACGTCTAGTCA",1.33598470687866,-7.4315447807312 -"Sample_1_ATTCTACGTGGTAACG",-1.65370309352875,0.284121036529541 -"Sample_1_ATTGGACCAGCGTTCG",-1.78101325035095,0.713341593742371 -"Sample_1_ATTGGACCAGCTTCGG",1.49016416072845,2.39981245994568 -"Sample_1_ATTGGACGTAGCGTGA",-2.39092779159546,8.75310325622559 -"Sample_1_ATTGGACGTCGCATCG",-0.568874716758728,-7.45785522460938 -"Sample_1_ATTGGTGAGCCCGAAA",3.87436842918396,8.06886005401611 -"Sample_1_CAACCAATCCTTGGTC",1.85938584804535,-10.6938591003418 -"Sample_1_CAACTAGCATGGTAGG",0.444642275571823,-10.9781732559204 -"Sample_1_CAACTAGTCGCATGGC",3.67217469215393,4.44149017333984 -"Sample_1_CAAGAAAAGACTAAGT",-0.461171239614487,-10.0358076095581 -"Sample_1_CAAGAAAAGTTAGCGG",-0.913442373275757,-9.3472204208374 -"Sample_1_CAAGAAATCTATGTGG",2.4080605506897,-7.9039249420166 -"Sample_1_CAAGATCGTTACTGAC",-0.705993413925171,0.502585411071777 -"Sample_1_CAAGATCGTTCAACCA",4.06773996353149,-9.06484794616699 -"Sample_1_CAAGATCGTTCAGCGC",3.37582325935364,6.59067440032959 -"Sample_1_CAAGATCGTTTAGGAA",-2.89604735374451,8.69244861602783 -"Sample_1_CAAGTTGGTAACGCGA",-2.18872356414795,6.14468860626221 -"Sample_1_CACAAACAGTTCGATC",3.33541393280029,4.73566913604736 -"Sample_1_CACACAACAATCACAC",-1.42048728466034,8.60146808624268 -"Sample_1_CACACAAGTCGCGTGT",-0.36109921336174,6.46589708328247 -"Sample_1_CACACCTCAAACCTAC",-0.0752605050802231,-11.2059812545776 -"Sample_1_CACACCTTCCCATTTA",3.69698071479797,-9.83880805969238 -"Sample_1_CACACTCCAGTACACT",0.302799552679062,-9.39656639099121 -"Sample_1_CACACTCCATGTAAGA",-1.15875506401062,9.82337951660156 -"Sample_1_CACACTCTCTAACCGA",1.15156519412994,1.41201436519623 -"Sample_1_CACAGGCGTGGGTATG",1.74510419368744,-11.1257190704346 -"Sample_1_CACAGTAAGTGACATA",-1.72241485118866,0.955516397953033 -"Sample_1_CACATAGCAAGCCGCT",-2.08288741111755,9.1315860748291 -"Sample_1_CACATAGGTAGCAAAT",-0.27866131067276,-10.177417755127 -"Sample_1_CACATAGTCGCGTTTC",-2.39640355110168,7.1630334854126 -"Sample_1_CACATTTCATTGCGGC",-0.480587512254715,7.12131977081299 -"Sample_1_CACATTTTCACGAAGG",4.37657451629639,-9.47565937042236 -"Sample_1_CACATTTTCCGTCATC",-2.06394028663635,8.14588832855225 -"Sample_1_CACCACTCACTCGACG",-0.0998870208859444,2.10886740684509 -"Sample_1_CACCACTTCATACGGT",1.23085379600525,-8.50186920166016 -"Sample_1_CACCACTTCCGCAAGC",1.76651978492737,-9.63887405395508 -"Sample_1_CACCACTTCTTGAGAC",1.33997654914856,-8.02445125579834 -"Sample_1_CACCAGGCACCTCGTT",-1.77731072902679,0.249673694372177 -"Sample_1_CACCAGGTCAAGGTAA",1.93451154232025,-5.20873880386353 -"Sample_1_CACCTTGTCTCTAAGG",0.348949998617172,5.99846458435059 -"Sample_1_CAGAGAGCATCACGTA",0.0168837998062372,5.74600076675415 -"Sample_1_CAGATCAAGATATGGT",4.78597164154053,5.06462812423706 -"Sample_1_CAGCAGCGTCGCCATG",0.0234979223459959,8.53036022186279 -"Sample_1_CAGCAGCTCGGAATCT",-9.7459831237793,-2.83713102340698 -"Sample_1_CAGCAGCTCTGTCCGT",5.17809438705444,6.07472658157349 -"Sample_1_CAGCCGAAGCGACGTA",1.78307318687439,-8.52631950378418 -"Sample_1_CAGCGACAGTAGCGGT",1.97281169891357,-7.80335140228271 -"Sample_1_CAGCTGGAGTGTACGG",2.36268353462219,-8.91238784790039 -"Sample_1_CAGCTGGTCTTTCCTC",0.594456553459167,-10.2626962661743 -"Sample_1_CAGTAACCATCTCGCT",-1.1311844587326,2.66928648948669 -"Sample_1_CAGTAACTCTGTCCGT",-0.565705716609955,9.11149883270264 -"Sample_1_CAGTCCTAGACAAGCC",1.2447783946991,-7.52887678146362 -"Sample_1_CAGTCCTAGATCTGCT",-2.33919215202332,7.74161624908447 -"Sample_1_CAGTCCTAGTAGATGT",3.889319896698,4.13125944137573 -"Sample_1_CAGTCCTCAGGCGATA",3.58737468719482,6.17286729812622 -"Sample_1_CAGTCCTGTTCAGCGC",3.10988306999207,4.3418083190918 -"Sample_1_CAGTCCTTCTCTTGAT",3.84278798103333,4.96447801589966 -"Sample_1_CATATGGTCTGTGCAA",2.00363111495972,-4.90690517425537 -"Sample_1_CATATTCCAGTGACAG",-2.58675384521484,6.67126178741455 -"Sample_1_CATATTCGTAAATACG",2.40221738815308,8.27754211425781 -"Sample_1_CATCAAGAGGTGTTAA",0.0630362555384636,5.19026231765747 -"Sample_1_CATCAAGCACGACGAA",5.24060583114624,6.38461589813232 -"Sample_1_CATCAAGCAGGCAGTA",3.17114496231079,-9.54288673400879 -"Sample_1_CATCAAGCATGTTGAC",0.755975067615509,2.47337126731873 -"Sample_1_CATCAAGGTCTCCACT",3.95773458480835,-9.28275012969971 -"Sample_1_CATCAGAAGAAACCAT",-0.232032909989357,5.63811492919922 -"Sample_1_CATCAGACACCCTATC",3.51261401176453,5.64078712463379 -"Sample_1_CATCAGAGTCCATGAT",0.627810120582581,-8.96159744262695 -"Sample_1_CATCAGAGTCCGAAGA",-1.37576651573181,-0.0231322832405567 -"Sample_1_CATCCACGTCCCTTGT",-0.18080335855484,5.19724941253662 -"Sample_1_CATCCACGTGCATCTA",-0.00507249962538481,6.67111539840698 -"Sample_1_CATCCACTCTGAGGGA",2.91036915779114,-9.02821731567383 -"Sample_1_CATCGAAAGGAATCGC",-1.04445242881775,6.89203262329102 -"Sample_1_CATCGAACAGGCTGAA",-2.75409817695618,7.36702346801758 -"Sample_1_CATCGAACAGGGAGAG",3.00789451599121,7.0022177696228 -"Sample_1_CATCGGGCAAAGGTGC",0.0438354015350342,-11.3050632476807 -"Sample_1_CATGACAAGTGAACGC",2.04186844825745,-5.21881484985352 -"Sample_1_CATGACACACAGACTT",1.71415591239929,-10.8167772293091 -"Sample_1_CATGACACACCGTTGG",3.3174159526825,3.91487193107605 -"Sample_1_CATGACACAGCTATTG",2.94020867347717,-9.61607265472412 -"Sample_1_CATGACAGTCTCACCT",3.10072755813599,4.4870982170105 -"Sample_1_CATGCCTCAATGAAAC",1.06381857395172,-9.70048427581787 -"Sample_1_CATGGCGAGTGTGGCA",2.47490406036377,-7.07280588150024 -"Sample_1_CATGGCGCACGAGGTA",1.9792947769165,-7.69011211395264 -"Sample_1_CATGGCGGTACAGTGG",0.440909385681152,-8.57760238647461 -"Sample_1_CATTATCAGGGTTCCC",2.5673565864563,-10.3409767150879 -"Sample_1_CATTATCCACAGCCCA",-3.84395003318787,8.70332336425781 -"Sample_1_CATTATCGTGGTCTCG",2.44035959243774,6.45651531219482 -"Sample_1_CCAATCCGTCTCTTAT",-0.76575756072998,9.56751537322998 -"Sample_1_CCAATCCTCTCATTCA",-2.16597080230713,8.92309093475342 -"Sample_1_CCACCTATCTTGCCGT",0.697722792625427,-11.2273750305176 -"Sample_1_CCACGGACAATCACAC",3.53270649909973,-9.78309535980225 -"Sample_1_CCACGGATCAGGCAAG",2.36914968490601,-4.821044921875 -"Sample_1_CCACTACAGTGGACGT",3.45099496841431,5.1000828742981 -"Sample_1_CCAGCGAGTTCGTTGA",4.47835874557495,7.14763879776001 -"Sample_1_CCATTCGCAAGCCATT",-2.19050478935242,9.32952308654785 -"Sample_1_CCCAATCTCAACACCA",3.72384238243103,7.22496032714844 -"Sample_1_CCCTCCTAGTAGCCGA",2.67608094215393,-9.94626426696777 -"Sample_1_CCCTCCTGTTGTACAC",-2.77268433570862,8.73142910003662 -"Sample_1_CCGGTAGAGTTACGGG",-1.94900166988373,9.13476848602295 -"Sample_1_CCGGTAGTCGCCTGAG",-0.346305727958679,-9.99585151672363 -"Sample_1_CCGTACTCAGGGTATG",4.4346809387207,5.63301467895508 -"Sample_1_CCGTACTTCTACTATC",2.30927538871765,-10.162428855896 -"Sample_1_CCGTGGAAGCTGAAAT",0.248432323336601,8.61422252655029 -"Sample_1_CCGTTCAAGCCCGAAA",0.899591088294983,-9.11897277832031 -"Sample_1_CCTAAAGAGCGATTCT",3.52225279808044,5.047682762146 -"Sample_1_CCTAAAGCAAGTAATG",3.43550872802734,6.08645820617676 -"Sample_1_CCTACACAGCAAATCA",-9.55943775177002,-2.64964008331299 -"Sample_1_CCTACACTCTTTACGT",2.94011187553406,-10.7847766876221 -"Sample_1_CCTACCACATCGGAAG",-0.994282484054565,0.110569573938847 -"Sample_1_CCTAGCTAGAGGTACC",-2.75922751426697,7.46610164642334 -"Sample_1_CCTATTATCAAACCAC",1.77717697620392,7.98904800415039 -"Sample_1_CCTCTGAGTAAGAGGA",-0.769384860992432,0.120132997632027 -"Sample_1_CCTCTGAGTTATTCTC",3.06545925140381,6.632399559021 -"Sample_1_CCTCTGATCAGCCTAA",0.73478627204895,-8.36841773986816 -"Sample_1_CCTTACGAGCACCGCT",2.17968821525574,-9.68575382232666 -"Sample_1_CCTTACGGTCGCATCG",1.81071054935455,-7.07152080535889 -"Sample_1_CCTTCCCCAAACCTAC",-0.123919628560543,9.3926420211792 -"Sample_1_CCTTCCCTCACTGGGC",-2.03845858573914,7.19721555709839 -"Sample_1_CCTTCGAAGCGTTTAC",3.56893587112427,-8.37166690826416 -"Sample_1_CCTTCGACAAGAAGAG",4.00477886199951,6.70125198364258 -"Sample_1_CGAACATAGTCAAGCG",1.87806880474091,-6.78408050537109 -"Sample_1_CGAACATCATCAGTCA",4.20589876174927,3.86020541191101 -"Sample_1_CGAATGTGTCATTAGC",1.22296571731567,8.5341157913208 -"Sample_1_CGACCTTAGCACCGCT",-0.220777273178101,5.40536308288574 -"Sample_1_CGACCTTAGTTGTAGA",-9.59134387969971,-2.68039727210999 -"Sample_1_CGACCTTTCAAGCCTA",-0.871612966060638,0.59259432554245 -"Sample_1_CGACTTCTCATAGCAC",3.20290374755859,-8.6392068862915 -"Sample_1_CGAGCACAGACGCAAC",1.51586127281189,-10.4823131561279 -"Sample_1_CGAGCACAGCTCAACT",5.4262547492981,6.61784839630127 -"Sample_1_CGAGCACAGGCGTACA",-3.0296037197113,7.72162580490112 -"Sample_1_CGAGCACTCTGCAAGT",-3.02756762504578,7.93671941757202 -"Sample_1_CGAGCCACATGCAACT",-2.39545130729675,6.50141954421997 -"Sample_1_CGAGCCATCGTGGTCG",0.118915848433971,4.0856032371521 -"Sample_1_CGATCGGAGACGCTTT",-0.942717790603638,1.26931869983673 -"Sample_1_CGATCGGGTCCCTTGT",-0.311519205570221,1.38760602474213 -"Sample_1_CGATGGCTCGGTCCGA",-0.703660845756531,9.6351432800293 -"Sample_1_CGATGTAAGGACATTA",2.69289517402649,5.67592811584473 -"Sample_1_CGATTGAGTGGTACAG",2.4296669960022,-7.28416109085083 -"Sample_1_CGCCAAGAGACCACGA",-1.55237197875977,0.290328741073608 -"Sample_1_CGCCAAGGTGTCCTCT",2.64222478866577,-11.172399520874 -"Sample_1_CGCGGTAAGGCATGGT",3.52520227432251,-8.27082443237305 -"Sample_1_CGCGGTAAGTATCGAA",1.30341637134552,-10.4853525161743 -"Sample_1_CGCGGTACACAGGCCT",-0.577741324901581,-9.55523109436035 -"Sample_1_CGCGGTATCAACGCTA",1.42548716068268,-8.31450366973877 -"Sample_1_CGCGGTATCTCTGCTG",-0.701200485229492,4.63832187652588 -"Sample_1_CGCGTTTTCCAGATCA",1.53237640857697,2.18888473510742 -"Sample_1_CGCTATCCACTGTCGG",-2.63296365737915,7.32019424438477 -"Sample_1_CGCTATCGTGCAGACA",1.72842288017273,-10.254602432251 -"Sample_1_CGCTATCTCGCCATAA",1.02027058601379,-7.88271999359131 -"Sample_1_CGCTATCTCTGTACGA",0.429194837808609,4.70363616943359 -"Sample_1_CGCTGGATCCTAGGGC",-0.287453681230545,7.05276489257812 -"Sample_1_CGCTTCACATTTCAGG",3.01135110855103,4.62495565414429 -"Sample_1_CGGACACAGCAGCGTA",-1.65239548683167,9.84995555877686 -"Sample_1_CGGACACAGGTGCTTT",0.505939483642578,-8.15459156036377 -"Sample_1_CGGACACTCCTCGCAT",-1.3574812412262,1.77687704563141 -"Sample_1_CGGACGTTCAGGTTCA",3.45047450065613,6.77175569534302 -"Sample_1_CGGACTGTCTCTGAGA",-0.771688759326935,7.42810297012329 -"Sample_1_CGGAGCTCAAACTGTC",3.18879222869873,3.82512140274048 -"Sample_1_CGGAGCTGTACCGTTA",4.37616443634033,7.13164615631104 -"Sample_1_CGGAGTCTCAGGCCCA",0.488511353731155,-10.135516166687 -"Sample_1_CGGCTAGCAATCTGCA",-0.0722795501351357,-11.1829032897949 -"Sample_1_CGTAGCGCAAACAACA",3.24545168876648,-7.32401418685913 -"Sample_1_CGTAGCGTCAGCACAT",2.57239961624146,-8.44242191314697 -"Sample_1_CGTAGCGTCCTAGAAC",1.98351907730103,-4.74703550338745 -"Sample_1_CGTAGGCGTCTGATTG",-0.411053359508514,7.43650817871094 -"Sample_1_CGTCAGGCAAGCTGTT",0.0623848550021648,-9.1264591217041 -"Sample_1_CGTCAGGTCAAGGTAA",-2.64447140693665,8.86438369750977 -"Sample_1_CGTCCATAGCTATGCT",2.21137070655823,-8.25054550170898 -"Sample_1_CGTCCATCACCCTATC",3.18663787841797,4.84319591522217 -"Sample_1_CGTCTACCATAGGATA",-1.49236154556274,8.76383018493652 -"Sample_1_CGTGAGCCATTCTTAC",2.04542541503906,7.80729675292969 -"Sample_1_CGTGAGCTCAATCTCT",-9.68434810638428,-2.77521347999573 -"Sample_1_CGTTGGGTCAGAGACG",4.27883386611938,7.04338455200195 -"Sample_1_CGTTGGGTCCTTGCCA",-1.01970958709717,5.23676347732544 -"Sample_1_CTAACTTAGCCCAGCT",3.78999733924866,5.56672286987305 -"Sample_1_CTAAGACAGTGTCTCA",0.692916095256805,-10.3941383361816 -"Sample_1_CTAAGACCAGATCGGA",2.14137864112854,8.07774925231934 -"Sample_1_CTAAGACGTCGGATCC",1.88571536540985,-4.62730932235718 -"Sample_1_CTAAGACTCCACTGGG",1.96300554275513,-5.01746273040771 -"Sample_1_CTAATGGCACAGACTT",-1.51943206787109,7.44185066223145 -"Sample_1_CTAATGGGTTCAGGCC",1.69961595535278,-7.671311378479 -"Sample_1_CTACACCCAACGCACC",2.36763954162598,-8.64446449279785 -"Sample_1_CTACACCGTTCAGCGC",2.47601747512817,-10.017373085022 -"Sample_1_CTACACCTCGGTTCGG",2.50060033798218,4.72233295440674 -"Sample_1_CTACATTCATGCCCGA",3.05085158348083,-8.61839389801025 -"Sample_1_CTACATTGTTTGGCGC",3.50059175491333,-7.82734441757202 -"Sample_1_CTACCCAAGGTGCACA",2.47169971466064,-5.09640884399414 -"Sample_1_CTACCCACACGACTCG",2.2097053527832,-6.95829296112061 -"Sample_1_CTACCCACAGCGATCC",0.106824487447739,-10.9473457336426 -"Sample_1_CTACGTCAGAGCTGCA",-1.74871504306793,8.53816795349121 -"Sample_1_CTACGTCAGGCGATAC",3.16552686691284,7.09193658828735 -"Sample_1_CTACGTCGTCTCAACA",1.79297733306885,-8.68103694915771 -"Sample_1_CTACGTCTCGTCGTTC",0.707013785839081,-8.96208381652832 -"Sample_1_CTAGAGTCAATCTACG",1.07578718662262,-9.35842895507812 -"Sample_1_CTAGAGTCAGGAATCG",-0.696973741054535,0.933324337005615 -"Sample_1_CTAGAGTTCAACTCTT",3.69802665710449,5.8221116065979 -"Sample_1_CTAGTGACAGACGCTC",-1.2039532661438,6.70439147949219 -"Sample_1_CTAGTGACAGCGTAAG",-0.951570391654968,5.65003728866577 -"Sample_1_CTAGTGACATCTATGG",0.754350483417511,6.65543651580811 -"Sample_1_CTAGTGATCCTTGACC",-1.53773200511932,9.29082012176514 -"Sample_1_CTCACACTCACATACG",2.93328738212585,4.76689195632935 -"Sample_1_CTCAGAATCGAGGTAG",1.74549067020416,2.13115930557251 -"Sample_1_CTCAGAATCGGAAATA",-1.24728047847748,2.17150449752808 -"Sample_1_CTCATTAAGTCGTTTG",-0.278563052415848,8.12511730194092 -"Sample_1_CTCCTAGGTCATACTG",1.60536992549896,-10.105263710022 -"Sample_1_CTCCTAGTCGGCGCTA",-1.16230344772339,0.910468280315399 -"Sample_1_CTCGAAAAGAACAATC",0.688374102115631,5.26966381072998 -"Sample_1_CTCGAAAAGACGACGT",-0.969482243061066,3.41217827796936 -"Sample_1_CTCGAAACACAGCGTC",4.66794013977051,-8.9266939163208 -"Sample_1_CTCGGGAGTTCGGCAC",2.80904102325439,6.28461122512817 -"Sample_1_CTCGTCACACACTGCG",0.99674254655838,-9.7141695022583 -"Sample_1_CTCGTCACACAGGAGT",0.605369091033936,-10.1221284866333 -"Sample_1_CTCGTCAGTTCCACGG",4.20492076873779,4.16893815994263 -"Sample_1_CTCTAATAGTCCGTAT",0.664677858352661,-11.162670135498 -"Sample_1_CTCTAATAGTGGGTTG",4.37943887710571,5.05606937408447 -"Sample_1_CTCTACGAGGTGCACA",3.79614877700806,4.42203044891357 -"Sample_1_CTCTACGTCGAACGGA",4.63490200042725,7.02498912811279 -"Sample_1_CTCTGGTAGCGATATA",3.74753785133362,4.32856369018555 -"Sample_1_CTCTGGTAGGGAACGG",-0.494250893592834,8.30830860137939 -"Sample_1_CTCTGGTAGGTCGGAT",2.90352034568787,6.94984722137451 -"Sample_1_CTCTGGTCACTGCCAG",3.11969470977783,4.14710187911987 -"Sample_1_CTGAAACGTGCCTGTG",0.540169775485992,-8.17657661437988 -"Sample_1_CTGAAGTCACGAAGCA",-2.25880408287048,7.93933582305908 -"Sample_1_CTGAAGTTCCGCAAGC",2.15600371360779,-8.90572452545166 -"Sample_1_CTGAAGTTCGCAAACT",-2.75590109825134,8.06023216247559 -"Sample_1_CTGATAGAGCGTCAAG",-0.234809145331383,9.64826393127441 -"Sample_1_CTGATAGTCGAATCCA",1.14090037345886,8.470778465271 -"Sample_1_CTGATCCTCATAACCG",4.99145793914795,6.03238916397095 -"Sample_1_CTGATCCTCGCAAGCC",-2.84604144096375,8.18588733673096 -"Sample_1_CTGATCCTCTACTATC",-1.87423133850098,6.75371694564819 -"Sample_1_CTGCCTAAGAAGGTGA",0.431790769100189,-10.2718181610107 -"Sample_1_CTGCCTAAGACAGAGA",0.690374672412872,2.89846467971802 -"Sample_1_CTGCCTACAGACAGGT",4.11702585220337,-9.92671203613281 -"Sample_1_CTGCTGTCAACGATCT",-0.0789527148008347,-7.78494644165039 -"Sample_1_CTGGTCTCAACCGCCA",4.51300239562988,4.46926164627075 -"Sample_1_CTGGTCTGTATCAGTC",0.0246476363390684,-9.97648525238037 -"Sample_1_CTGGTCTGTTGACGTT",1.82693767547607,7.89642190933228 -"Sample_1_CTGGTCTGTTGTCGCG",-1.99664735794067,8.44965553283691 -"Sample_1_CTGTGCTCACATGGGA",-0.913871049880981,9.0831413269043 -"Sample_1_CTGTGCTCACGGCCAT",2.77731490135193,5.03067302703857 -"Sample_1_CTGTGCTCACTTCGAA",-0.474156647920609,-7.00108814239502 -"Sample_1_CTGTGCTTCAGCGATT",3.83106756210327,6.7234935760498 -"Sample_1_CTTAACTGTGGACGAT",-1.18547022342682,8.16516590118408 -"Sample_1_CTTAACTTCAACGGGA",2.7127513885498,-10.636570930481 -"Sample_1_CTTACCGAGCGTCAAG",2.53008604049683,6.5587797164917 -"Sample_1_CTTAGGAAGCCAACAG",-2.21248030662537,7.6800799369812 -"Sample_1_CTTCTCTAGTATCGAA",3.2545702457428,5.242271900177 -"Sample_1_CTTCTCTAGTGCGATG",1.55109906196594,-10.6372385025024 -"Sample_1_CTTTGCGTCGGGAGTA",3.32105731964111,7.79813146591187 -"Sample_1_GAAACTCAGTACGTTC",-0.0590820275247097,3.00523972511292 -"Sample_1_GAAACTCGTCTGCCAG",1.43477606773376,1.62395751476288 -"Sample_1_GAAATGAAGAAGGGTA",3.6400351524353,4.82408857345581 -"Sample_1_GAAATGAGTGTTAAGA",3.89413261413574,6.3429069519043 -"Sample_1_GAACATCTCCTTGGTC",0.147489830851555,9.08437347412109 -"Sample_1_GAACCTACAACACCCG",-2.72442746162415,8.52769947052002 -"Sample_1_GAACGGAAGCAAATCA",2.40917038917542,6.35535955429077 -"Sample_1_GAAGCAGGTTCAGGCC",2.13171696662903,-10.4592218399048 -"Sample_1_GAAGCAGTCTGTGCAA",2.79256415367126,-10.4532032012939 -"Sample_1_GAATAAGAGCGATTCT",2.80030536651611,4.93432283401489 -"Sample_1_GAATAAGAGTTGAGAT",0.255801111459732,-9.0773811340332 -"Sample_1_GAATAAGGTACCGTTA",-2.08134174346924,9.20035934448242 -"Sample_1_GAATAAGTCCACTCCA",0.482437074184418,-9.88090896606445 -"Sample_1_GACACGCCACGACGAA",1.77269780635834,6.04607200622559 -"Sample_1_GACACGCGTAACGTTC",-0.344223469495773,4.08104705810547 -"Sample_1_GACAGAGAGGCGTACA",1.77840542793274,-5.82710361480713 -"Sample_1_GACAGAGTCGAGAACG",-1.19841289520264,0.437630861997604 -"Sample_1_GACCAATGTGAGCGAT",-0.459125220775604,3.54582333564758 -"Sample_1_GACCTGGCACTTCGAA",2.00395822525024,-9.84332847595215 -"Sample_1_GACCTGGCAGTAAGAT",-0.690893054008484,-0.0484486930072308 -"Sample_1_GACGGCTGTTGATTGC",0.115743860602379,-10.0460443496704 -"Sample_1_GACGGCTGTTTGGGCC",3.63397407531738,8.15212535858154 -"Sample_1_GACGGCTTCAACGCTA",3.45173978805542,5.30575752258301 -"Sample_1_GACGTGCTCACCATAG",-1.38138711452484,0.540376663208008 -"Sample_1_GACGTTAAGAATAGGG",1.24904870986938,-8.83970832824707 -"Sample_1_GACGTTAAGTGTACGG",-1.23987770080566,7.93319177627563 -"Sample_1_GACTACAAGTACACCT",3.67229127883911,7.68147802352905 -"Sample_1_GACTACATCGGAATCT",1.89055633544922,-8.93047523498535 -"Sample_1_GACTGCGGTTCGGCAC",5.0992488861084,6.02694940567017 -"Sample_1_GACTGCGTCAGTTGAC",-1.82335186004639,7.77199745178223 -"Sample_1_GACTGCGTCTGTACGA",-3.26714158058167,7.48848247528076 -"Sample_1_GAGCAGAAGTGCTGCC",-2.72569298744202,8.85517311096191 -"Sample_1_GAGCAGAGTCTTGATG",-0.114556439220905,-8.72591876983643 -"Sample_1_GAGGTGAGTCCTAGCG",-3.91759777069092,8.89718532562256 -"Sample_1_GAGGTGATCCCTAACC",4.48416471481323,-8.21122741699219 -"Sample_1_GATCAGTAGAGTTGGC",-1.40649104118347,8.05350399017334 -"Sample_1_GATCAGTAGTAGCGGT",-2.33520460128784,8.5764684677124 -"Sample_1_GATCAGTCAAAGCGGT",-0.718833148479462,8.70720100402832 -"Sample_1_GATCAGTTCCGAGCCA",0.809561252593994,-9.30906963348389 -"Sample_1_GATCAGTTCCTCGCAT",0.247267454862595,-8.882155418396 -"Sample_1_GATCGATGTTCCCGAG",-1.44037353992462,7.87010145187378 -"Sample_1_GATCGATTCCACGCAG",0.848930478096008,-10.4926357269287 -"Sample_1_GATCGCGAGACCTTTG",3.75076770782471,4.36834716796875 -"Sample_1_GATCGCGCAAGAAAGG",2.47015929222107,-5.13344526290894 -"Sample_1_GATCGCGGTCAACTGT",4.32910776138306,-8.34249305725098 -"Sample_1_GATCGCGTCAAGGTAA",0.419142216444016,-7.20157623291016 -"Sample_1_GATCGTAAGGTACTCT",-2.35361385345459,8.44811534881592 -"Sample_1_GATGAAAAGACACTAA",-2.16669344902039,8.51125144958496 -"Sample_1_GATGAAATCGTCTGAA",0.608438611030579,-9.77297592163086 -"Sample_1_GATGAGGTCCTAGGGC",2.92352557182312,-7.74598598480225 -"Sample_1_GATGCTAAGTCCCACG",-2.50823783874512,7.89695882797241 -"Sample_1_GATGCTACAAAGGAAG",3.3099536895752,4.10033941268921 -"Sample_1_GATGCTAGTAGAGGAA",3.6833643913269,5.86702919006348 -"Sample_1_GATTCAGAGACAGGCT",0.506023585796356,2.37659072875977 -"Sample_1_GCAAACTCATCCCATC",0.195472240447998,2.00387382507324 -"Sample_1_GCAAACTGTGGACGAT",2.8027970790863,-7.55388450622559 -"Sample_1_GCAATCAAGTAGGCCA",3.89770817756653,-9.63875389099121 -"Sample_1_GCAATCAGTCTAGTCA",3.319176197052,-7.93057918548584 -"Sample_1_GCAATCATCACCCTCA",1.55381631851196,-8.63716602325439 -"Sample_1_GCAGTTACAACGATGG",0.915478527545929,-10.5911989212036 -"Sample_1_GCAGTTAGTTCAGCGC",-0.50658243894577,9.50882816314697 -"Sample_1_GCATACATCCTGCCAT",4.18612575531006,4.04001998901367 -"Sample_1_GCATACATCTTTAGGG",2.39410734176636,-10.1385555267334 -"Sample_1_GCATGCGGTCCCTTGT",0.753268539905548,5.58596706390381 -"Sample_1_GCATGTAAGACGCACA",2.04623961448669,6.26838445663452 -"Sample_1_GCATGTAAGAGTAATC",-1.86949074268341,1.27132964134216 -"Sample_1_GCATGTACACCTCGTT",-0.607209146022797,-0.21784308552742 -"Sample_1_GCCAAATAGTGGAGAA",5.28897666931152,5.73409652709961 -"Sample_1_GCCTCTACAAACTGCT",1.99360835552216,2.94407296180725 -"Sample_1_GCGACCACATGCCCGA",4.27168607711792,4.8252100944519 -"Sample_1_GCGAGAAAGCTACCGC",-1.8541671037674,8.79474830627441 -"Sample_1_GCGAGAACAAGGCTCC",2.24975895881653,6.80652856826782 -"Sample_1_GCGAGAACACCAGTTA",0.461079746484756,-8.72281551361084 -"Sample_1_GCGCAACAGGGTGTGT",-0.853554964065552,4.91273975372314 -"Sample_1_GCGCAACCACCCAGTG",1.3683660030365,-8.18411445617676 -"Sample_1_GCGCAACTCCTATGTT",2.05239844322205,7.65978288650513 -"Sample_1_GCGCAACTCTTGCATT",0.621173620223999,-7.6070671081543 -"Sample_1_GCGCAGTTCCGTCATC",-0.41158390045166,-0.153892979025841 -"Sample_1_GCGCCAACACCGTTGG",-3.94150376319885,8.99466133117676 -"Sample_1_GCGCGATTCCGTAGTA",2.8406445980072,-8.92532444000244 -"Sample_1_GCGGGTTGTGCGCTTG",-0.924243748188019,5.06863498687744 -"Sample_1_GCTCCTAGTAGAAGGA",-0.274944663047791,0.737953662872314 -"Sample_1_GCTCTGTCAAGTCTAC",-0.24533349275589,-9.85272216796875 -"Sample_1_GCTGCAGAGTGGGATC",3.90335202217102,7.29629278182983 -"Sample_1_GCTGCAGCACCACCAG",1.15303874015808,-10.8980417251587 -"Sample_1_GCTGCAGGTCGATTGT",0.839805424213409,-10.0366201400757 -"Sample_1_GCTGCAGTCCTTTACA",1.97093594074249,2.22766900062561 -"Sample_1_GCTGCAGTCGGAGGTA",3.85833120346069,-8.80115032196045 -"Sample_1_GCTGCGAAGGCTACGA",-0.551234364509583,-0.121521957218647 -"Sample_1_GCTGCGAGTGTCAATC",-0.301801383495331,9.10567188262939 -"Sample_1_GCTGCGATCTGATACG",0.585711479187012,-9.06491184234619 -"Sample_1_GCTGGGTCATATGCTG",-0.167133241891861,-10.2117414474487 -"Sample_1_GCTTCCACACATCCGG",4.97530794143677,4.74355840682983 -"Sample_1_GCTTCCACATCTATGG",-0.833450794219971,-9.52284812927246 -"Sample_1_GCTTCCATCTCTGTCG",0.49830573797226,4.52252912521362 -"Sample_1_GCTTGAACAGTATAAG",4.07127618789673,6.22015476226807 -"Sample_1_GCTTGAATCTAACCGA",0.0995322689414024,-8.19691753387451 -"Sample_1_GGAAAGCTCCTTGCCA",1.77435982227325,-8.22579479217529 -"Sample_1_GGAACTTCAGATGGCA",3.8548309803009,4.24844980239868 -"Sample_1_GGAATAACAAATTGCC",4.2466139793396,-8.38707447052002 -"Sample_1_GGAATAACAGTTCATG",-2.76382303237915,8.49629497528076 -"Sample_1_GGACATTCACTTCGAA",2.88918328285217,-9.60821151733398 -"Sample_1_GGAGCAACAAGCGCTC",2.62432622909546,6.51960515975952 -"Sample_1_GGAGCAACATCTCCCA",-0.0781831219792366,6.06057548522949 -"Sample_1_GGAGCAATCGCATGGC",-0.415369153022766,-6.98866748809814 -"Sample_1_GGAGCAATCGTAGATC",-0.213849201798439,-11.1357717514038 -"Sample_1_GGATGTTAGTCCGTAT",-2.71616840362549,8.0947847366333 -"Sample_1_GGATTACCATTGAGCT",-0.00959466397762299,-9.83193206787109 -"Sample_1_GGATTACTCACCAGGC",0.0195837784558535,5.12490224838257 -"Sample_1_GGCAATTAGAGGTAGA",3.55588412284851,4.14010095596313 -"Sample_1_GGCAATTAGTGTTGAA",-1.62776207923889,1.25527679920197 -"Sample_1_GGCAATTCAGATTGCT",-0.650635421276093,3.80666255950928 -"Sample_1_GGCAATTGTCAAACTC",1.12907147407532,-9.06212615966797 -"Sample_1_GGCAATTGTGATAAGT",4.46939086914062,4.07049417495728 -"Sample_1_GGCCGATGTTTCCACC",2.21034383773804,-10.815357208252 -"Sample_1_GGCGACTCATCCTAGA",1.00149989128113,-8.86007308959961 -"Sample_1_GGCGACTCATCCTTGC",-0.086730919778347,6.56266164779663 -"Sample_1_GGCGTGTCACATGACT",1.4458087682724,-8.99271869659424 -"Sample_1_GGGAATGAGATGGCGT",-1.99444055557251,9.0369234085083 -"Sample_1_GGGAATGGTCATCCCT",-1.42071139812469,8.02858638763428 -"Sample_1_GGGACCTTCTTGGGTA",-0.297968000173569,-0.169596880674362 -"Sample_1_GGGAGATAGTGTTTGC",-0.943174183368683,8.66666221618652 -"Sample_1_GGGAGATCAGATGGCA",0.432471334934235,4.46840381622314 -"Sample_1_GGGAGATCATACCATG",-1.81196558475494,7.86424541473389 -"Sample_1_GGGAGATGTCAAACTC",4.35870504379272,-9.81123352050781 -"Sample_1_GGGATGAGTCGTCTTC",3.31418943405151,6.1149525642395 -"Sample_1_GGGATGATCAGTTCGA",1.49927914142609,6.2656192779541 -"Sample_1_GGGCACTAGCAGGTCA",3.70309233665466,-8.38417911529541 -"Sample_1_GGGCACTCAAAGCAAT",-0.917432487010956,3.94810175895691 -"Sample_1_GGGCACTTCCTAGGGC",-1.72339963912964,8.80133819580078 -"Sample_1_GGGCACTTCTATCGCC",0.794236779212952,-9.22216606140137 -"Sample_1_GGGCATCCACGGCGTT",0.523612499237061,-9.20165252685547 -"Sample_1_GGGCATCCATCACAAC",0.126509308815002,3.50232219696045 -"Sample_1_GGGCATCGTTCCTCCA",5.15055227279663,5.61600017547607 -"Sample_1_GGGCATCTCCGCATCT",2.44237637519836,5.54469442367554 -"Sample_1_GGTATTGGTGAGGGTT",-1.89470827579498,6.66817665100098 -"Sample_1_GGTATTGGTTCCCGAG",0.467153906822205,3.69567680358887 -"Sample_1_GGTGAAGAGAGGTAGA",1.0671169757843,2.13026976585388 -"Sample_1_GGTGAAGTCCATGAGT",3.94793105125427,6.30688762664795 -"Sample_1_GGTGAAGTCCTCATTA",2.28394198417664,-8.18015956878662 -"Sample_1_GGTGCGTAGAGTACAT",1.31479394435883,-10.6336231231689 -"Sample_1_GGTGCGTAGTACATGA",0.000618312740698457,5.58661413192749 -"Sample_1_GGTGTTAGTAGTAGTA",-0.9777010679245,0.241243064403534 -"Sample_1_GTAACGTAGGACAGCT",2.71862125396729,5.48501348495483 -"Sample_1_GTAACGTTCACTTACT",2.46418857574463,4.88584899902344 -"Sample_1_GTAACTGCATCGGTTA",3.43324613571167,-8.33129024505615 -"Sample_1_GTAACTGTCCGAACGC",-0.600250899791718,-7.25049495697021 -"Sample_1_GTACGTAAGACACTAA",-0.762470722198486,-6.92325448989868 -"Sample_1_GTACGTACATGCTGGC",2.95901250839233,5.1428108215332 -"Sample_1_GTACGTATCCTGTAGA",-3.03267621994019,8.2058572769165 -"Sample_1_GTACTTTGTGTCAATC",-0.377443730831146,8.26597881317139 -"Sample_1_GTAGGCCAGCGCTCCA",2.83828735351562,7.73592042922974 -"Sample_1_GTAGGCCGTAACGTTC",-0.085719883441925,-9.89418888092041 -"Sample_1_GTATCTTTCCTATGTT",2.58372282981873,7.10137033462524 -"Sample_1_GTATTCTCAGCTGCTG",3.51512503623962,6.91034078598022 -"Sample_1_GTATTCTGTCCCTACT",0.819347321987152,2.55777430534363 -"Sample_1_GTATTCTTCCAATGGT",3.17723178863525,5.15378618240356 -"Sample_1_GTCACAAGTCCGAAGA",-1.10143232345581,8.11679267883301 -"Sample_1_GTCACAAGTTCCACAA",-0.00161242228932679,-8.44725322723389 -"Sample_1_GTCACGGCACCGGAAA",-1.85790908336639,7.6867299079895 -"Sample_1_GTCACGGGTTGCCTCT",5.13865041732788,5.92956209182739 -"Sample_1_GTCACGGTCAAGGCTT",-1.69250202178955,1.36277711391449 -"Sample_1_GTCATTTAGCTTTGGT",0.913913488388062,-9.86781406402588 -"Sample_1_GTCATTTTCAGTTTGG",3.2378990650177,6.07441854476929 -"Sample_1_GTCCTCAAGTATCGAA",-1.88364386558533,9.17751693725586 -"Sample_1_GTCCTCAAGTGCCATT",4.55810070037842,5.94284296035767 -"Sample_1_GTCGGGTGTCCTCTTG",3.48605370521545,7.67852401733398 -"Sample_1_GTCGTAATCCATGAGT",0.712278187274933,3.15098786354065 -"Sample_1_GTCTCGTAGTTCGCAT",-2.62067914009094,8.04558563232422 -"Sample_1_GTCTTCGGTATAAACG",3.36765074729919,3.47876286506653 -"Sample_1_GTGAAGGAGTACGACG",1.64230263233185,-9.28687381744385 -"Sample_1_GTGAAGGCAATAACGA",1.18194985389709,-8.54368877410889 -"Sample_1_GTGAAGGCATGTTGAC",3.65819811820984,7.12100124359131 -"Sample_1_GTGAAGGGTCCTCTTG",1.01911449432373,4.56774282455444 -"Sample_1_GTGAAGGTCATGTAGC",1.88991320133209,-4.98144006729126 -"Sample_1_GTGCAGCAGCGATTCT",1.97823536396027,8.21100234985352 -"Sample_1_GTGCAGCAGGCTACGA",4.96904230117798,4.73781299591064 -"Sample_1_GTGCAGCTCAAAGACA",-2.11408162117004,7.25128841400146 -"Sample_1_GTGCATACATCCGGGT",2.59401774406433,-10.0361862182617 -"Sample_1_GTGCATATCACCGTAA",1.41646754741669,-7.00791072845459 -"Sample_1_GTGCATATCATTATCC",2.34949779510498,-9.26599597930908 -"Sample_1_GTGCGGTAGAGGTACC",3.4325692653656,7.27686643600464 -"Sample_1_GTGCGGTCAAGCCGTC",-0.0261060874909163,-8.94536685943604 -"Sample_1_GTGCGGTCACATTCGA",-2.58474707603455,7.86465454101562 -"Sample_1_GTGCTTCAGCCAACAG",2.04113745689392,-7.75461864471436 -"Sample_1_GTGGGTCTCAAGAAGT",-0.143787667155266,0.184897065162659 -"Sample_1_GTGTGCGTCGGTGTCG",1.89520585536957,7.89379787445068 -"Sample_1_GTGTTAGCATCCTAGA",2.98713135719299,-9.54645729064941 -"Sample_1_GTGTTAGGTAGATTAG",3.19328880310059,6.71580076217651 -"Sample_1_GTTACAGCATTAGGCT",-2.07037782669067,8.11500644683838 -"Sample_1_GTTCATTCATGCATGT",3.05845189094543,-10.6843824386597 -"Sample_1_GTTCATTTCCAAACAC",-1.46215522289276,7.51512670516968 -"Sample_1_GTTCTCGTCTAGCACA",1.64257752895355,-9.90656185150146 -"Sample_1_GTTTCTAAGATTACCC",-0.856835722923279,4.50320482254028 -"Sample_1_GTTTCTAAGGTGCAAC",-0.79082852602005,6.44924545288086 -"Sample_1_GTTTCTACATCACGAT",-1.2471718788147,8.7328052520752 -"Sample_1_TAAACCGAGTTCGATC",4.17689800262451,-8.61411666870117 -"Sample_1_TAAACCGGTCTGCGGT",2.21265935897827,6.44423198699951 -"Sample_1_TAAACCGGTTCGCGAC",4.26833820343018,5.61408281326294 -"Sample_1_TAAGAGAGTACTTAGC",-0.231787458062172,-9.1062707901001 -"Sample_1_TAAGAGATCCGAAGAG",-2.22437620162964,8.64959335327148 -"Sample_1_TAAGCGTAGGACTGGT",1.11238598823547,-7.42065334320068 -"Sample_1_TAAGTGCCAGACAAAT",3.06007885932922,-10.8409214019775 -"Sample_1_TAAGTGCTCGCCATAA",1.33711624145508,-8.89894008636475 -"Sample_1_TACACGAGTCACTTCC",2.62036275863647,-10.6512508392334 -"Sample_1_TACACGAGTTTCGCTC",-3.44797801971436,8.82529067993164 -"Sample_1_TACACGATCCCAGGTG",0.328533351421356,1.93988871574402 -"Sample_1_TACCTATGTCCAACTA",1.79840457439423,8.07206153869629 -"Sample_1_TACCTTAAGCTAACAA",2.00481867790222,-10.1567783355713 -"Sample_1_TACCTTATCTGGCGTG",3.44766330718994,-7.34216642379761 -"Sample_1_TACGGGCCATCCTAGA",-0.773902714252472,6.37834692001343 -"Sample_1_TACGGGCCATTACCTT",-0.873016655445099,3.12933731079102 -"Sample_1_TACGGTAAGGATGGAA",2.57178568840027,6.06211090087891 -"Sample_1_TACTCATTCGGCCGAT",-1.07941901683807,1.96940577030182 -"Sample_1_TACTCGCCAACTGCGC",0.12674917280674,-9.52519512176514 -"Sample_1_TACTCGCTCCGAATGT",-1.46638655662537,9.37839794158936 -"Sample_1_TACTCGCTCGTAGGTT",-0.709179699420929,7.927161693573 -"Sample_1_TACTTACTCCGTTGTC",0.193615168333054,-11.0330762863159 -"Sample_1_TACTTGTGTACTTAGC",0.547445058822632,-8.65194034576416 -"Sample_1_TAGACCAAGAGAGCTC",-0.923305094242096,9.42425537109375 -"Sample_1_TAGACCACAGTCCTTC",-0.254191100597382,7.69011116027832 -"Sample_1_TAGCCGGCAATGTTGC",2.23692178726196,5.39970970153809 -"Sample_1_TAGCCGGTCGAGAACG",0.670314967632294,2.79208374023438 -"Sample_1_TAGGCATCAGTCCTTC",-0.212992161512375,4.94781446456909 -"Sample_1_TAGGCATGTTGTACAC",-1.56503427028656,9.72060108184814 -"Sample_1_TAGTGGTCAAGGGTCA",1.79028570652008,-9.33463764190674 -"Sample_1_TAGTGGTTCAATACCG",-2.43932199478149,7.5512843132019 -"Sample_1_TAGTGGTTCCTAGTGA",4.62346315383911,6.04124069213867 -"Sample_1_TAGTTGGAGTAGGCCA",1.70250725746155,-9.2817325592041 -"Sample_1_TATCAGGCAACTGCTA",1.90206789970398,-8.08635997772217 -"Sample_1_TATCAGGGTCGGATCC",2.33634972572327,5.65392637252808 -"Sample_1_TATCTCAAGACTTGAA",2.89245080947876,-8.42257881164551 -"Sample_1_TATCTCAAGGGAGTAA",1.15609526634216,-9.3127908706665 -"Sample_1_TATCTCAAGGTCATCT",-0.0732176303863525,4.14719820022583 -"Sample_1_TATCTCAGTCTACCTC",0.861790955066681,3.72557926177979 -"Sample_1_TATCTCATCATGTAGC",4.74549865722656,5.55421447753906 -"Sample_1_TATGCCCCAAGCTGTT",4.57893753051758,5.42203712463379 -"Sample_1_TATGCCCCAATCACAC",-1.75137376785278,8.93626308441162 -"Sample_1_TATTACCCAAACCCAT",-1.08634078502655,9.86554718017578 -"Sample_1_TATTACCCACGAAAGC",-3.17634963989258,7.99935531616211 -"Sample_1_TATTACCGTGATGTGG",-0.757175803184509,-10.6268272399902 -"Sample_1_TCAACGACAATCTACG",0.729838252067566,-9.01301383972168 -"Sample_1_TCAACGATCCTTTCGG",-0.119923010468483,1.73784840106964 -"Sample_1_TCAATCTGTGCAGACA",-0.817590773105621,0.849547564983368 -"Sample_1_TCAATCTGTGCGATAG",3.12782597541809,6.89581727981567 -"Sample_1_TCACAAGCAATCGAAA",3.11376762390137,-9.75539970397949 -"Sample_1_TCACAAGGTCTCATCC",-0.939577758312225,-9.22567844390869 -"Sample_1_TCACGAACATACTACG",-0.983627140522003,0.339897841215134 -"Sample_1_TCACGAAGTAGAAAGG",-0.174675762653351,0.925788164138794 -"Sample_1_TCACGAATCATCGATG",3.16532158851624,5.26398658752441 -"Sample_1_TCACGAATCGTCACGG",3.10678124427795,5.26355648040771 -"Sample_1_TCAGATGAGTTGAGTA",-0.851998031139374,6.84454441070557 -"Sample_1_TCAGCAAGTGCTTCTC",4.46320247650146,6.54095649719238 -"Sample_1_TCAGCTCAGACAGGCT",0.473435878753662,3.07445621490479 -"Sample_1_TCAGCTCGTCGAGTTT",3.46569585800171,7.70187616348267 -"Sample_1_TCAGGATTCATTTGGG",3.57357001304626,5.51153659820557 -"Sample_1_TCAGGTAAGTCCGGTC",-1.08828246593475,3.27308392524719 -"Sample_1_TCAGGTACAGGATTGG",-1.3678594827652,1.09354650974274 -"Sample_1_TCAGGTAGTCCGTCAG",1.78806042671204,-6.79833126068115 -"Sample_1_TCAGGTATCGAGGTAG",2.78911685943604,-9.50050926208496 -"Sample_1_TCAGGTATCGCCTGTT",0.186782777309418,5.83754014968872 -"Sample_1_TCATTACCAGTATAAG",3.80675768852234,-9.31516075134277 -"Sample_1_TCATTTGAGGCAATTA",3.26984238624573,7.00719356536865 -"Sample_1_TCATTTGAGTGGTAGC",-1.83511877059937,0.763606548309326 -"Sample_1_TCATTTGGTGTTGAGG",-0.40348955988884,0.527018845081329 -"Sample_1_TCCACACAGTACACCT",-2.07267808914185,7.43482255935669 -"Sample_1_TCCACACCAGTTTACG",1.34894216060638,-10.5973358154297 -"Sample_1_TCCCGATAGACAGGCT",-1.52165865898132,8.51216793060303 -"Sample_1_TCCCGATCAAGTTCTG",1.08454620838165,-8.11517524719238 -"Sample_1_TCCCGATTCGTAGATC",-0.574307441711426,6.86248254776001 -"Sample_1_TCGAGGCGTAGAAGGA",-1.67881655693054,5.5960693359375 -"Sample_1_TCGAGGCGTTTACTCT",2.77362060546875,-7.10477542877197 -"Sample_1_TCGAGGCTCCTCAACC",3.32093286514282,4.53740787506104 -"Sample_1_TCGAGGCTCGTTGACA",2.26038861274719,-9.09016132354736 -"Sample_1_TCGCGAGGTCAGTGGA",0.826019704341888,2.77624607086182 -"Sample_1_TCGGGACCAGACGTAG",2.47859764099121,7.33025217056274 -"Sample_1_TCGGGACGTGATAAGT",0.549019038677216,-8.88973331451416 -"Sample_1_TCGGGACTCACCCGAG",-1.4236661195755,7.81136608123779 -"Sample_1_TCGGGACTCCGAAGAG",1.04633367061615,-9.42446517944336 -"Sample_1_TCGGTAACACAGGCCT",2.70883774757385,5.68682098388672 -"Sample_1_TCGGTAACATTAACCG",2.62944936752319,-7.42343044281006 -"Sample_1_TCGTACCAGATCTGCT",2.68780517578125,4.87401533126831 -"Sample_1_TCGTACCGTTTGACAC",0.988836824893951,-8.22246360778809 -"Sample_1_TCGTACCTCAAACCGT",2.91508603096008,-6.93784999847412 -"Sample_1_TCGTACCTCCCGACTT",3.98005962371826,4.68684387207031 -"Sample_1_TCGTAGACATCTATGG",2.33761858940125,-9.18652248382568 -"Sample_1_TCTATTGAGGCGACAT",3.19004487991333,7.47237157821655 -"Sample_1_TCTATTGGTAGCTCCG",2.08007287979126,8.28045749664307 -"Sample_1_TCTCATAAGACTTTCG",-0.0148756196722388,0.413755983114243 -"Sample_1_TCTCTAATCCGTAGTA",0.356642127037048,3.3492259979248 -"Sample_1_TCTGAGAAGAGTGAGA",0.167540058493614,-10.5540132522583 -"Sample_1_TCTGAGACACCACGTG",-0.394235789775848,0.657951951026917 -"Sample_1_TCTGAGACAGATCTGT",2.25200581550598,4.95615243911743 -"Sample_1_TCTGGAAAGTACGTTC",2.3894190788269,7.75733852386475 -"Sample_1_TCTTCGGAGGGATGGG",3.40541625022888,7.54967021942139 -"Sample_1_TCTTTCCAGACTGGGT",2.91451907157898,-8.84024524688721 -"Sample_1_TCTTTCCGTGCTTCTC",4.45303058624268,5.40031242370605 -"Sample_1_TGAAAGATCCCTAATT",0.408669084310532,-11.5035591125488 -"Sample_1_TGAAAGATCGTTGCCT",-1.74003314971924,0.677280843257904 -"Sample_1_TGACAACTCCTACAGA",4.10102844238281,-9.4396858215332 -"Sample_1_TGACTTTCAATGGATA",0.185648918151855,2.33132433891296 -"Sample_1_TGACTTTTCTAGCACA",4.6494517326355,6.21598196029663 -"Sample_1_TGAGAGGAGCAATCTC",3.26755475997925,-10.1487407684326 -"Sample_1_TGAGCATGTCAAAGCG",-0.318619072437286,3.96842837333679 -"Sample_1_TGAGCCGGTGCCTTGG",0.380860716104507,-11.3137044906616 -"Sample_1_TGAGGGAAGCATGGCA",-2.01061582565308,7.04821109771729 -"Sample_1_TGAGGGACATGGGAAC",-0.402147501707077,4.38231039047241 -"Sample_1_TGAGGGATCACATGCA",3.75300097465515,5.03439044952393 -"Sample_1_TGATTTCTCCTTCAAT",0.907620906829834,-8.50947666168213 -"Sample_1_TGCACCTAGGGCACTA",1.94379246234894,-9.66688251495361 -"Sample_1_TGCCCATCAGTAAGCG",2.71397686004639,4.52637004852295 -"Sample_1_TGCCCTAGTTCAACCA",0.544191718101501,-8.1515588760376 -"Sample_1_TGCCCTATCAGGCAAG",-0.381988912820816,7.94350624084473 -"Sample_1_TGCGGGTAGACCTAGG",3.11841130256653,5.89002752304077 -"Sample_1_TGCGGGTGTTATGTGC",1.86625289916992,-4.57099962234497 -"Sample_1_TGCGTGGGTAATTGGA",2.65090322494507,-7.9413366317749 -"Sample_1_TGCTACCAGCTCCCAG",-0.164288207888603,9.45863723754883 -"Sample_1_TGCTGCTGTCCAACTA",-0.170738756656647,5.44306707382202 -"Sample_1_TGGACGCAGGAGCGAG",-1.28355073928833,5.41598510742188 -"Sample_1_TGGACGCGTAACGCGA",3.76633501052856,7.44051599502563 -"Sample_1_TGGCGCAAGTACGCGA",3.62111139297485,6.60442638397217 -"Sample_1_TGGCGCAGTTCCCGAG",0.417125910520554,-8.17689323425293 -"Sample_1_TGGCGCATCCTACAGA",3.98059034347534,3.94790768623352 -"Sample_1_TGGGAAGAGGAACTGC",-2.67890954017639,8.92380428314209 -"Sample_1_TGGGAAGTCTATCCTA",-1.16108965873718,-8.17358207702637 -"Sample_1_TGGGCGTCAGTTAACC",2.37601184844971,7.94367790222168 -"Sample_1_TGGTTAGAGCGCCTTG",1.78255271911621,-9.43976020812988 -"Sample_1_TGGTTAGAGGAATTAC",0.202621400356293,3.16776537895203 -"Sample_1_TGGTTAGGTCAATACC",2.95137333869934,-7.68622827529907 -"Sample_1_TGGTTCCTCCACTCCA",-0.133145228028297,5.59178066253662 -"Sample_1_TGTATTCAGATGTCGG",0.559718906879425,4.71241235733032 -"Sample_1_TGTATTCCAGCGTCCA",2.47919487953186,-9.31047630310059 -"Sample_1_TGTATTCTCTGATACG",2.65503406524658,-9.29549503326416 -"Sample_1_TGTCCCAAGAGACTAT",4.23468255996704,-9.66808795928955 -"Sample_1_TGTCCCATCTTGTACT",2.05299663543701,-10.1142024993896 -"Sample_1_TGTGGTACATGCAACT",-1.71205174922943,9.08261585235596 -"Sample_1_TGTGGTAGTGCATCTA",-1.38025176525116,0.385601788759232 -"Sample_1_TGTGGTATCCCGACTT",3.94791197776794,4.88269948959351 -"Sample_1_TGTGTTTAGGCCCTCA",4.42033195495605,-8.75133609771729 -"Sample_1_TGTGTTTGTTGTGGAG",0.861023962497711,2.71898102760315 -"Sample_1_TGTGTTTTCATGCTCC",4.20694208145142,6.62960815429688 -"Sample_1_TGTTCCGAGCCACCTG",2.02483177185059,-7.66323566436768 -"Sample_1_TGTTCCGCAGTGACAG",2.15780735015869,-4.7347297668457 -"Sample_1_TTAGGACAGGGTCGAT",-1.73990225791931,1.03959047794342 -"Sample_1_TTAGGACGTGGTACAG",2.86032891273499,6.22198581695557 -"Sample_1_TTAGGCACATAAAGGT",3.91695690155029,7.83230876922607 -"Sample_1_TTAGTTCTCACTTACT",2.65172982215881,4.42386531829834 -"Sample_1_TTAGTTCTCTTGAGAC",4.26221132278442,5.4747896194458 -"Sample_1_TTATGCTTCACGAAGG",-0.320900499820709,9.24231719970703 -"Sample_1_TTCGGTCAGCCAGAAC",2.49619698524475,-11.1582651138306 -"Sample_1_TTCGGTCAGTTCCACA",-9.78505706787109,-2.88191485404968 -"Sample_1_TTCTACACATTACCTT",-0.912493407726288,0.903590738773346 -"Sample_1_TTCTACATCGCAAGCC",3.59308815002441,4.6816234588623 -"Sample_1_TTCTCAACACACTGCG",-0.525051534175873,4.81193590164185 -"Sample_1_TTCTCCTGTTCCCGAG",2.76335906982422,-11.0394191741943 -"Sample_1_TTCTCCTTCCGAATGT",3.08159613609314,5.49576997756958 -"Sample_1_TTCTTAGGTTTAGGAA",1.8193222284317,2.29203128814697 -"Sample_1_TTGAACGAGCCATCGC",1.70358169078827,-8.67632293701172 -"Sample_1_TTGAACGGTAGCTCCG",1.77607333660126,3.53138470649719 -"Sample_1_TTGAACGGTCTCAACA",0.216464161872864,0.14102204144001 -"Sample_1_TTGAACGTCAGTTCGA",-0.885632991790771,8.57219505310059 -"Sample_1_TTGACTTTCCCTAACC",3.38709950447083,-9.91372966766357 -"Sample_1_TTGCCGTCACCTTGTC",4.22200012207031,-8.53142833709717 -"Sample_1_TTGCCGTTCTTGCAAG",2.00515246391296,5.09844970703125 -"Sample_1_TTGCGTCCAGCCTTGG",1.36465454101562,3.23980307579041 -"Sample_1_TTGCGTCTCACGGTTA",-0.768053710460663,7.19588947296143 -"Sample_1_TTGCGTCTCCTATTCA",4.39566516876221,6.37975931167603 -"Sample_1_TTGCGTCTCTTCTGGC",-1.27155423164368,5.38261890411377 -"Sample_1_TTGGAACGTCCATCCT",-0.50926810503006,-7.16424179077148 -"Sample_1_TTGGCAAAGAGATGAG",3.38818669319153,-8.84610462188721 -"Sample_1_TTGGCAAGTATCACCA",4.30581283569336,5.23382425308228 -"Sample_1_TTGTAGGAGTCGTACT",4.05017566680908,4.9457688331604 -"Sample_1_TTTATGCAGCCACTAT",2.11655640602112,6.38171672821045 -"Sample_1_TTTATGCCAGTTAACC",4.27194786071777,6.75956535339355 -"Sample_1_TTTATGCGTACGACCC",4.51594161987305,-8.65060520172119 -"Sample_1_TTTGCGCAGGCTACGA",3.73736119270325,-10.3495903015137 -"Sample_1_TTTGCGCTCACTATTC",4.00344753265381,5.35648918151855 -"Sample_1_TTTGGTTTCTGTTTGT",-0.620654821395874,-0.052264254540205 -"Sample_1_TTTGTCACAATTGCTG",2.45908951759338,2.93657040596008 -"Sample_1_TTTGTCAGTAGGAGTC",-9.50566387176514,-2.59451222419739 -"Sample_1_TTTGTCATCCTCATTA",0.563555896282196,-11.2930526733398 -"Sample_2_AAACCTGTCTGCTGTC",-0.490078926086426,0.549113750457764 -"Sample_2_AAACGGGGTTTGCATG",-3.64654541015625,8.48738956451416 -"Sample_2_AAACGGGTCCTTTACA",4.32082033157349,5.82150983810425 -"Sample_2_AAAGTAGCAAACAACA",-2.330397605896,7.41575479507446 -"Sample_2_AAAGTAGCATAGAAAC",-2.8954746723175,7.01710414886475 -"Sample_2_AAATGCCCATGATCCA",2.59175968170166,6.68973636627197 -"Sample_2_AAATGCCGTTAGTGGG",-1.24871790409088,7.59334897994995 -"Sample_2_AAATGCCTCAGTTAGC",2.50271654129028,6.59390735626221 -"Sample_2_AACACGTGTCGCTTCT",2.89007759094238,6.81137943267822 -"Sample_2_AACCATGGTCTCACCT",-0.881959915161133,4.75376081466675 -"Sample_2_AACTCAGGTACCGTAT",1.61758422851562,-10.2320880889893 -"Sample_2_AACTCAGGTCCATCCT",-0.492989033460617,-8.98588466644287 -"Sample_2_AACTCAGGTCGCTTCT",3.44364976882935,7.31388521194458 -"Sample_2_AACTCAGTCCTTTCTC",1.84780240058899,-10.3979635238647 -"Sample_2_AACTCTTCAAGTAATG",2.95634984970093,-7.24510526657104 -"Sample_2_AACTCTTCACACATGT",-0.258985668420792,2.83517622947693 -"Sample_2_AACTCTTGTGCCTGTG",-3.85883975028992,8.86901473999023 -"Sample_2_AACTCTTTCAAACAAG",2.84009861946106,-10.5207099914551 -"Sample_2_AACTCTTTCCTCGCAT",-0.801875591278076,0.99469929933548 -"Sample_2_AACTGGTGTGCGGTAA",2.94190096855164,6.61568784713745 -"Sample_2_AACTTTCGTAGCGATG",2.36633157730103,-11.2723989486694 -"Sample_2_AAGACCTAGATGTCGG",4.63610363006592,6.65094566345215 -"Sample_2_AAGCCGCAGCTGAACG",2.95281982421875,-7.06207132339478 -"Sample_2_AAGCCGCGTTCGCGAC",1.90649282932281,-4.3629994392395 -"Sample_2_AAGGAGCCAATACGCT",2.27291035652161,8.33000564575195 -"Sample_2_AAGGCAGAGAGGTAGA",-0.313212305307388,7.13910865783691 -"Sample_2_AAGGCAGAGGAATCGC",-2.3313319683075,6.38703918457031 -"Sample_2_AAGGTTCGTCATATGC",-2.39826774597168,7.92004346847534 -"Sample_2_AAGGTTCTCAGTGCAT",2.75081443786621,6.81925868988037 -"Sample_2_AAGTCTGAGAGGTAGA",2.21746158599854,-4.61439085006714 -"Sample_2_AATCCAGCAAGCCCAC",1.52866888046265,-10.3515777587891 -"Sample_2_AATCCAGGTAAGCACG",-0.652664244174957,1.24966681003571 -"Sample_2_AATCCAGTCACAACGT",2.70746731758118,4.70324420928955 -"Sample_2_AATCGGTCATCACAAC",4.1713695526123,5.05643939971924 -"Sample_2_ACACCCTCAGCTGTGC",3.25817537307739,-9.19936370849609 -"Sample_2_ACACCCTCATACCATG",1.85876524448395,-7.0362982749939 -"Sample_2_ACACCCTGTCACAAGG",3.81666612625122,-8.16307163238525 -"Sample_2_ACACTGAAGCTGAACG",-1.80179393291473,6.01932048797607 -"Sample_2_ACACTGATCTTACCTA",1.49532723426819,-9.90207004547119 -"Sample_2_ACAGCCGAGCTACCGC",-0.299345016479492,-10.9248352050781 -"Sample_2_ACAGCTAAGGCAGTCA",0.0687426030635834,-8.87082958221436 -"Sample_2_ACATACGAGAGAACAG",2.02515482902527,5.26141357421875 -"Sample_2_ACATACGAGGCTAGAC",1.31729650497437,2.30223202705383 -"Sample_2_ACATACGTCAACCAAC",4.1568078994751,-9.93806457519531 -"Sample_2_ACATCAGAGGTCGGAT",1.74978625774384,-7.4006929397583 -"Sample_2_ACATCAGGTCCGTTAA",3.64074635505676,-9.10654163360596 -"Sample_2_ACATGGTGTCGCTTCT",4.60535192489624,6.80487251281738 -"Sample_2_ACCAGTACACAACTGT",-0.900677025318146,6.28268575668335 -"Sample_2_ACCAGTATCCTCGCAT",4.63266372680664,6.16399812698364 -"Sample_2_ACCAGTATCTGAAAGA",1.85046684741974,-7.70588684082031 -"Sample_2_ACCGTAACAAGAAGAG",-1.25323462486267,8.55003833770752 -"Sample_2_ACCGTAACATTGAGCT",0.523276209831238,4.48776054382324 -"Sample_2_ACCGTAATCGCTTGTC",4.93847465515137,6.10134792327881 -"Sample_2_ACCTTTAAGCGATATA",-1.60482251644135,7.89704322814941 -"Sample_2_ACGAGCCAGACCTAGG",-1.7933337688446,9.19381523132324 -"Sample_2_ACGAGCCGTCTAGTCA",-1.04017198085785,-9.5222864151001 -"Sample_2_ACGATACAGAGACGAA",3.57528924942017,5.17307281494141 -"Sample_2_ACGATACCAATACGCT",4.17659568786621,-9.83602905273438 -"Sample_2_ACGATACGTCTGCCAG",-0.0625262856483459,9.14486885070801 -"Sample_2_ACGATGTAGAGCTTCT",3.00420713424683,4.93508291244507 -"Sample_2_ACGATGTCATTCACTT",3.05492854118347,6.09268951416016 -"Sample_2_ACGATGTGTCATATCG",-0.184129402041435,4.12423133850098 -"Sample_2_ACGCAGCCAAGCGCTC",3.84883570671082,6.03940296173096 -"Sample_2_ACGCCGAAGGTGTTAA",2.98343276977539,-9.8183012008667 -"Sample_2_ACGGAGAAGACAGAGA",-1.15667343139648,5.1014256477356 -"Sample_2_ACGGCCAGTTGATTCG",-1.20367753505707,1.22011935710907 -"Sample_2_ACGTCAAAGGTAGCTG",2.15204977989197,-9.13882446289062 -"Sample_2_ACGTCAATCGGAGCAA",3.88373327255249,-9.93347835540771 -"Sample_2_ACGTCAATCGGCATCG",3.54095673561096,6.88327932357788 -"Sample_2_ACTATCTAGCTACCTA",1.17048394680023,-9.69287014007568 -"Sample_2_ACTGAACGTCAATGTC",-1.05541348457336,8.75903034210205 -"Sample_2_ACTGAACTCCCAACGG",-1.58664488792419,9.07959938049316 -"Sample_2_ACTGCTCCATTAGCCA",3.75638747215271,-9.08351135253906 -"Sample_2_ACTGTCCAGAAGAAGC",3.23553967475891,3.52504372596741 -"Sample_2_ACTGTCCGTACCGCTG",3.05354285240173,5.32069110870361 -"Sample_2_ACTTACTAGCCGGTAA",-0.647111356258392,6.39336824417114 -"Sample_2_ACTTACTGTACCGGCT",3.15500378608704,-10.5710678100586 -"Sample_2_ACTTGTTCAGATGGCA",3.12691426277161,-8.43655776977539 -"Sample_2_ACTTGTTTCACGATGT",1.8674328327179,8.36542320251465 -"Sample_2_ACTTTCACACAGTCGC",-1.37780356407166,9.32563877105713 -"Sample_2_ACTTTCAGTAAGGATT",3.40812635421753,3.73725628852844 -"Sample_2_ACTTTCATCAGTTGAC",-3.07826089859009,7.76824903488159 -"Sample_2_AGAATAGTCAACACCA",0.513394296169281,-9.85814666748047 -"Sample_2_AGAGCGAAGGTCATCT",2.38384199142456,7.48771047592163 -"Sample_2_AGAGCGAGTACCGGCT",2.55985856056213,-9.12986660003662 -"Sample_2_AGAGCTTAGGAGTTTA",0.757569968700409,-9.38083553314209 -"Sample_2_AGAGCTTTCAAGAAGT",2.1169228553772,-7.37352514266968 -"Sample_2_AGAGTGGTCCAGTAGT",-2.06863594055176,9.15886116027832 -"Sample_2_AGAGTGGTCCTTGACC",-2.16603922843933,6.62261199951172 -"Sample_2_AGCAGCCAGTTCCACA",-1.39186799526215,6.81519460678101 -"Sample_2_AGCCTAACAGTCTTCC",4.19441938400269,-8.50279903411865 -"Sample_2_AGCGTATAGTGCAAGC",4.76222133636475,6.38078451156616 -"Sample_2_AGCGTATGTGACGGTA",3.50631380081177,-8.90280151367188 -"Sample_2_AGCGTCGCAGCGAACA",2.0508885383606,7.84936046600342 -"Sample_2_AGCTCCTTCAGAGCTT",0.265067994594574,-11.4641981124878 -"Sample_2_AGCTCTCCATTTGCCC",2.25950384140015,-4.79206371307373 -"Sample_2_AGCTTGAAGACTAGAT",2.37022662162781,-8.43184757232666 -"Sample_2_AGCTTGAGTCTCACCT",-3.15956044197083,8.38435173034668 -"Sample_2_AGGCCACCAAGCGAGT",4.69372797012329,-8.89098834991455 -"Sample_2_AGGCCACCAAGTAATG",4.4335298538208,-9.27685546875 -"Sample_2_AGGCCACCACGGCTAC",0.602977216243744,2.32114458084106 -"Sample_2_AGGCCACTCTCTTATG",1.45615410804749,-10.2027721405029 -"Sample_2_AGGCCGTGTAGAGGAA",2.72038602828979,5.77423143386841 -"Sample_2_AGGCCGTTCATCGATG",5.02051305770874,6.81888341903687 -"Sample_2_AGGGATGGTCTCAACA",-0.449371218681335,-0.02501873485744 -"Sample_2_AGGGTGACAGTAAGAT",2.6294264793396,-9.95604705810547 -"Sample_2_AGGTCATCACAGACTT",4.32317161560059,-9.59361457824707 -"Sample_2_AGGTCATTCGAGAGCA",0.130787417292595,-8.65794658660889 -"Sample_2_AGGTCCGCAGCTGTGC",2.16333436965942,-4.60969543457031 -"Sample_2_AGTAGTCTCACTCCTG",1.30030786991119,4.48664236068726 -"Sample_2_AGTCTTTAGCCTCGTG",-1.12294936180115,2.37903666496277 -"Sample_2_AGTCTTTAGGATTCGG",-1.00004613399506,4.97402715682983 -"Sample_2_AGTGAGGAGGCTCTTA",1.1366765499115,-9.53948593139648 -"Sample_2_AGTGAGGGTGCACCAC",2.06195974349976,8.08732986450195 -"Sample_2_AGTGAGGTCTGATACG",0.80098021030426,3.20799517631531 -"Sample_2_AGTGTCATCCTTTCGG",-0.828728377819061,2.52179622650146 -"Sample_2_AGTGTCATCTGGGCCA",-1.13049411773682,-8.10165977478027 -"Sample_2_AGTGTCATCTTACCTA",-0.507210373878479,0.763081014156342 -"Sample_2_AGTTGGTTCGTCCAGG",4.27675294876099,5.66985416412354 -"Sample_2_ATAACGCAGTGGCACA",2.4228208065033,-4.83216571807861 -"Sample_2_ATAACGCCAGTCAGAG",0.230245217680931,1.97227597236633 -"Sample_2_ATAACGCGTAGCTTGT",-2.02874135971069,8.3585786819458 -"Sample_2_ATAAGAGGTGCCTGGT",3.53997111320496,-9.68237590789795 -"Sample_2_ATAGACCTCTTTACGT",3.13179802894592,-9.79790878295898 -"Sample_2_ATCACGAAGTGGAGTC",-0.268428832292557,3.73414397239685 -"Sample_2_ATCACGATCAGCAACT",-2.93929743766785,7.16215181350708 -"Sample_2_ATCATCTCAACACGCC",-2.7608847618103,6.73698854446411 -"Sample_2_ATCATGGAGGTGATAT",-0.58057051897049,-10.2264251708984 -"Sample_2_ATCATGGCAGCCAGAA",-9.70353317260742,-2.79446578025818 -"Sample_2_ATCCACCAGTAGATGT",-0.844080448150635,-9.38298416137695 -"Sample_2_ATCCACCCATCGATGT",-9.6027193069458,-2.69187784194946 -"Sample_2_ATCCACCGTAACGACG",1.6488618850708,3.19818305969238 -"Sample_2_ATCCGAAAGAAGATTC",1.16246068477631,-10.9371213912964 -"Sample_2_ATCCGAACACTAGTAC",-0.298514664173126,-10.2400493621826 -"Sample_2_ATCCGAAGTACACCGC",1.47890138626099,-8.03507423400879 -"Sample_2_ATCGAGTCACAACTGT",3.62979173660278,6.66318655014038 -"Sample_2_ATCTACTCACGAGGTA",0.35524770617485,-9.78182792663574 -"Sample_2_ATCTACTCATGGGACA",0.289614409208298,6.48829364776611 -"Sample_2_ATCTACTTCGGCCGAT",-1.16056311130524,8.30403614044189 -"Sample_2_ATCTGCCAGCGCTCCA",2.50581169128418,6.12142324447632 -"Sample_2_ATCTGCCAGTGACATA",3.3936619758606,-8.75055027008057 -"Sample_2_ATCTGCCAGTTGAGTA",-0.54170298576355,-9.81553554534912 -"Sample_2_ATCTGCCCACTTAACG",2.13407731056213,5.65158319473267 -"Sample_2_ATCTGCCCATCCCACT",1.33990383148193,-7.68851804733276 -"Sample_2_ATCTGCCGTCTCTTTA",-0.649528920650482,2.95045685768127 -"Sample_2_ATCTGCCGTTAGTGGG",1.18494427204132,8.26185035705566 -"Sample_2_ATCTGCCTCGCTTAGA",0.416380047798157,4.09205341339111 -"Sample_2_ATGAGGGGTACCGTAT",-1.38384568691254,0.138669610023499 -"Sample_2_ATGCGATGTTCTGTTT",3.50140786170959,5.31016731262207 -"Sample_2_ATGGGAGAGTTGTAGA",3.06533694267273,-7.0898585319519 -"Sample_2_ATGTGTGAGCATCATC",1.23980057239532,-10.3855648040771 -"Sample_2_ATGTGTGAGGTCATCT",0.677724361419678,-8.79709243774414 -"Sample_2_ATGTGTGGTGTATGGG",3.46640658378601,5.14710903167725 -"Sample_2_ATTACTCAGGTCATCT",-0.416295945644379,-10.8151025772095 -"Sample_2_ATTACTCGTGGAAAGA",1.73413217067719,-4.04146242141724 -"Sample_2_ATTATCCAGTAGCGGT",0.121281199157238,-10.4840250015259 -"Sample_2_ATTATCCCACCCATGG",5.0090708732605,6.09070730209351 -"Sample_2_ATTCTACCAAGCGATG",-0.306306272745132,3.73010683059692 -"Sample_2_ATTCTACCATTAACCG",0.931414842605591,5.88514566421509 -"Sample_2_ATTCTACGTCTAGTCA",0.955424070358276,-7.98334217071533 -"Sample_2_ATTCTACGTGGTAACG",-1.66443777084351,0.242027014493942 -"Sample_2_ATTGGACCAGCGTTCG",-1.63098180294037,0.83291083574295 -"Sample_2_ATTGGACCAGCTTCGG",1.51930618286133,2.5130558013916 -"Sample_2_ATTGGACGTAGCGTGA",-2.35958409309387,8.86671257019043 -"Sample_2_ATTGGACGTCGCATCG",-0.466469705104828,-7.53118848800659 -"Sample_2_ATTGGTGAGCCCGAAA",3.87886953353882,7.92998504638672 -"Sample_2_CAACCAATCCTTGGTC",1.78140270709991,-10.9876022338867 -"Sample_2_CAACTAGCATGGTAGG",0.523173570632935,-11.2812299728394 -"Sample_2_CAACTAGTCGCATGGC",3.78208899497986,4.38339757919312 -"Sample_2_CAAGAAAAGACTAAGT",-0.182187020778656,-9.82359313964844 -"Sample_2_CAAGAAAAGTTAGCGG",-0.92163747549057,-9.37548828125 -"Sample_2_CAAGAAATCTATGTGG",2.23352384567261,-8.80153369903564 -"Sample_2_CAAGATCGTTACTGAC",-0.626469492912292,0.431938767433167 -"Sample_2_CAAGATCGTTCAACCA",3.90511727333069,-8.92542839050293 -"Sample_2_CAAGATCGTTCAGCGC",3.08817005157471,7.45178985595703 -"Sample_2_CAAGATCGTTTAGGAA",-2.4654266834259,9.04079532623291 -"Sample_2_CAAGTTGGTAACGCGA",-2.1410870552063,6.00674247741699 -"Sample_2_CACAAACAGTTCGATC",3.46169996261597,4.51105213165283 -"Sample_2_CACACAACAATCACAC",-0.959655046463013,8.5671443939209 -"Sample_2_CACACAAGTCGCGTGT",-0.399586260318756,6.32531642913818 -"Sample_2_CACACCTCAAACCTAC",-0.102731004357338,-11.2875709533691 -"Sample_2_CACACCTTCCCATTTA",3.37370896339417,-9.82248687744141 -"Sample_2_CACACTCCAGTACACT",-0.0381368137896061,-10.5873222351074 -"Sample_2_CACACTCCATGTAAGA",-0.911188900470734,9.77788162231445 -"Sample_2_CACACTCTCTAACCGA",1.23326289653778,1.44244313240051 -"Sample_2_CACAGGCGTGGGTATG",1.8047137260437,-11.1056575775146 -"Sample_2_CACAGTAAGTGACATA",-1.67017483711243,0.933240592479706 -"Sample_2_CACATAGCAAGCCGCT",-2.40538668632507,8.72878837585449 -"Sample_2_CACATAGGTAGCAAAT",-0.120711632072926,-10.6269474029541 -"Sample_2_CACATAGTCGCGTTTC",-2.36183404922485,7.04131889343262 -"Sample_2_CACATTTCATTGCGGC",-0.322303056716919,7.45219469070435 -"Sample_2_CACATTTTCCGTCATC",-1.74980771541595,8.02418231964111 -"Sample_2_CACCACTCACTCGACG",0.116703920066357,1.80954802036285 -"Sample_2_CACCACTTCATACGGT",1.01820063591003,-8.42933368682861 -"Sample_2_CACCACTTCCGCAAGC",1.96131932735443,-9.52595615386963 -"Sample_2_CACCACTTCTTGAGAC",1.38165414333344,-8.03112411499023 -"Sample_2_CACCAGGCACCTCGTT",-1.76517975330353,0.34677591919899 -"Sample_2_CACCAGGTCAAGGTAA",1.85722279548645,-5.20342445373535 -"Sample_2_CACCTTGTCTCTAAGG",0.392473191022873,6.09053182601929 -"Sample_2_CAGAGAGCATCACGTA",-0.0161797590553761,5.82370042800903 -"Sample_2_CAGATCAAGATATGGT",4.78344917297363,5.24658536911011 -"Sample_2_CAGCAGCGTCGCCATG",0.388050585985184,8.674147605896 -"Sample_2_CAGCAGCTCGGAATCT",-9.71193981170654,-2.80500936508179 -"Sample_2_CAGCAGCTCTGTCCGT",4.25414705276489,6.67632484436035 -"Sample_2_CAGCCGAAGCGACGTA",1.77174460887909,-8.17306518554688 -"Sample_2_CAGCGACAGTAGCGGT",1.94938325881958,-7.45058870315552 -"Sample_2_CAGCTGGAGTGTACGG",2.83210229873657,-9.15941524505615 -"Sample_2_CAGCTGGTCTTTCCTC",0.813422977924347,-10.1450634002686 -"Sample_2_CAGTAACCATCTCGCT",-1.10551929473877,2.63267159461975 -"Sample_2_CAGTAACTCTGTCCGT",-0.453981071710587,9.04695606231689 -"Sample_2_CAGTCCTAGACAAGCC",1.22171974182129,-7.55849075317383 -"Sample_2_CAGTCCTAGATCTGCT",-2.43294644355774,8.37426471710205 -"Sample_2_CAGTCCTAGTAGATGT",4.30033540725708,4.6929178237915 -"Sample_2_CAGTCCTCAGGCGATA",3.48290491104126,6.6887354850769 -"Sample_2_CAGTCCTGTTCAGCGC",3.30033564567566,4.32956504821777 -"Sample_2_CAGTCCTTCTCTTGAT",3.40116262435913,4.92864513397217 -"Sample_2_CATATGGTCTGTGCAA",2.10639381408691,-4.86203098297119 -"Sample_2_CATATTCCAGTGACAG",-2.57765293121338,6.73637247085571 -"Sample_2_CATATTCGTAAATACG",2.4977343082428,8.13886165618896 -"Sample_2_CATCAAGAGGTGTTAA",0.0492724478244781,5.4917631149292 -"Sample_2_CATCAAGCACGACGAA",5.21133041381836,6.63512754440308 -"Sample_2_CATCAAGCAGGCAGTA",3.6913890838623,-9.57955932617188 -"Sample_2_CATCAAGCATGTTGAC",0.917635023593903,2.62550806999207 -"Sample_2_CATCAAGGTCTCCACT",3.85607004165649,-9.25901508331299 -"Sample_2_CATCAGAAGAAACCAT",-0.123688906431198,5.47343921661377 -"Sample_2_CATCAGACACCCTATC",3.18976974487305,5.59975624084473 -"Sample_2_CATCAGAGTCCATGAT",0.528202414512634,-8.88026428222656 -"Sample_2_CATCAGAGTCCGAAGA",-1.18055868148804,-0.112405799329281 -"Sample_2_CATCCACGTCCCTTGT",-0.163310363888741,5.15359449386597 -"Sample_2_CATCCACGTGCATCTA",0.182706698775291,6.56506109237671 -"Sample_2_CATCCACTCTGAGGGA",2.65286135673523,-8.9774055480957 -"Sample_2_CATCGAAAGGAATCGC",-1.2217024564743,7.03407144546509 -"Sample_2_CATCGAACAGGCTGAA",-2.79355835914612,7.46945524215698 -"Sample_2_CATCGAACAGGGAGAG",2.56699299812317,7.38778305053711 -"Sample_2_CATCGGGCAAAGGTGC",-0.0260573625564575,-11.3374099731445 -"Sample_2_CATGACAAGTGAACGC",1.77930700778961,-4.45737648010254 -"Sample_2_CATGACACACAGACTT",1.63493812084198,-10.5269365310669 -"Sample_2_CATGACACACCGTTGG",2.98568391799927,3.88332796096802 -"Sample_2_CATGACACAGCTATTG",3.24907469749451,-9.18910121917725 -"Sample_2_CATGACAGTCTCACCT",2.9936306476593,4.48337125778198 -"Sample_2_CATGCCTCAATGAAAC",1.06246817111969,-9.78233623504639 -"Sample_2_CATGGCGAGTGTGGCA",2.67200350761414,-6.86736392974854 -"Sample_2_CATGGCGCACGAGGTA",2.29739046096802,-8.17120361328125 -"Sample_2_CATGGCGGTACAGTGG",0.220493778586388,-8.48588466644287 -"Sample_2_CATTATCAGGGTTCCC",2.48880004882812,-10.6197299957275 -"Sample_2_CATTATCCACAGCCCA",-3.66858148574829,8.57568454742432 -"Sample_2_CATTATCGTGGTCTCG",2.43172335624695,6.05743312835693 -"Sample_2_CCAATCCGTCTCTTAT",-0.754400670528412,9.67450046539307 -"Sample_2_CCAATCCTCTCATTCA",-1.41102147102356,8.86275768280029 -"Sample_2_CCACCTATCTTGCCGT",0.767095506191254,-11.1441736221313 -"Sample_2_CCACGGACAATCACAC",3.48531818389893,-9.85309314727783 -"Sample_2_CCACGGATCAGGCAAG",2.30445122718811,-4.8685474395752 -"Sample_2_CCACTACAGTGGACGT",3.81238508224487,5.97278690338135 -"Sample_2_CCAGCGAGTTCGTTGA",4.53498363494873,7.09426498413086 -"Sample_2_CCATTCGCAAGCCATT",-1.40313637256622,9.00105762481689 -"Sample_2_CCCAATCTCAACACCA",3.69979310035706,7.36099433898926 -"Sample_2_CCCTCCTAGTAGCCGA",2.54321932792664,-9.99652671813965 -"Sample_2_CCCTCCTGTTGTACAC",-2.07157206535339,9.06001091003418 -"Sample_2_CCGGTAGAGTTACGGG",-2.02895927429199,9.03836059570312 -"Sample_2_CCGGTAGTCGCCTGAG",-0.30379256606102,-10.1836833953857 -"Sample_2_CCGTACTCAGGGTATG",4.34455728530884,5.8697304725647 -"Sample_2_CCGTACTTCTACTATC",2.19050550460815,-10.2162351608276 -"Sample_2_CCGTGGAAGCTGAAAT",1.62085175514221,6.99724054336548 -"Sample_2_CCGTTCAAGCCCGAAA",1.03903639316559,-9.09964752197266 -"Sample_2_CCTAAAGAGCGATTCT",3.45481276512146,5.17640256881714 -"Sample_2_CCTAAAGCAAGTAATG",3.51051259040833,6.29619789123535 -"Sample_2_CCTACACAGCAAATCA",-9.64228248596191,-2.73223996162415 -"Sample_2_CCTACACTCTTTACGT",3.09435629844666,-10.79185962677 -"Sample_2_CCTACCACATCGGAAG",-1.07175958156586,0.0709773823618889 -"Sample_2_CCTAGCTAGAGGTACC",-1.77269721031189,8.33713626861572 -"Sample_2_CCTATTATCAAACCAC",1.88781785964966,8.09424304962158 -"Sample_2_CCTCTGAGTAAGAGGA",-0.690892338752747,0.102271623909473 -"Sample_2_CCTCTGAGTTATTCTC",2.49461460113525,7.51391506195068 -"Sample_2_CCTCTGATCAGCCTAA",0.619292616844177,-8.10032272338867 -"Sample_2_CCTTACGAGCACCGCT",2.12799453735352,-9.46491146087646 -"Sample_2_CCTTACGGTCGCATCG",1.91421008110046,-6.49885606765747 -"Sample_2_CCTTCCCCAAACCTAC",-0.547998428344727,9.4435396194458 -"Sample_2_CCTTCCCTCACTGGGC",-2.1534583568573,7.05650329589844 -"Sample_2_CCTTCGAAGCGTTTAC",3.63080143928528,-8.35389518737793 -"Sample_2_CCTTCGACAAGAAGAG",3.60039114952087,6.39253044128418 -"Sample_2_CGAACATAGTCAAGCG",1.84329652786255,-6.73609781265259 -"Sample_2_CGAACATCATCAGTCA",3.92343926429749,3.84774327278137 -"Sample_2_CGAATGTGTCATTAGC",1.35752034187317,8.44132900238037 -"Sample_2_CGACCTTAGCACCGCT",-0.193068310618401,5.49457693099976 -"Sample_2_CGACCTTAGTTGTAGA",-9.54714298248291,-2.63613820075989 -"Sample_2_CGACCTTTCAAGCCTA",-1.17123031616211,0.670337319374084 -"Sample_2_CGACTTCTCATAGCAC",3.12348318099976,-9.34591293334961 -"Sample_2_CGAGCACAGACGCAAC",1.50253617763519,-10.2598571777344 -"Sample_2_CGAGCACAGCTCAACT",5.34832239151001,6.55733633041382 -"Sample_2_CGAGCACAGGCGTACA",-2.92095398902893,7.74431657791138 -"Sample_2_CGAGCACTCTGCAAGT",-2.81320667266846,7.77249622344971 -"Sample_2_CGAGCCACATGCAACT",-2.31915378570557,6.79165506362915 -"Sample_2_CGAGCCATCGTGGTCG",0.129045769572258,4.00837182998657 -"Sample_2_CGATCGGAGACGCTTT",-1.43082666397095,1.38790929317474 -"Sample_2_CGATCGGGTCCCTTGT",-0.497782498598099,1.18018436431885 -"Sample_2_CGATGGCTCGGTCCGA",-0.595341742038727,9.70358943939209 -"Sample_2_CGATGTAAGGACATTA",2.262770652771,5.61354732513428 -"Sample_2_CGATTGAGTGGTACAG",2.21263766288757,-7.1452522277832 -"Sample_2_CGCCAAGAGACCACGA",-1.61572372913361,0.336304038763046 -"Sample_2_CGCCAAGGTGTCCTCT",2.62102651596069,-11.1984996795654 -"Sample_2_CGCGGTAAGGCATGGT",3.77258563041687,-8.61324214935303 -"Sample_2_CGCGGTAAGTATCGAA",1.21839737892151,-10.4119844436646 -"Sample_2_CGCGGTACACAGGCCT",-0.429587543010712,-9.71237850189209 -"Sample_2_CGCGGTATCAACGCTA",1.34285998344421,-8.11648654937744 -"Sample_2_CGCGGTATCTCTGCTG",-0.76593291759491,4.63291263580322 -"Sample_2_CGCGTTTTCCAGATCA",1.55738854408264,2.28002500534058 -"Sample_2_CGCTATCCACTGTCGG",-2.62551498413086,7.39648914337158 -"Sample_2_CGCTATCGTGCAGACA",1.52604925632477,-10.1507329940796 -"Sample_2_CGCTATCTCGCCATAA",0.70492821931839,-7.9971399307251 -"Sample_2_CGCTATCTCTGTACGA",0.696970283985138,4.67271137237549 -"Sample_2_CGCTGGATCCTAGGGC",-0.643948197364807,6.99590015411377 -"Sample_2_CGCTTCACATTTCAGG",3.2002637386322,4.86870288848877 -"Sample_2_CGGACACAGCAGCGTA",-1.49595606327057,9.7742977142334 -"Sample_2_CGGACACAGGTGCTTT",0.325844705104828,-9.01515579223633 -"Sample_2_CGGACACTCCTCGCAT",-1.136066198349,2.17664122581482 -"Sample_2_CGGACGTTCAGGTTCA",2.91334700584412,6.23187828063965 -"Sample_2_CGGACTGTCTCTGAGA",-0.537001669406891,7.70639705657959 -"Sample_2_CGGAGCTCAAACTGTC",2.95291590690613,4.40727710723877 -"Sample_2_CGGAGCTGTACCGTTA",3.74634099006653,7.87159252166748 -"Sample_2_CGGAGTCTCAGGCCCA",0.5074103474617,-10.1908931732178 -"Sample_2_CGGCTAGCAATCTGCA",2.49887895584106,-7.14664554595947 -"Sample_2_CGTAGCGCAAACAACA",3.30574679374695,-7.20064544677734 -"Sample_2_CGTAGCGTCAGCACAT",2.6238067150116,-7.98223114013672 -"Sample_2_CGTAGCGTCCTAGAAC",1.92870914936066,-4.61911725997925 -"Sample_2_CGTAGGCGTCTGATTG",-0.339592933654785,7.75080966949463 -"Sample_2_CGTCAGGCAAGCTGTT",-0.0619976334273815,-9.20181846618652 -"Sample_2_CGTCAGGTCAAGGTAA",-1.95469057559967,9.40045738220215 -"Sample_2_CGTCCATAGCTATGCT",2.18891310691833,-8.02189064025879 -"Sample_2_CGTCCATCACCCTATC",3.72082686424255,5.04044342041016 -"Sample_2_CGTCTACCATAGGATA",-1.37086188793182,9.56240749359131 -"Sample_2_CGTGAGCCATTCTTAC",1.96705269813538,8.01556968688965 -"Sample_2_CGTGAGCTCAATCTCT",-9.72149085998535,-2.81126403808594 -"Sample_2_CGTTGGGTCAGAGACG",4.13262844085693,7.19426298141479 -"Sample_2_CGTTGGGTCCTTGCCA",-1.01343011856079,5.69344091415405 -"Sample_2_CTAACTTAGCCCAGCT",3.52886343002319,5.49601984024048 -"Sample_2_CTAAGACAGTGTCTCA",0.75559413433075,-10.5450859069824 -"Sample_2_CTAAGACCAGATCGGA",2.67302942276001,7.93223142623901 -"Sample_2_CTAAGACGTCGGATCC",1.77666974067688,-4.63154602050781 -"Sample_2_CTAAGACTCCACTGGG",1.9324631690979,-5.01174306869507 -"Sample_2_CTAATGGCACAGACTT",-1.30354022979736,7.69536733627319 -"Sample_2_CTAATGGGTTCAGGCC",1.93240475654602,-7.53331232070923 -"Sample_2_CTACACCCAACGCACC",2.3537609577179,-8.66712951660156 -"Sample_2_CTACACCGTTCAGCGC",3.58492398262024,-9.8335485458374 -"Sample_2_CTACACCTCGGTTCGG",2.41098499298096,4.83375978469849 -"Sample_2_CTACATTCATGCCCGA",2.68095064163208,-8.3710823059082 -"Sample_2_CTACATTGTTTGGCGC",3.46601009368896,-7.99448347091675 -"Sample_2_CTACCCAAGGTGCACA",2.44010090827942,-5.0979380607605 -"Sample_2_CTACCCACAAGGTGTG",-1.78617238998413,8.09042930603027 -"Sample_2_CTACCCACACGACTCG",2.24034452438354,-7.04807043075562 -"Sample_2_CTACCCACAGCGATCC",-0.0666037127375603,-10.9951772689819 -"Sample_2_CTACCCATCCAAATGC",-0.615976870059967,-7.24832201004028 -"Sample_2_CTACGTCAGAGCTGCA",-1.02769064903259,8.77914142608643 -"Sample_2_CTACGTCAGGCGATAC",2.86343550682068,7.27869176864624 -"Sample_2_CTACGTCGTCTCAACA",2.16005802154541,-8.97551822662354 -"Sample_2_CTACGTCTCGTCGTTC",0.717475891113281,-8.5037260055542 -"Sample_2_CTAGAGTCAATCTACG",1.05786633491516,-9.72928905487061 -"Sample_2_CTAGAGTCAGGAATCG",-0.591778039932251,0.673186957836151 -"Sample_2_CTAGAGTTCAACTCTT",3.38687801361084,6.02981662750244 -"Sample_2_CTAGTGACAGACGCTC",-1.47266352176666,6.9931583404541 -"Sample_2_CTAGTGACAGCGTAAG",-0.976550459861755,5.75491189956665 -"Sample_2_CTAGTGACATCTATGG",0.654601752758026,6.67149543762207 -"Sample_2_CTAGTGATCCTTGACC",-0.941399753093719,9.19360828399658 -"Sample_2_CTCACACTCACATACG",2.70308685302734,5.28052139282227 -"Sample_2_CTCAGAATCGGAAATA",-1.13821828365326,2.00149893760681 -"Sample_2_CTCATTAAGTCGTTTG",0.0688078477978706,8.20681571960449 -"Sample_2_CTCCTAGGTCATACTG",2.04771852493286,-7.1206202507019 -"Sample_2_CTCCTAGTCGGCGCTA",-1.16898572444916,0.936104297637939 -"Sample_2_CTCGAAAAGAACAATC",0.532846987247467,5.54428720474243 -"Sample_2_CTCGAAAAGACGACGT",-0.874139249324799,3.31508922576904 -"Sample_2_CTCGAAACACAGCGTC",4.70609092712402,-9.0043249130249 -"Sample_2_CTCGGGAGTTCGGCAC",1.80933499336243,6.53144311904907 -"Sample_2_CTCGTCACACACTGCG",1.17533349990845,-9.53723812103271 -"Sample_2_CTCGTCACACAGGAGT",0.701548278331757,-10.0405750274658 -"Sample_2_CTCGTCAGTTCCACGG",4.4127836227417,4.45223236083984 -"Sample_2_CTCTAATAGTCCGTAT",0.95601612329483,-11.2520427703857 -"Sample_2_CTCTAATAGTGGGTTG",4.19468450546265,5.14185237884521 -"Sample_2_CTCTACGAGGTGCACA",3.81541299819946,4.64831781387329 -"Sample_2_CTCTACGTCGAACGGA",4.68153715133667,6.86170291900635 -"Sample_2_CTCTGGTAGCGATATA",3.41120910644531,5.13582420349121 -"Sample_2_CTCTGGTAGGGAACGG",-0.278810024261475,8.31703948974609 -"Sample_2_CTCTGGTAGGTCGGAT",3.02982354164124,7.55151605606079 -"Sample_2_CTCTGGTCACTGCCAG",3.35514616966248,3.86028575897217 -"Sample_2_CTGAAACGTGCCTGTG",-0.13188011944294,-8.83624649047852 -"Sample_2_CTGAAGTCACGAAGCA",-2.30964469909668,8.38949966430664 -"Sample_2_CTGAAGTTCCGCAAGC",2.23647546768188,-8.76334667205811 -"Sample_2_CTGAAGTTCGCAAACT",-2.35367131233215,8.27389335632324 -"Sample_2_CTGATAGAGCGTCAAG",-0.207430496811867,9.69259357452393 -"Sample_2_CTGATCCTCATAACCG",4.76245307922363,6.04568719863892 -"Sample_2_CTGATCCTCGCAAGCC",-2.42928433418274,8.29139518737793 -"Sample_2_CTGATCCTCTACTATC",-1.49048697948456,6.71158456802368 -"Sample_2_CTGCCTAAGAAGGTGA",0.220979258418083,-10.6752214431763 -"Sample_2_CTGCCTAAGACAGAGA",0.838299453258514,2.79089736938477 -"Sample_2_CTGCCTACAGACAGGT",4.10219526290894,-9.91577339172363 -"Sample_2_CTGCTGTCAACGATCT",-0.0589476674795151,-7.82927417755127 -"Sample_2_CTGGTCTCAACCGCCA",4.87493991851807,4.86474514007568 -"Sample_2_CTGGTCTGTATCAGTC",-0.0585139207541943,-10.0522594451904 -"Sample_2_CTGGTCTGTTGACGTT",2.06621980667114,7.77075910568237 -"Sample_2_CTGGTCTGTTGTCGCG",-1.71637654304504,8.71114349365234 -"Sample_2_CTGTGCTCACATGGGA",-0.482556700706482,9.39114856719971 -"Sample_2_CTGTGCTCACGGCCAT",3.16288828849792,5.20122289657593 -"Sample_2_CTGTGCTTCAGCGATT",3.72534990310669,6.72742986679077 -"Sample_2_CTTAACTGTGGACGAT",-1.09564471244812,8.63945865631104 -"Sample_2_CTTACCGAGCGTCAAG",2.61703109741211,6.55156087875366 -"Sample_2_CTTAGGAAGCCAACAG",-1.58060324192047,8.31639957427979 -"Sample_2_CTTCTCTAGTATCGAA",2.79227042198181,5.34777164459229 -"Sample_2_CTTCTCTAGTGCGATG",1.48864996433258,-10.5585298538208 -"Sample_2_CTTTGCGTCGGGAGTA",3.67751693725586,7.53460025787354 -"Sample_2_GAAACTCAGTACGTTC",-0.0470688603818417,3.0260272026062 -"Sample_2_GAAACTCGTCTGCCAG",1.41713547706604,1.63406538963318 -"Sample_2_GAAATGAAGAAGGGTA",3.68726634979248,4.7767972946167 -"Sample_2_GAAATGAGTGTTAAGA",3.86777877807617,6.83382415771484 -"Sample_2_GAACATCTCCTTGGTC",0.303660601377487,8.9306058883667 -"Sample_2_GAACCTACAACACCCG",-1.58270299434662,8.75334548950195 -"Sample_2_GAACGGAAGCAAATCA",2.52289223670959,6.52110815048218 -"Sample_2_GAAGCAGGTTCAGGCC",2.20913100242615,-10.5493307113647 -"Sample_2_GAAGCAGTCTGTGCAA",2.66300821304321,-11.0251178741455 -"Sample_2_GAATAAGAGCGATTCT",2.38001608848572,5.55337285995483 -"Sample_2_GAATAAGAGTTGAGAT",0.329577773809433,-9.29755306243896 -"Sample_2_GAATAAGGTACCGTTA",-0.661956489086151,9.64391708374023 -"Sample_2_GAATAAGTCCACTCCA",0.392118364572525,-10.3640604019165 -"Sample_2_GACACGCCACGACGAA",1.57430946826935,6.30031871795654 -"Sample_2_GACACGCGTAACGTTC",-0.289780288934708,4.34227132797241 -"Sample_2_GACAGAGAGGCGTACA",1.80434787273407,-6.00570249557495 -"Sample_2_GACAGAGTCGAGAACG",-1.27216899394989,0.478481978178024 -"Sample_2_GACCAATGTGAGCGAT",-0.446041494607925,3.66822671890259 -"Sample_2_GACCTGGCACTTCGAA",2.1007833480835,-9.66742134094238 -"Sample_2_GACCTGGCAGTAAGAT",-0.798046827316284,0.0232799798250198 -"Sample_2_GACGGCTGTTGATTGC",-0.0369717329740524,-10.1397943496704 -"Sample_2_GACGGCTGTTTGGGCC",3.49552464485168,8.18905067443848 -"Sample_2_GACGGCTTCAACGCTA",3.13623714447021,5.66573047637939 -"Sample_2_GACGTGCTCACCATAG",-1.16980254650116,0.251904189586639 -"Sample_2_GACGTTAAGAATAGGG",1.14140188694,-8.85609149932861 -"Sample_2_GACGTTAAGTGTACGG",-1.22859585285187,7.80867338180542 -"Sample_2_GACTACAAGTACACCT",3.78286933898926,7.79269695281982 -"Sample_2_GACTACATCGGAATCT",1.88375401496887,-8.96599292755127 -"Sample_2_GACTGCGGTTCGGCAC",5.10640668869019,5.99217557907104 -"Sample_2_GACTGCGTCAGTTGAC",-1.63415563106537,8.1964168548584 -"Sample_2_GACTGCGTCTGTACGA",-2.64307260513306,8.40739154815674 -"Sample_2_GAGCAGAAGTGCTGCC",-2.55199003219604,8.84134006500244 -"Sample_2_GAGCAGAGTCTTGATG",-0.275412797927856,-8.9815502166748 -"Sample_2_GAGGTGAGTCCTAGCG",-3.94951558113098,8.89658069610596 -"Sample_2_GAGGTGATCCCTAACC",4.56758737564087,-7.99584007263184 -"Sample_2_GATCAGTAGAGTTGGC",-1.61097073554993,8.34739780426025 -"Sample_2_GATCAGTAGTAGCGGT",-2.56796622276306,8.20053672790527 -"Sample_2_GATCAGTCAAAGCGGT",-0.728795945644379,8.57220458984375 -"Sample_2_GATCAGTTCCGAGCCA",0.703264772891998,-9.33327579498291 -"Sample_2_GATCAGTTCCTCGCAT",-0.0764984488487244,-9.26644325256348 -"Sample_2_GATCGATGTTCCCGAG",-1.44836151599884,7.96166038513184 -"Sample_2_GATCGCGAGACCTTTG",3.6006932258606,4.40932941436768 -"Sample_2_GATCGCGCAAGAAAGG",2.40417647361755,-5.18708276748657 -"Sample_2_GATCGCGGTCAACTGT",4.34507608413696,-8.33303642272949 -"Sample_2_GATCGCGTCAAGGTAA",0.324859470129013,-7.14410257339478 -"Sample_2_GATCGTAAGGTACTCT",-1.12324368953705,9.0920295715332 -"Sample_2_GATGAAAAGACACTAA",-2.09026455879211,8.66267395019531 -"Sample_2_GATGAAATCGTCTGAA",0.753444790840149,-10.0609703063965 -"Sample_2_GATGAGGTCCTAGGGC",2.74209833145142,-7.49758052825928 -"Sample_2_GATGCTAAGTCCCACG",-2.07711338996887,8.22933387756348 -"Sample_2_GATGCTACAAAGGAAG",3.42569279670715,4.30734157562256 -"Sample_2_GATGCTAGTAGAGGAA",3.47193551063538,5.51085090637207 -"Sample_2_GATTCAGAGACAGGCT",0.557960450649261,2.42450594902039 -"Sample_2_GCAAACTCATCCCATC",0.127689123153687,1.80011403560638 -"Sample_2_GCAAACTGTGGACGAT",2.76135182380676,-7.56620359420776 -"Sample_2_GCAATCAAGTAGGCCA",3.80027103424072,-9.60830497741699 -"Sample_2_GCAATCAGTCTAGTCA",3.20101308822632,-7.67079734802246 -"Sample_2_GCAATCATCACCCTCA",1.31010639667511,-8.7073802947998 -"Sample_2_GCAGTTACAACGATGG",0.992745876312256,-10.5525493621826 -"Sample_2_GCAGTTAGTTCAGCGC",-0.845603585243225,9.57477474212646 -"Sample_2_GCATACATCCTGCCAT",4.47138452529907,4.27621221542358 -"Sample_2_GCATACATCTTTAGGG",2.60876774787903,-10.2735280990601 -"Sample_2_GCATGCGGTCCCTTGT",0.634178280830383,5.52774381637573 -"Sample_2_GCATGTAAGACGCACA",1.77095925807953,6.54305076599121 -"Sample_2_GCATGTAAGAGTAATC",-1.79028022289276,1.33167827129364 -"Sample_2_GCATGTACACCTCGTT",-0.642697811126709,-0.0606019273400307 -"Sample_2_GCCAAATAGTGGAGAA",5.28751611709595,5.51163339614868 -"Sample_2_GCCTCTACAAACTGCT",2.01838421821594,2.96047949790955 -"Sample_2_GCGACCACATGCCCGA",4.58801031112671,4.8243522644043 -"Sample_2_GCGAGAAAGCTACCGC",-1.86373209953308,8.97793674468994 -"Sample_2_GCGAGAACAAGGCTCC",2.09097218513489,6.96303749084473 -"Sample_2_GCGAGAACACCAGTTA",0.734833955764771,-8.76655769348145 -"Sample_2_GCGCAACAGGGTGTGT",-0.724202871322632,4.89528703689575 -"Sample_2_GCGCAACCACCCAGTG",1.32647740840912,-8.2830982208252 -"Sample_2_GCGCAACTCCTATGTT",2.13918423652649,7.75530195236206 -"Sample_2_GCGCAACTCTTGCATT",0.811457753181458,-7.41915321350098 -"Sample_2_GCGCAGTTCCGTCATC",-0.410527855157852,-0.15557087957859 -"Sample_2_GCGCCAACACCGTTGG",-3.93152570724487,8.96311378479004 -"Sample_2_GCGCGATTCCGTAGTA",2.87845993041992,-8.61576557159424 -"Sample_2_GCGGGTTGTGCGCTTG",-1.04879868030548,5.33108425140381 -"Sample_2_GCTCCTAGTAGAAGGA",-0.240868091583252,0.754394233226776 -"Sample_2_GCTCTGTCAAGTCTAC",0.0139041189104319,-9.63117504119873 -"Sample_2_GCTGCAGAGTGGGATC",3.24108123779297,7.60893535614014 -"Sample_2_GCTGCAGCACCACCAG",1.09569346904755,-10.8765726089478 -"Sample_2_GCTGCAGGTCGATTGT",0.630888223648071,-10.0789928436279 -"Sample_2_GCTGCAGTCCTTTACA",1.9308944940567,2.29233169555664 -"Sample_2_GCTGCAGTCGGAGGTA",4.01273107528687,-8.30885219573975 -"Sample_2_GCTGCGAAGGCTACGA",-0.455357402563095,-0.0756356865167618 -"Sample_2_GCTGCGAGTGTCAATC",-1.3524934053421,9.04055309295654 -"Sample_2_GCTGCGATCTGATACG",1.2120156288147,-8.5221700668335 -"Sample_2_GCTGGGTCATATGCTG",-0.319922834634781,-9.51258182525635 -"Sample_2_GCTTCCACACATCCGG",4.68958759307861,4.600013256073 -"Sample_2_GCTTCCACATCTATGG",-0.674934148788452,-9.52346992492676 -"Sample_2_GCTTCCATCTCTGTCG",0.502156376838684,4.26655960083008 -"Sample_2_GCTTGAACAGTATAAG",3.90642142295837,6.49509572982788 -"Sample_2_GCTTGAATCTAACCGA",0.165855452418327,-8.31894683837891 -"Sample_2_GGAAAGCTCCTTGCCA",1.69597268104553,-8.24169445037842 -"Sample_2_GGAACTTCAGATGGCA",4.12051916122437,4.91739416122437 -"Sample_2_GGAATAACAAATTGCC",4.38854074478149,-8.47641372680664 -"Sample_2_GGAATAACAGTTCATG",-2.27713060379028,9.05123043060303 -"Sample_2_GGACATTCACTTCGAA",2.73819208145142,-9.6597375869751 -"Sample_2_GGAGCAACAAGCGCTC",2.35614085197449,6.38037872314453 -"Sample_2_GGAGCAACATCTCCCA",-0.0190206374973059,6.22568559646606 -"Sample_2_GGAGCAATCGCATGGC",-0.49411529302597,-7.05848169326782 -"Sample_2_GGAGCAATCGTAGATC",-0.257333904504776,-11.098464012146 -"Sample_2_GGATGTTAGTCCGTAT",-2.54227590560913,7.52298450469971 -"Sample_2_GGATTACCATTGAGCT",0.313053160905838,-9.60498237609863 -"Sample_2_GGATTACTCACCAGGC",-0.139906793832779,5.21865463256836 -"Sample_2_GGCAATTAGAGGTAGA",3.6366171836853,4.40870189666748 -"Sample_2_GGCAATTAGTGTTGAA",-1.60160446166992,1.36376941204071 -"Sample_2_GGCAATTCAGATTGCT",-0.430101990699768,3.6316819190979 -"Sample_2_GGCAATTGTCAAACTC",1.12596523761749,-8.58235740661621 -"Sample_2_GGCAATTGTGATAAGT",4.76277256011963,4.38891935348511 -"Sample_2_GGCCGATGTTTCCACC",2.78472232818604,-10.4785709381104 -"Sample_2_GGCGACTCATCCTAGA",0.802654206752777,-9.03677368164062 -"Sample_2_GGCGACTCATCCTTGC",-0.134599044919014,6.67172861099243 -"Sample_2_GGCGTGTCACATGACT",1.35419487953186,-8.93182468414307 -"Sample_2_GGGAATGAGATGGCGT",-1.77495944499969,9.24043464660645 -"Sample_2_GGGAATGGTCATCCCT",-1.31121337413788,7.84960889816284 -"Sample_2_GGGACCTTCTTGGGTA",-0.245622664690018,-0.248764559626579 -"Sample_2_GGGAGATAGTGTTTGC",-0.603672981262207,8.72039222717285 -"Sample_2_GGGAGATCAGATGGCA",0.288437187671661,4.33993577957153 -"Sample_2_GGGAGATCATACCATG",-1.68566536903381,8.01160049438477 -"Sample_2_GGGAGATGTCAAACTC",4.45819807052612,-9.67481803894043 -"Sample_2_GGGATGAGTCGTCTTC",3.16674709320068,6.18274354934692 -"Sample_2_GGGATGATCAGTTCGA",1.56116890907288,6.39014387130737 -"Sample_2_GGGCACTAGCAGGTCA",3.61108350753784,-8.46444702148438 -"Sample_2_GGGCACTCAAAGCAAT",-0.940939664840698,3.90363097190857 -"Sample_2_GGGCACTTCCTAGGGC",-1.7984334230423,7.94485807418823 -"Sample_2_GGGCACTTCTATCGCC",0.908063471317291,-9.57259464263916 -"Sample_2_GGGCATCCACGGCGTT",0.0772609561681747,-9.08860969543457 -"Sample_2_GGGCATCCATCACAAC",0.105370059609413,3.53539514541626 -"Sample_2_GGGCATCGTTCCTCCA",5.09278631210327,5.5049204826355 -"Sample_2_GGGCATCTCCGCATCT",2.21695756912231,5.51585245132446 -"Sample_2_GGTATTGGTGAGGGTT",-2.28631472587585,6.9326343536377 -"Sample_2_GGTATTGGTTCCCGAG",0.174898311495781,3.95622086524963 -"Sample_2_GGTGAAGAGAGGTAGA",0.96459025144577,2.09068655967712 -"Sample_2_GGTGAAGTCCATGAGT",3.62265682220459,6.30330801010132 -"Sample_2_GGTGAAGTCCTCATTA",2.31561636924744,-8.1111011505127 -"Sample_2_GGTGCGTAGAGTACAT",1.30013704299927,-10.6613492965698 -"Sample_2_GGTGCGTAGTACATGA",0.00674083223566413,5.63631010055542 -"Sample_2_GGTGTTAGTAGTAGTA",-0.841310143470764,0.291478186845779 -"Sample_2_GTAACGTAGGACAGCT",2.13543128967285,6.18163537979126 -"Sample_2_GTAACGTTCACTTACT",2.26708507537842,4.88862133026123 -"Sample_2_GTAACTGCATCGGTTA",3.72936224937439,-9.24002838134766 -"Sample_2_GTAACTGTCCGAACGC",-0.591503441333771,-7.26344013214111 -"Sample_2_GTACGTAAGACACTAA",-0.802965998649597,-6.88883113861084 -"Sample_2_GTACGTACATGCTGGC",2.94774031639099,5.97824478149414 -"Sample_2_GTACGTATCCTGTAGA",-2.65919256210327,8.68787574768066 -"Sample_2_GTACTTTGTGTCAATC",-0.0365527719259262,8.50883769989014 -"Sample_2_GTAGGCCAGCGCTCCA",2.70624542236328,7.81864976882935 -"Sample_2_GTAGGCCGTAACGTTC",0.0443353764712811,-9.60340023040771 -"Sample_2_GTATCTTTCCTATGTT",2.87174987792969,6.97519302368164 -"Sample_2_GTATTCTCAGCTGCTG",3.47861528396606,6.56786918640137 -"Sample_2_GTATTCTGTCCCTACT",0.765854597091675,2.53114652633667 -"Sample_2_GTATTCTTCCAATGGT",2.85680294036865,5.74490976333618 -"Sample_2_GTCACAAGTCCGAAGA",-1.45028686523438,8.37578868865967 -"Sample_2_GTCACAAGTTCCACAA",-0.168279573321342,-8.10830783843994 -"Sample_2_GTCACGGCACCGGAAA",-1.98417901992798,7.97107744216919 -"Sample_2_GTCACGGGTTGCCTCT",4.90126323699951,5.80808115005493 -"Sample_2_GTCACGGTCAAGGCTT",-1.73285377025604,1.31158447265625 -"Sample_2_GTCATTTAGCTTTGGT",1.18699812889099,-9.87060928344727 -"Sample_2_GTCATTTTCAGTTTGG",3.1621241569519,6.34158229827881 -"Sample_2_GTCCTCAAGTATCGAA",-1.42456471920013,9.12302875518799 -"Sample_2_GTCCTCAAGTGCCATT",4.46475553512573,5.98818349838257 -"Sample_2_GTCGGGTGTCCTCTTG",3.58841300010681,7.65212202072144 -"Sample_2_GTCGTAATCCATGAGT",0.678011059761047,3.62503743171692 -"Sample_2_GTCTCGTAGTTCGCAT",-2.46576762199402,8.27730369567871 -"Sample_2_GTCTTCGGTATAAACG",3.30279564857483,3.45396447181702 -"Sample_2_GTGAAGGAGTACGACG",1.42178654670715,-9.41478061676025 -"Sample_2_GTGAAGGCAATAACGA",1.49739336967468,-8.57739353179932 -"Sample_2_GTGAAGGCATGTTGAC",3.9612991809845,7.29014682769775 -"Sample_2_GTGAAGGGTCCTCTTG",0.346495002508163,4.75266218185425 -"Sample_2_GTGAAGGTCATGTAGC",1.81842517852783,-5.05217123031616 -"Sample_2_GTGCAGCAGCGATTCT",1.71381461620331,8.48898029327393 -"Sample_2_GTGCAGCAGGCTACGA",4.81126356124878,4.41991853713989 -"Sample_2_GTGCAGCTCAAAGACA",-2.69243359565735,7.36370849609375 -"Sample_2_GTGCATACATCCGGGT",2.54055881500244,-9.76883602142334 -"Sample_2_GTGCATATCACCGTAA",1.56337988376617,-7.05689907073975 -"Sample_2_GTGCATATCATTATCC",2.30328178405762,-9.21470165252686 -"Sample_2_GTGCGGTAGAGGTACC",3.43727016448975,7.61666774749756 -"Sample_2_GTGCGGTCAAGCCGTC",0.356183260679245,-8.5522632598877 -"Sample_2_GTGCGGTCACATTCGA",-2.40909481048584,7.73382234573364 -"Sample_2_GTGCTTCAGCCAACAG",2.13452196121216,-7.71556043624878 -"Sample_2_GTGGGTCTCAAGAAGT",0.161444291472435,1.39933753013611 -"Sample_2_GTGTTAGCATCCTAGA",3.0701892375946,-9.34256267547607 -"Sample_2_GTGTTAGGTAGATTAG",2.93646121025085,5.89251327514648 -"Sample_2_GTTACAGCATTAGGCT",-2.01941561698914,8.2223653793335 -"Sample_2_GTTCATTCATGCATGT",3.17137241363525,-10.7611799240112 -"Sample_2_GTTCATTTCCAAACAC",-1.02676200866699,7.79853343963623 -"Sample_2_GTTCTCGTCTAGCACA",1.33371365070343,-9.78653144836426 -"Sample_2_GTTTCTAAGATTACCC",-0.828245282173157,4.50760984420776 -"Sample_2_GTTTCTAAGGTGCAAC",-1.04392397403717,5.96352005004883 -"Sample_2_GTTTCTACATCACGAT",-1.61111128330231,8.68826103210449 -"Sample_2_TAAACCGAGTTCGATC",4.24266576766968,-8.34085273742676 -"Sample_2_TAAACCGGTCTGCGGT",2.20468544960022,6.19665002822876 -"Sample_2_TAAACCGGTTCGCGAC",4.4120945930481,5.55164051055908 -"Sample_2_TAAGAGAGTACTTAGC",-0.0202916115522385,-9.08162021636963 -"Sample_2_TAAGAGATCCGAAGAG",-1.73159539699554,9.28734493255615 -"Sample_2_TAAGCGTAGGACTGGT",1.01854777336121,-7.44385480880737 -"Sample_2_TAAGTGCCAGACAAAT",3.16579866409302,-10.8557100296021 -"Sample_2_TAAGTGCTCGCCATAA",1.17174112796783,-8.98999881744385 -"Sample_2_TACACGAGTCACTTCC",2.68450808525085,-10.5971698760986 -"Sample_2_TACACGAGTTTCGCTC",-3.67823910713196,8.80016708374023 -"Sample_2_TACACGATCCCAGGTG",0.486207693815231,2.20995116233826 -"Sample_2_TACCTATGTCCAACTA",1.89273202419281,7.97818326950073 -"Sample_2_TACCTTAAGCTAACAA",1.91757607460022,-10.0196628570557 -"Sample_2_TACCTTATCTGGCGTG",3.39237856864929,-7.36124467849731 -"Sample_2_TACGGGCCATCCTAGA",-0.941759824752808,6.41630840301514 -"Sample_2_TACGGGCCATTACCTT",-0.715539991855621,3.24874901771545 -"Sample_2_TACGGTAAGGATGGAA",2.4907169342041,6.81452178955078 -"Sample_2_TACTCATTCGGCCGAT",-1.03990757465363,2.19286823272705 -"Sample_2_TACTCGCCAACTGCGC",0.0640943571925163,-9.37134552001953 -"Sample_2_TACTCGCTCCGAATGT",-1.23900187015533,9.40444374084473 -"Sample_2_TACTCGCTCGTAGGTT",-1.37959682941437,8.6184549331665 -"Sample_2_TACTTACTCCGTTGTC",-0.378066837787628,-10.5021018981934 -"Sample_2_TACTTGTGTACTTAGC",0.601878225803375,-8.72785568237305 -"Sample_2_TAGCCGGCAATGTTGC",2.08283996582031,5.39203262329102 -"Sample_2_TAGCCGGTCGAGAACG",0.710172712802887,2.69971704483032 -"Sample_2_TAGGCATCAGTCCTTC",-0.199499294161797,4.88635444641113 -"Sample_2_TAGGCATGTTGTACAC",-1.75997734069824,9.69057559967041 -"Sample_2_TAGTGGTCAAGGGTCA",1.8580836057663,-9.30741691589355 -"Sample_2_TAGTGGTTCAATACCG",-2.42201852798462,7.58682680130005 -"Sample_2_TAGTGGTTCCTAGTGA",4.32615947723389,5.82360410690308 -"Sample_2_TAGTTGGAGTAGGCCA",2.10703587532043,-9.4180212020874 -"Sample_2_TATCAGGCAACTGCTA",3.01266264915466,-7.66838932037354 -"Sample_2_TATCAGGGTCGGATCC",2.10829472541809,5.77377796173096 -"Sample_2_TATCTCAAGACTTGAA",3.22497272491455,-8.35658359527588 -"Sample_2_TATCTCAAGGGAGTAA",1.71579372882843,-9.6931209564209 -"Sample_2_TATCTCAAGGTCATCT",0.20000909268856,4.17796468734741 -"Sample_2_TATCTCAGTCTACCTC",0.622728645801544,3.93293619155884 -"Sample_2_TATCTCATCATGTAGC",3.52879238128662,4.94586372375488 -"Sample_2_TATGCCCCAAGCTGTT",4.35267782211304,6.29163122177124 -"Sample_2_TATGCCCCAATCACAC",-1.34835290908813,9.08397388458252 -"Sample_2_TATTACCCAAACCCAT",-1.20105111598969,9.64632320404053 -"Sample_2_TATTACCCACGAAAGC",-3.1008563041687,7.87512397766113 -"Sample_2_TATTACCGTGATGTGG",-0.726540684700012,-10.5975551605225 -"Sample_2_TCAACGACAATCTACG",1.01282525062561,-8.72939014434814 -"Sample_2_TCAACGATCCTTTCGG",-0.147374913096428,1.84160447120667 -"Sample_2_TCAATCTGTGCAGACA",-0.750780582427979,0.921518206596375 -"Sample_2_TCAATCTGTGCGATAG",3.269047498703,7.31080436706543 -"Sample_2_TCACAAGCAATCGAAA",3.19095134735107,-9.82450675964355 -"Sample_2_TCACAAGGTCTCATCC",-0.998427093029022,-9.17161273956299 -"Sample_2_TCACGAACATACTACG",-1.02685046195984,0.457566678524017 -"Sample_2_TCACGAAGTAGAAAGG",-0.144474372267723,0.938789904117584 -"Sample_2_TCACGAATCATCGATG",2.66990828514099,5.40595579147339 -"Sample_2_TCACGAATCGTCACGG",2.91738343238831,5.60171556472778 -"Sample_2_TCAGATGAGTTGAGTA",-0.855517029762268,7.07564783096313 -"Sample_2_TCAGCAAGTGCTTCTC",4.59517002105713,6.50900983810425 -"Sample_2_TCAGCTCAGACAGGCT",0.636192619800568,2.95896220207214 -"Sample_2_TCAGCTCGTCGAGTTT",2.41833209991455,7.68106079101562 -"Sample_2_TCAGGATTCATTTGGG",3.02907824516296,6.09552049636841 -"Sample_2_TCAGGTAAGTCCGGTC",-1.08664703369141,3.37329173088074 -"Sample_2_TCAGGTACAGGATTGG",-1.38645303249359,1.06985294818878 -"Sample_2_TCAGGTAGTCCGTCAG",1.85578954219818,-6.78238821029663 -"Sample_2_TCAGGTATCGAGGTAG",3.06301879882812,-9.57776165008545 -"Sample_2_TCAGGTATCGCCTGTT",0.290255010128021,5.88131189346313 -"Sample_2_TCATTACCAGTATAAG",3.57920432090759,-9.31604099273682 -"Sample_2_TCATTTGAGGCAATTA",3.15743279457092,6.95983982086182 -"Sample_2_TCATTTGAGTGGTAGC",-1.79879903793335,0.698711395263672 -"Sample_2_TCATTTGGTGTTGAGG",-0.236364439129829,0.490620046854019 -"Sample_2_TCCACACAGTACACCT",-1.88263213634491,7.53612089157104 -"Sample_2_TCCACACCAGTTTACG",1.18819570541382,-10.5198602676392 -"Sample_2_TCCCGATAGACAGGCT",-1.82697546482086,8.10618495941162 -"Sample_2_TCCCGATCAAGTTCTG",1.32605218887329,-8.09475040435791 -"Sample_2_TCCCGATTCGTAGATC",-0.34793484210968,6.82689762115479 -"Sample_2_TCGAGGCGTAGAAGGA",-1.93426334857941,5.83449268341064 -"Sample_2_TCGAGGCGTTTACTCT",-0.422722637653351,-7.51220035552979 -"Sample_2_TCGAGGCTCCTCAACC",3.26434779167175,4.55835199356079 -"Sample_2_TCGAGGCTCGTTGACA",1.96573960781097,-9.00004577636719 -"Sample_2_TCGCGAGGTCAGTGGA",0.799896538257599,2.99936676025391 -"Sample_2_TCGGGACCAGACGTAG",2.47797441482544,7.50278520584106 -"Sample_2_TCGGGACGTGATAAGT",0.536046981811523,-8.67595291137695 -"Sample_2_TCGGGACTCACCCGAG",-1.34791612625122,8.15101051330566 -"Sample_2_TCGGGACTCCGAAGAG",0.801810204982758,-9.76134395599365 -"Sample_2_TCGGTAACACAGGCCT",2.70240354537964,5.71730661392212 -"Sample_2_TCGGTAACATTAACCG",2.72554087638855,-7.28230905532837 -"Sample_2_TCGTACCAGATCTGCT",2.74314451217651,4.74351167678833 -"Sample_2_TCGTACCGTTTGACAC",1.24084913730621,-8.58538913726807 -"Sample_2_TCGTACCTCAAACCGT",2.77638173103333,-6.88912534713745 -"Sample_2_TCGTACCTCCCGACTT",3.91711211204529,4.83328151702881 -"Sample_2_TCGTAGACATCTATGG",2.32776689529419,-9.08304691314697 -"Sample_2_TCTATTGAGGCGACAT",3.0478048324585,7.45929002761841 -"Sample_2_TCTATTGGTAGCTCCG",1.9036318063736,8.401930809021 -"Sample_2_TCTCATAAGACTTTCG",-0.26843649148941,0.440858572721481 -"Sample_2_TCTCTAATCCGTAGTA",0.106110207736492,3.64707207679749 -"Sample_2_TCTGAGAAGAGTGAGA",0.0986613184213638,-10.573037147522 -"Sample_2_TCTGAGACACCACGTG",-0.347568064928055,0.62399435043335 -"Sample_2_TCTGAGACAGATCTGT",2.18160247802734,4.7471137046814 -"Sample_2_TCTGGAAAGTACGTTC",2.5413863658905,7.87342262268066 -"Sample_2_TCTTCGGAGGGATGGG",3.16142439842224,7.69505977630615 -"Sample_2_TCTTTCCAGACTGGGT",3.11197710037231,-9.09385108947754 -"Sample_2_TCTTTCCGTGCTTCTC",3.96654582023621,5.12148332595825 -"Sample_2_TGAAAGATCCCTAATT",0.413892805576324,-11.5104751586914 -"Sample_2_TGAAAGATCGTTGCCT",-1.82062709331512,0.679070472717285 -"Sample_2_TGACAACTCCTACAGA",4.11157417297363,-9.47665309906006 -"Sample_2_TGACTTTCAATGGATA",0.133321732282639,2.16348314285278 -"Sample_2_TGACTTTTCTAGCACA",3.91469502449036,5.64563322067261 -"Sample_2_TGAGAGGAGCAATCTC",3.32446098327637,-10.1729011535645 -"Sample_2_TGAGCATGTCAAAGCG",-0.320151478052139,3.88992023468018 -"Sample_2_TGAGCCGGTGCCTTGG",0.392462402582169,-11.3297214508057 -"Sample_2_TGAGGGAAGCATGGCA",-2.30605149269104,6.66576671600342 -"Sample_2_TGAGGGACATGGGAAC",-0.40620556473732,4.1514139175415 -"Sample_2_TGAGGGATCACATGCA",3.93912601470947,5.13544416427612 -"Sample_2_TGATTTCTCCTTCAAT",1.37932920455933,-8.09883308410645 -"Sample_2_TGCACCTAGGGCACTA",1.89535689353943,-9.5971097946167 -"Sample_2_TGCCCATCAGTAAGCG",2.47450041770935,4.81238031387329 -"Sample_2_TGCCCTAGTTCAACCA",0.430548876523972,-8.16486167907715 -"Sample_2_TGCCCTATCAGGCAAG",-0.954828262329102,7.93054342269897 -"Sample_2_TGCGGGTAGACCTAGG",3.00772261619568,6.36842393875122 -"Sample_2_TGCGGGTGTTATGTGC",1.81350755691528,-4.58076953887939 -"Sample_2_TGCGTGGGTAATTGGA",2.59625315666199,-7.9819130897522 -"Sample_2_TGCTACCAGCTCCCAG",-0.290158778429031,9.40306758880615 -"Sample_2_TGCTGCTGTCCAACTA",-0.206542328000069,5.74718332290649 -"Sample_2_TGGACGCAGGAGCGAG",-1.30097639560699,5.44703245162964 -"Sample_2_TGGACGCGTAACGCGA",3.69309878349304,7.7619194984436 -"Sample_2_TGGCGCAAGTACGCGA",3.52826690673828,6.42247486114502 -"Sample_2_TGGCGCAGTTCCCGAG",0.596459746360779,-8.30012321472168 -"Sample_2_TGGCGCATCCTACAGA",3.81291365623474,3.70639801025391 -"Sample_2_TGGGAAGAGGAACTGC",-2.51700830459595,9.10831165313721 -"Sample_2_TGGGAAGTCTATCCTA",-1.09713327884674,-8.13563346862793 -"Sample_2_TGGGCGTCAGTTAACC",2.53604459762573,7.96760654449463 -"Sample_2_TGGGCGTGTGTTGGGA",3.93395209312439,6.29234218597412 -"Sample_2_TGGTTAGAGCGCCTTG",1.68449985980988,-8.73291206359863 -"Sample_2_TGGTTAGAGGAATTAC",0.226238220930099,3.15258741378784 -"Sample_2_TGGTTAGGTCAATACC",1.79595255851746,-7.26984024047852 -"Sample_2_TGGTTCCTCCACTCCA",-0.247537881135941,5.4781322479248 -"Sample_2_TGTATTCAGATGTCGG",0.655124366283417,5.0499529838562 -"Sample_2_TGTATTCCAGCGTCCA",2.73580384254456,-9.10747909545898 -"Sample_2_TGTATTCTCTGATACG",2.79089879989624,-9.41042900085449 -"Sample_2_TGTCCCAAGAGACTAT",4.28743362426758,-9.61738967895508 -"Sample_2_TGTCCCATCTTGTACT",2.23207139968872,-9.7170524597168 -"Sample_2_TGTGGTACATGCAACT",-1.52169525623322,8.8923168182373 -"Sample_2_TGTGGTAGTGCATCTA",-1.4170435667038,0.371904283761978 -"Sample_2_TGTGGTATCCCGACTT",4.06054067611694,4.92706680297852 -"Sample_2_TGTGTTTAGGCCCTCA",4.41744661331177,-8.75786590576172 -"Sample_2_TGTGTTTGTTGTGGAG",0.886349081993103,2.75564980506897 -"Sample_2_TGTGTTTTCATGCTCC",3.99766516685486,7.14530324935913 -"Sample_2_TGTTCCGAGCCACCTG",2.03159761428833,-7.48870182037354 -"Sample_2_TGTTCCGCAGTGACAG",2.15023994445801,-4.81290817260742 -"Sample_2_TTAGGACAGGGTCGAT",-1.65826022624969,1.2582323551178 -"Sample_2_TTAGGACGTGGTACAG",2.78120112419128,6.07779550552368 -"Sample_2_TTAGGCACATAAAGGT",3.82346057891846,7.81411695480347 -"Sample_2_TTAGTTCTCACTTACT",2.80376887321472,5.07882785797119 -"Sample_2_TTAGTTCTCTTGAGAC",4.08466577529907,5.40344619750977 -"Sample_2_TTATGCTTCACGAAGG",-0.663998782634735,9.07790470123291 -"Sample_2_TTCGGTCAGCCAGAAC",2.79841041564941,-11.091046333313 -"Sample_2_TTCGGTCAGTTCCACA",-9.72986316680908,-2.86799764633179 -"Sample_2_TTCTACACATTACCTT",-0.911657810211182,0.86281955242157 -"Sample_2_TTCTACATCGCAAGCC",3.24749040603638,4.84483575820923 -"Sample_2_TTCTCAACACACTGCG",-0.522373557090759,5.0238676071167 -"Sample_2_TTCTCCTGTTCCCGAG",2.91026926040649,-10.9879932403564 -"Sample_2_TTCTCCTTCCGAATGT",2.64249014854431,6.47498655319214 -"Sample_2_TTCTTAGGTTTAGGAA",1.80916798114777,2.36553573608398 -"Sample_2_TTGAACGAGCCATCGC",1.69159650802612,-8.73082828521729 -"Sample_2_TTGAACGGTAGCTCCG",2.7043342590332,3.82222485542297 -"Sample_2_TTGAACGGTCTCAACA",0.170126393437386,0.0854008719325066 -"Sample_2_TTGAACGTCAGTTCGA",-0.970185399055481,8.64316844940186 -"Sample_2_TTGCCGTCACCTTGTC",4.17194795608521,-8.82023811340332 -"Sample_2_TTGCCGTTCTTGCAAG",1.83742129802704,5.71507740020752 -"Sample_2_TTGCGTCCAGCCTTGG",1.74079310894012,3.53607535362244 -"Sample_2_TTGCGTCTCACGGTTA",-1.0442887544632,6.89792537689209 -"Sample_2_TTGCGTCTCCTATTCA",4.28723382949829,6.82443141937256 -"Sample_2_TTGCGTCTCTTCTGGC",-1.35015404224396,5.51687955856323 -"Sample_2_TTGGCAAAGAGATGAG",3.7937273979187,-8.87756443023682 -"Sample_2_TTGGCAAGTATCACCA",4.17854595184326,5.105149269104 -"Sample_2_TTGTAGGAGTCGTACT",4.14154291152954,6.30688381195068 -"Sample_2_TTTATGCAGCCACTAT",2.19650101661682,6.07358264923096 -"Sample_2_TTTATGCCAGTTAACC",4.13388824462891,7.52402925491333 -"Sample_2_TTTGCGCAGGCTACGA",3.66919803619385,-10.4027309417725 -"Sample_2_TTTGCGCTCACTATTC",3.86760520935059,5.34841060638428 -"Sample_2_TTTGGTTTCTGTTTGT",-0.489038825035095,-0.0497650764882565 -"Sample_2_TTTGTCACAATTGCTG",2.46331763267517,2.93512606620789 -"Sample_2_TTTGTCAGTAGGAGTC",-9.53912162780762,-2.62771511077881 -"Sample_2_TTTGTCATCCTCATTA",0.460877120494843,-11.0803117752075 -"Sample_3_AAACCTGTCTGCTGTC",-0.396727502346039,0.387721180915833 -"Sample_3_AAACGGGGTTTGCATG",-3.83718252182007,8.58635711669922 -"Sample_3_AAACGGGTCCTTTACA",4.34322357177734,5.61384153366089 -"Sample_3_AAAGTAGCAAACAACA",-2.49119210243225,7.1802225112915 -"Sample_3_AAAGTAGCATAGAAAC",-2.82258176803589,6.88276767730713 -"Sample_3_AAATGCCCATGATCCA",2.92815351486206,6.47556447982788 -"Sample_3_AAATGCCGTTAGTGGG",-1.58650505542755,7.27634811401367 -"Sample_3_AAATGCCTCAGTTAGC",2.498379945755,6.19723606109619 -"Sample_3_AACACGTGTCGCTTCT",3.1306459903717,6.62193632125854 -"Sample_3_AACCATGGTCTCACCT",-0.990986227989197,4.80664730072021 -"Sample_3_AACTCAGGTACCGTAT",1.60424482822418,-10.1467189788818 -"Sample_3_AACTCAGGTCCATCCT",-0.408074021339417,-9.02249717712402 -"Sample_3_AACTCAGGTCGCTTCT",3.51643991470337,7.16677331924438 -"Sample_3_AACTCAGTCCTTTCTC",1.11915254592896,-10.035961151123 -"Sample_3_AACTCTTCACACATGT",-0.353020548820496,2.88753056526184 -"Sample_3_AACTCTTGTGCCTGTG",-3.80397248268127,8.86623191833496 -"Sample_3_AACTCTTTCAAACAAG",2.90502262115479,-10.5730800628662 -"Sample_3_AACTCTTTCCTCGCAT",-0.596313118934631,0.693148195743561 -"Sample_3_AACTGGTGTGCGGTAA",3.03787159919739,6.39949607849121 -"Sample_3_AACTTTCGTAGCGATG",2.31609535217285,-11.2041492462158 -"Sample_3_AAGACCTAGATGTCGG",4.37214136123657,6.16730690002441 -"Sample_3_AAGCCGCAGCTGAACG",2.85771131515503,-7.11361169815063 -"Sample_3_AAGCCGCGTTCGCGAC",1.77475368976593,-4.3726978302002 -"Sample_3_AAGGAGCCAATACGCT",2.27152299880981,8.27713012695312 -"Sample_3_AAGGCAGAGAGGTAGA",-0.115325920283794,6.82826995849609 -"Sample_3_AAGGCAGAGGAATCGC",-2.09306001663208,6.01981401443481 -"Sample_3_AAGGTTCGTCATATGC",-2.74299478530884,7.44491863250732 -"Sample_3_AAGGTTCTCAGTGCAT",2.86806869506836,6.82628774642944 -"Sample_3_AATCCAGCAAGCCCAC",1.47138679027557,-10.3342056274414 -"Sample_3_AATCCAGGTAAGCACG",-0.467231422662735,1.04238557815552 -"Sample_3_AATCCAGTCACAACGT",3.08766865730286,4.49657440185547 -"Sample_3_AATCGGTCATCACAAC",4.18903589248657,5.03767585754395 -"Sample_3_ACACCCTCAGCTGTGC",3.20319080352783,-9.2072639465332 -"Sample_3_ACACCCTCATACCATG",1.43962001800537,-7.34156942367554 -"Sample_3_ACACTGAAGCTGAACG",-1.79644191265106,6.05264282226562 -"Sample_3_ACACTGATCTTACCTA",1.27592265605927,-9.93198490142822 -"Sample_3_ACAGCCGAGCTACCGC",-0.363775879144669,-10.9093866348267 -"Sample_3_ACAGCTAAGGCAGTCA",0.247418686747551,-9.07986259460449 -"Sample_3_ACATACGAGAGAACAG",2.31117558479309,4.68346214294434 -"Sample_3_ACATACGAGGCTAGAC",1.36954128742218,2.36726665496826 -"Sample_3_ACATACGTCAACCAAC",4.19567441940308,-9.96702289581299 -"Sample_3_ACATCAGAGGTCGGAT",1.63427793979645,-7.89524793624878 -"Sample_3_ACATCAGGTCCGTTAA",3.42331194877625,-9.46917915344238 -"Sample_3_ACATGGTGTCGCTTCT",4.8958592414856,6.52756357192993 -"Sample_3_ACCAGTACACAACTGT",-0.959009647369385,6.33157157897949 -"Sample_3_ACCAGTATCCTCGCAT",4.76656293869019,6.09208631515503 -"Sample_3_ACCAGTATCTGAAAGA",1.78755044937134,-7.95785188674927 -"Sample_3_ACCGTAACAAGAAGAG",-1.90218698978424,8.56812763214111 -"Sample_3_ACCGTAACATTGAGCT",0.491711765527725,4.42844724655151 -"Sample_3_ACCGTAATCGCTTGTC",4.91420936584473,5.81713914871216 -"Sample_3_ACCTTTAAGCGATATA",-2.0538227558136,7.76728296279907 -"Sample_3_ACGAGCCAGACCTAGG",-1.86618566513062,9.17316436767578 -"Sample_3_ACGAGCCGTCTAGTCA",-0.877739310264587,-9.48667335510254 -"Sample_3_ACGATACAGAGACGAA",3.90209722518921,4.76808071136475 -"Sample_3_ACGATACCAATACGCT",4.20173645019531,-10.0311260223389 -"Sample_3_ACGATACGTCTGCCAG",-0.271163105964661,9.10234355926514 -"Sample_3_ACGATGTAGAGCTTCT",3.33252143859863,3.85839819908142 -"Sample_3_ACGATGTCATTCACTT",3.31918668746948,5.66721820831299 -"Sample_3_ACGATGTGTCATATCG",-0.0877501145005226,4.12014484405518 -"Sample_3_ACGCAGCCAAGCGCTC",3.70644879341125,5.83691930770874 -"Sample_3_ACGCCGAAGGTGTTAA",3.23652791976929,-9.91287994384766 -"Sample_3_ACGGAGAAGACAGAGA",-0.942581117153168,5.08545684814453 -"Sample_3_ACGGCCAGTTGATTCG",-1.43860626220703,1.24132108688354 -"Sample_3_ACGTCAAAGGTAGCTG",2.24929547309875,-9.02688980102539 -"Sample_3_ACGTCAATCGGAGCAA",3.85897278785706,-9.85359764099121 -"Sample_3_ACGTCAATCGGCATCG",3.825026512146,7.09366989135742 -"Sample_3_ACTATCTAGCTACCTA",1.46860766410828,-9.61578750610352 -"Sample_3_ACTGAACGTCAATGTC",-0.927719235420227,8.45430088043213 -"Sample_3_ACTGAACTCCCAACGG",-1.98489236831665,8.68005847930908 -"Sample_3_ACTGCTCCATTAGCCA",3.59808611869812,-8.7819652557373 -"Sample_3_ACTGTCCAGAAGAAGC",3.40765261650085,3.53222632408142 -"Sample_3_ACTGTCCGTACCGCTG",3.06533145904541,5.22821521759033 -"Sample_3_ACTTACTAGCCGGTAA",-0.906911849975586,6.39353132247925 -"Sample_3_ACTTACTGTACCGGCT",3.31644511222839,-10.3320016860962 -"Sample_3_ACTTGTTCAGATGGCA",3.1761462688446,-8.44988918304443 -"Sample_3_ACTTGTTTCACGATGT",1.82787525653839,8.43759250640869 -"Sample_3_ACTTTCACACAGTCGC",-1.79360401630402,8.95797443389893 -"Sample_3_ACTTTCAGTAAGGATT",3.54147958755493,3.55152058601379 -"Sample_3_ACTTTCATCAGTTGAC",-3.17644476890564,7.40374279022217 -"Sample_3_AGAATAGTCAACACCA",0.392506182193756,-9.71506690979004 -"Sample_3_AGAGCGAAGGTCATCT",2.75693893432617,7.17498111724854 -"Sample_3_AGAGCGAGTACCGGCT",2.5072934627533,-8.99595069885254 -"Sample_3_AGAGCTTAGGAGTTTA",1.02433598041534,-9.96888542175293 -"Sample_3_AGAGCTTTCAAGAAGT",1.96407246589661,-7.41730260848999 -"Sample_3_AGAGTGGTCCAGTAGT",-1.94333338737488,8.08909797668457 -"Sample_3_AGAGTGGTCCTTGACC",-2.25890016555786,6.59494066238403 -"Sample_3_AGCAGCCAGTTCCACA",-1.55633556842804,6.77548742294312 -"Sample_3_AGCCTAACAGTCTTCC",4.29767560958862,-8.62322807312012 -"Sample_3_AGCGTATAGTGCAAGC",4.75724792480469,6.28850173950195 -"Sample_3_AGCGTATGTGACGGTA",3.4590756893158,-8.95581436157227 -"Sample_3_AGCGTCGCAGCGAACA",2.84823393821716,7.77069330215454 -"Sample_3_AGCTCCTTCAGAGCTT",0.321358174085617,-11.3885087966919 -"Sample_3_AGCTCTCCATTTGCCC",2.44213700294495,-4.89895915985107 -"Sample_3_AGCTTGAAGACTAGAT",2.04340934753418,-8.77906894683838 -"Sample_3_AGCTTGAGTCTCACCT",-2.9917995929718,7.68621444702148 -"Sample_3_AGGCCACCAAGCGAGT",4.75440502166748,-8.95725727081299 -"Sample_3_AGGCCACCAAGTAATG",4.33867454528809,-9.3503885269165 -"Sample_3_AGGCCACCACGGCTAC",0.692148447036743,2.48933982849121 -"Sample_3_AGGCCACTCTCTTATG",1.55386888980865,-10.1784467697144 -"Sample_3_AGGCCGTGTAGAGGAA",2.35371875762939,7.12572956085205 -"Sample_3_AGGCCGTTCATCGATG",5.17563009262085,6.5079026222229 -"Sample_3_AGGGATGGTCTCAACA",-0.557101845741272,0.000981950666755438 -"Sample_3_AGGGTGACAGTAAGAT",2.42304444313049,-10.0403137207031 -"Sample_3_AGGTCATCACAGACTT",4.12796306610107,-9.71707916259766 -"Sample_3_AGGTCATTCGAGAGCA",0.281883507966995,-8.8474178314209 -"Sample_3_AGGTCCGCAGCTGTGC",2.15878820419312,-4.63976955413818 -"Sample_3_AGTAGTCCATGTCTCC",-0.952153503894806,0.466458380222321 -"Sample_3_AGTAGTCTCACTCCTG",1.12436473369598,4.61751317977905 -"Sample_3_AGTCTTTAGCCTCGTG",-1.03326427936554,2.17112874984741 -"Sample_3_AGTCTTTAGGATTCGG",0.433452248573303,6.23020648956299 -"Sample_3_AGTGAGGAGGCTCTTA",1.41923403739929,-9.63059139251709 -"Sample_3_AGTGAGGGTGCACCAC",2.18801426887512,8.10723400115967 -"Sample_3_AGTGAGGTCTGATACG",0.641865670681,3.3295533657074 -"Sample_3_AGTGTCATCCTTTCGG",-0.879697144031525,2.61135959625244 -"Sample_3_AGTGTCATCTGGGCCA",-1.07919895648956,-8.08193302154541 -"Sample_3_AGTGTCATCTTACCTA",-0.523324847221375,0.556499242782593 -"Sample_3_AGTTGGTTCGTCCAGG",4.3337025642395,5.50274419784546 -"Sample_3_ATAACGCCAGTCAGAG",0.267599552869797,1.97525382041931 -"Sample_3_ATAACGCGTAGCTTGT",-2.19165349006653,7.94540214538574 -"Sample_3_ATAAGAGGTGCCTGGT",3.88685607910156,-9.45316410064697 -"Sample_3_ATAGACCTCTTTACGT",2.35137796401978,-9.08162498474121 -"Sample_3_ATCACGAAGTGGAGTC",-0.253079146146774,3.85497951507568 -"Sample_3_ATCACGATCAGCAACT",-2.90073919296265,6.94681215286255 -"Sample_3_ATCATCTCAACACGCC",-2.76819610595703,6.78422117233276 -"Sample_3_ATCATGGAGGTGATAT",-0.631397187709808,-10.2288970947266 -"Sample_3_ATCATGGCAGCCAGAA",-9.78701114654541,-2.87741994857788 -"Sample_3_ATCCACCAGTAGATGT",-0.832554996013641,-9.55251884460449 -"Sample_3_ATCCACCCATCGATGT",-9.59557247161865,-2.68442964553833 -"Sample_3_ATCCACCGTAACGACG",1.49786484241486,3.0508017539978 -"Sample_3_ATCCGAACACTAGTAC",-0.311287313699722,-10.2675170898438 -"Sample_3_ATCCGAAGTACACCGC",1.64129877090454,-8.19408512115479 -"Sample_3_ATCGAGTCACAACTGT",3.61128902435303,6.43721055984497 -"Sample_3_ATCTACTCACGAGGTA",0.265942722558975,-9.38722133636475 -"Sample_3_ATCTACTCATGGGACA",0.168922677636147,6.2771143913269 -"Sample_3_ATCTACTTCGGCCGAT",-1.17216503620148,8.1036205291748 -"Sample_3_ATCTGCCAGCGCTCCA",2.5389769077301,5.99887466430664 -"Sample_3_ATCTGCCAGTGACATA",3.21087288856506,-8.87726879119873 -"Sample_3_ATCTGCCAGTTGAGTA",-0.314184010028839,-9.78339862823486 -"Sample_3_ATCTGCCCACTTAACG",1.04748630523682,5.30796718597412 -"Sample_3_ATCTGCCCATCCCACT",1.46473073959351,-7.88158321380615 -"Sample_3_ATCTGCCGTCTCTTTA",-0.522031188011169,3.0314359664917 -"Sample_3_ATCTGCCGTTAGTGGG",0.694676697254181,8.461501121521 -"Sample_3_ATCTGCCTCGCTTAGA",0.524760007858276,3.87449336051941 -"Sample_3_ATGAGGGGTACCGTAT",-1.50852835178375,0.15420638024807 -"Sample_3_ATGCGATGTTCTGTTT",3.06881260871887,5.77031707763672 -"Sample_3_ATGGGAGAGTTGTAGA",2.93261480331421,-6.99224948883057 -"Sample_3_ATGTGTGAGCATCATC",1.0949045419693,-10.6816530227661 -"Sample_3_ATGTGTGAGGTCATCT",0.928322017192841,-8.96856498718262 -"Sample_3_ATGTGTGGTGTATGGG",3.27127385139465,4.23137331008911 -"Sample_3_ATTACTCAGGTCATCT",-0.542717158794403,-10.6757259368896 -"Sample_3_ATTACTCGTGGAAAGA",0.759462416172028,1.74820959568024 -"Sample_3_ATTATCCAGTAGCGGT",0.22003972530365,-10.4020357131958 -"Sample_3_ATTATCCCACCCATGG",5.02305126190186,5.63511800765991 -"Sample_3_ATTCTACCAAGCGATG",-0.288484752178192,3.67431998252869 -"Sample_3_ATTCTACCATTAACCG",1.10585141181946,5.92396020889282 -"Sample_3_ATTCTACGTCTAGTCA",1.01367950439453,-8.38042068481445 -"Sample_3_ATTCTACGTGGTAACG",-1.57673525810242,0.26592555642128 -"Sample_3_ATTGGACCAGCGTTCG",-1.78924512863159,0.794165968894958 -"Sample_3_ATTGGACCAGCTTCGG",1.53935647010803,2.5507185459137 -"Sample_3_ATTGGACGTAGCGTGA",-2.56573247909546,7.91956233978271 -"Sample_3_ATTGGACGTCGCATCG",-0.481060236692429,-7.49798011779785 -"Sample_3_ATTGGTGAGCCCGAAA",3.82445645332336,7.98057985305786 -"Sample_3_CAACCAATCCTTGGTC",1.69249391555786,-10.6827373504639 -"Sample_3_CAACTAGCATGGTAGG",0.458320200443268,-11.1998596191406 -"Sample_3_CAAGAAAAGACTAAGT",-0.153314962983131,-9.97834491729736 -"Sample_3_CAAGAAAAGTTAGCGG",-0.872168838977814,-9.41042518615723 -"Sample_3_CAAGAAATCTATGTGG",1.59762406349182,-9.20169734954834 -"Sample_3_CAAGATCGTTACTGAC",-0.548183560371399,0.619833946228027 -"Sample_3_CAAGATCGTTCAACCA",3.98467016220093,-9.21397590637207 -"Sample_3_CAAGATCGTTCAGCGC",3.08713340759277,6.41563653945923 -"Sample_3_CAAGATCGTTTAGGAA",-2.74132370948792,8.52921390533447 -"Sample_3_CAAGTTGGTAACGCGA",-2.2722954750061,6.0800986289978 -"Sample_3_CACAAACAGTTCGATC",3.12134909629822,4.57727432250977 -"Sample_3_CACACAACAATCACAC",-2.29870438575745,7.79659938812256 -"Sample_3_CACACAAGTCGCGTGT",-0.401744604110718,6.23093605041504 -"Sample_3_CACACCTCAAACCTAC",0.000822234665974975,-11.2283906936646 -"Sample_3_CACACCTTCCCATTTA",3.43436002731323,-9.8281946182251 -"Sample_3_CACACTCCAGTACACT",0.489936143159866,-9.53086948394775 -"Sample_3_CACACTCCATGTAAGA",-1.24106502532959,9.7615909576416 -"Sample_3_CACACTCTCTAACCGA",1.17571580410004,1.42682671546936 -"Sample_3_CACAGGCGTGGGTATG",1.62724792957306,-11.1015548706055 -"Sample_3_CACAGTAAGTGACATA",-1.72008860111237,0.82255631685257 -"Sample_3_CACATAGCAAGCCGCT",-2.22038578987122,9.18139553070068 -"Sample_3_CACATAGGTAGCAAAT",0.00108729407656938,-11.0767421722412 -"Sample_3_CACATAGTCGCGTTTC",-1.98076450824738,7.02449512481689 -"Sample_3_CACATTTCATTGCGGC",-0.272789716720581,7.47581624984741 -"Sample_3_CACATTTTCCGTCATC",-1.82262456417084,7.82293939590454 -"Sample_3_CACCACTCACTCGACG",0.097664900124073,1.85538160800934 -"Sample_3_CACCACTTCATACGGT",1.17486882209778,-8.28981876373291 -"Sample_3_CACCACTTCCGCAAGC",1.86311399936676,-9.52130508422852 -"Sample_3_CACCACTTCTTGAGAC",1.50072681903839,-7.99178457260132 -"Sample_3_CACCAGGCACCTCGTT",-1.77021992206573,0.216466546058655 -"Sample_3_CACCAGGTCAAGGTAA",1.87831354141235,-5.23000288009644 -"Sample_3_CACCTTGTCTCTAAGG",0.480559349060059,5.96506643295288 -"Sample_3_CAGAGAGCATCACGTA",-0.0417132638394833,5.70233678817749 -"Sample_3_CAGATCAAGATATGGT",4.79084634780884,4.94593381881714 -"Sample_3_CAGCAGCGTCGCCATG",-0.373294621706009,8.56947708129883 -"Sample_3_CAGCAGCTCGGAATCT",-9.67610836029053,-2.76709151268005 -"Sample_3_CAGCAGCTCTGTCCGT",4.86126279830933,6.23032569885254 -"Sample_3_CAGCCGAAGCGACGTA",1.44806039333344,-8.60355377197266 -"Sample_3_CAGCGACAGTAGCGGT",1.86695885658264,-7.30396175384521 -"Sample_3_CAGCTGGAGTGTACGG",2.54253530502319,-9.10615921020508 -"Sample_3_CAGCTGGTCTTTCCTC",0.628168404102325,-10.7242107391357 -"Sample_3_CAGTAACCATCTCGCT",-1.07628655433655,2.66564011573792 -"Sample_3_CAGTAACTCTGTCCGT",-1.0053699016571,9.11256885528564 -"Sample_3_CAGTCCTAGACAAGCC",1.19705903530121,-7.57151317596436 -"Sample_3_CAGTCCTAGATCTGCT",-2.49235844612122,7.89328193664551 -"Sample_3_CAGTCCTAGTAGATGT",3.67368483543396,4.08617401123047 -"Sample_3_CAGTCCTCAGGCGATA",3.56921362876892,6.08867025375366 -"Sample_3_CAGTCCTGTTCAGCGC",3.23619914054871,4.30176401138306 -"Sample_3_CAGTCCTTCTCTTGAT",3.9048273563385,4.57641935348511 -"Sample_3_CATATGGTCTGTGCAA",2.19548916816711,-4.80521583557129 -"Sample_3_CATATTCCAGTGACAG",-2.54221057891846,6.61751747131348 -"Sample_3_CATATTCGTAAATACG",2.79560446739197,8.09181594848633 -"Sample_3_CATCAAGAGGTGTTAA",0.0237480234354734,5.25717210769653 -"Sample_3_CATCAAGCACGACGAA",5.16888856887817,6.38769674301147 -"Sample_3_CATCAAGCAGGCAGTA",3.34976434707642,-9.60735702514648 -"Sample_3_CATCAAGCATGTTGAC",0.865162372589111,2.66041231155396 -"Sample_3_CATCAAGGTCTCCACT",3.80285286903381,-9.46530532836914 -"Sample_3_CATCAGAAGAAACCAT",-0.108618125319481,5.64721250534058 -"Sample_3_CATCAGACACCCTATC",3.55324959754944,5.53215789794922 -"Sample_3_CATCAGAGTCCATGAT",0.719156742095947,-9.264817237854 -"Sample_3_CATCAGAGTCCGAAGA",-1.26449108123779,-0.0703477635979652 -"Sample_3_CATCCACGTCCCTTGT",-0.0893773585557938,5.16374063491821 -"Sample_3_CATCCACGTGCATCTA",0.225228324532509,6.56404638290405 -"Sample_3_CATCCACTCTGAGGGA",2.76578879356384,-9.26353168487549 -"Sample_3_CATCGAAAGGAATCGC",-1.002925157547,6.89066600799561 -"Sample_3_CATCGAACAGGCTGAA",-2.78314805030823,7.16065835952759 -"Sample_3_CATCGAACAGGGAGAG",3.08310484886169,6.80535650253296 -"Sample_3_CATCGGGCAAAGGTGC",0.0224177334457636,-11.1652936935425 -"Sample_3_CATGACAAGTGAACGC",1.86130201816559,-4.66304636001587 -"Sample_3_CATGACACACAGACTT",1.82601058483124,-10.3727798461914 -"Sample_3_CATGACACACCGTTGG",3.36838746070862,3.73187804222107 -"Sample_3_CATGACACAGCTATTG",2.80301880836487,-9.77646827697754 -"Sample_3_CATGACAGTCTCACCT",3.15170407295227,4.17508029937744 -"Sample_3_CATGCCTCAATGAAAC",1.19016015529633,-9.58657073974609 -"Sample_3_CATGGCGAGTGTGGCA",2.65015268325806,-7.357421875 -"Sample_3_CATGGCGCACGAGGTA",2.24566435813904,-7.97040796279907 -"Sample_3_CATGGCGGTACAGTGG",0.638587772846222,-8.83823490142822 -"Sample_3_CATTATCAGGGTTCCC",2.30296969413757,-10.347710609436 -"Sample_3_CATTATCCACAGCCCA",-3.81222486495972,8.67202568054199 -"Sample_3_CATTATCGTGGTCTCG",2.40660238265991,6.09558629989624 -"Sample_3_CCAATCCGTCTCTTAT",-0.821308314800262,9.7714204788208 -"Sample_3_CCAATCCTCTCATTCA",-2.46389770507812,8.93087291717529 -"Sample_3_CCACCTATCTTGCCGT",0.685720324516296,-11.1230630874634 -"Sample_3_CCACGGACAATCACAC",3.35874199867249,-9.98855495452881 -"Sample_3_CCACGGATCAGGCAAG",2.3634786605835,-4.92572546005249 -"Sample_3_CCACTACAGTGGACGT",3.96252465248108,5.62788152694702 -"Sample_3_CCAGCGAGTTCGTTGA",4.59052658081055,7.05620956420898 -"Sample_3_CCATTCGCAAGCCATT",-2.55693817138672,8.77912425994873 -"Sample_3_CCCAATCTCAACACCA",3.81389498710632,6.97400760650635 -"Sample_3_CCCTCCTAGTAGCCGA",2.84018516540527,-10.0352830886841 -"Sample_3_CCCTCCTGTTGTACAC",-2.68330097198486,8.86049938201904 -"Sample_3_CCGGTAGAGTTACGGG",-2.09695053100586,8.95217418670654 -"Sample_3_CCGGTAGTCGCCTGAG",-0.367690682411194,-10.0292072296143 -"Sample_3_CCGTACTCAGGGTATG",4.02957630157471,5.78465604782104 -"Sample_3_CCGTACTTCTACTATC",2.31579160690308,-10.2220401763916 -"Sample_3_CCGTTCAAGCCCGAAA",0.859101414680481,-8.84210968017578 -"Sample_3_CCTAAAGAGCGATTCT",3.73600697517395,4.73077774047852 -"Sample_3_CCTAAAGCAAGTAATG",3.32959794998169,6.24429893493652 -"Sample_3_CCTACACAGCAAATCA",-9.63324928283691,-2.72319674491882 -"Sample_3_CCTACACTCTTTACGT",2.92660903930664,-10.9033441543579 -"Sample_3_CCTACCACATCGGAAG",-0.96126800775528,0.313237071037292 -"Sample_3_CCTAGCTAGAGGTACC",-2.62778067588806,7.36778020858765 -"Sample_3_CCTATTATCAAACCAC",1.7908627986908,7.81265115737915 -"Sample_3_CCTCTGAGTAAGAGGA",-0.853547751903534,0.204179093241692 -"Sample_3_CCTCTGAGTTATTCTC",3.39320635795593,6.30593395233154 -"Sample_3_CCTCTGATCAGCCTAA",0.776785492897034,-8.35674667358398 -"Sample_3_CCTTACGAGCACCGCT",2.23433780670166,-9.48871326446533 -"Sample_3_CCTTACGGTCGCATCG",1.74165117740631,-6.97130298614502 -"Sample_3_CCTTCCCCAAACCTAC",-0.571065306663513,9.2332124710083 -"Sample_3_CCTTCCCTCACTGGGC",-2.01559519767761,6.93144655227661 -"Sample_3_CCTTCGAAGCGTTTAC",3.59388542175293,-8.52132892608643 -"Sample_3_CCTTCGACAAGAAGAG",3.77610898017883,6.01475095748901 -"Sample_3_CGAACATAGTCAAGCG",1.85820877552032,-6.79544448852539 -"Sample_3_CGAACATCATCAGTCA",4.08008289337158,3.94199824333191 -"Sample_3_CGAATGTGTCATTAGC",1.28373539447784,8.49672985076904 -"Sample_3_CGACCTTAGCACCGCT",-0.218284904956818,5.34520196914673 -"Sample_3_CGACCTTAGTTGTAGA",-9.60087871551514,-2.69006514549255 -"Sample_3_CGACCTTTCAAGCCTA",-1.30314373970032,0.732021391391754 -"Sample_3_CGACTTCTCATAGCAC",3.05998635292053,-8.93478679656982 -"Sample_3_CGAGCACAGACGCAAC",1.58438503742218,-10.4225978851318 -"Sample_3_CGAGCACAGCTCAACT",5.35456943511963,6.52657556533813 -"Sample_3_CGAGCACAGGCGTACA",-2.93054699897766,7.7348747253418 -"Sample_3_CGAGCACTCTGCAAGT",-2.78346228599548,7.62291145324707 -"Sample_3_CGAGCCACATGCAACT",-2.52008771896362,6.63480949401855 -"Sample_3_CGAGCCATCGTGGTCG",0.33092001080513,3.89140391349792 -"Sample_3_CGATCGGAGACGCTTT",-0.988581717014313,1.12739515304565 -"Sample_3_CGATCGGGTCCCTTGT",-0.713656604290009,1.0623391866684 -"Sample_3_CGATGGCTCGGTCCGA",-0.558200538158417,9.59815406799316 -"Sample_3_CGATGTAAGGACATTA",2.87524271011353,5.50127363204956 -"Sample_3_CGATTGAGTGGTACAG",2.30147433280945,-7.38990926742554 -"Sample_3_CGCCAAGAGACCACGA",-1.56619584560394,0.278328686952591 -"Sample_3_CGCCAAGGTGTCCTCT",2.79597401618958,-11.0225524902344 -"Sample_3_CGCGGTAAGGCATGGT",3.82421803474426,-8.77615737915039 -"Sample_3_CGCGGTAAGTATCGAA",1.30107069015503,-10.5036010742188 -"Sample_3_CGCGGTACACAGGCCT",-0.528827369213104,-9.76459884643555 -"Sample_3_CGCGGTATCAACGCTA",1.30671715736389,-8.08608150482178 -"Sample_3_CGCGGTATCTCTGCTG",-0.376318126916885,4.47065544128418 -"Sample_3_CGCGTTTTCCAGATCA",1.56143152713776,2.37268042564392 -"Sample_3_CGCTATCCACTGTCGG",-2.53819298744202,7.20212411880493 -"Sample_3_CGCTATCGTGCAGACA",1.58960592746735,-10.3353519439697 -"Sample_3_CGCTATCTCGCCATAA",0.65632688999176,-8.05765819549561 -"Sample_3_CGCTATCTCTGTACGA",0.521823823451996,4.67654085159302 -"Sample_3_CGCTGGATCCTAGGGC",-0.405551493167877,7.01514196395874 -"Sample_3_CGCTTCACATTTCAGG",3.41797614097595,4.70816135406494 -"Sample_3_CGGACACAGCAGCGTA",-1.64943540096283,9.73821830749512 -"Sample_3_CGGACACAGGTGCTTT",0.425260245800018,-8.56314086914062 -"Sample_3_CGGACACTCCTCGCAT",-1.02939283847809,2.28370523452759 -"Sample_3_CGGACGTTCAGGTTCA",3.18816208839417,5.31182241439819 -"Sample_3_CGGACTGTCTCTGAGA",-0.602764844894409,7.33610820770264 -"Sample_3_CGGAGCTCAAACTGTC",3.41487598419189,3.69396209716797 -"Sample_3_CGGAGCTGTACCGTTA",4.22595834732056,6.79280614852905 -"Sample_3_CGGAGTCTCAGGCCCA",0.326508522033691,-10.2638292312622 -"Sample_3_CGGCTAGCAATCTGCA",2.56037259101868,-7.33620738983154 -"Sample_3_CGTAGCGCAAACAACA",3.26266860961914,-7.18885374069214 -"Sample_3_CGTAGCGTCAGCACAT",2.5395781993866,-8.31676197052002 -"Sample_3_CGTAGCGTCCTAGAAC",1.98709738254547,-4.77577114105225 -"Sample_3_CGTAGGCGTCTGATTG",-0.619279623031616,7.50405025482178 -"Sample_3_CGTCAGGCAAGCTGTT",0.0753060579299927,-9.3048095703125 -"Sample_3_CGTCAGGTCAAGGTAA",-2.63144469261169,9.02789783477783 -"Sample_3_CGTCCATAGCTATGCT",2.21483564376831,-8.33411884307861 -"Sample_3_CGTCCATCACCCTATC",3.28497958183289,4.63322687149048 -"Sample_3_CGTCTACCATAGGATA",-1.21060514450073,8.77951812744141 -"Sample_3_CGTGAGCCATTCTTAC",2.07828450202942,7.81467628479004 -"Sample_3_CGTGAGCTCAATCTCT",-9.6484260559082,-2.73944091796875 -"Sample_3_CGTTGGGTCAGAGACG",4.37944793701172,6.85676574707031 -"Sample_3_CGTTGGGTCCTTGCCA",-1.06968486309052,5.78015422821045 -"Sample_3_CTAACTTAGCCCAGCT",3.89147472381592,5.43658208847046 -"Sample_3_CTAAGACAGTGTCTCA",0.886093974113464,-10.2052412033081 -"Sample_3_CTAAGACCAGATCGGA",2.13621091842651,8.08328437805176 -"Sample_3_CTAAGACGTCGGATCC",1.8533616065979,-4.64121246337891 -"Sample_3_CTAAGACTCCACTGGG",1.71803915500641,-5.07418918609619 -"Sample_3_CTAATGGCACAGACTT",-1.74541997909546,7.42806529998779 -"Sample_3_CTAATGGGTTCAGGCC",1.94342625141144,-7.20945644378662 -"Sample_3_CTACACCCAACGCACC",2.32463836669922,-8.76958465576172 -"Sample_3_CTACACCGTTCAGCGC",2.79750180244446,-9.97958660125732 -"Sample_3_CTACACCTCGGTTCGG",2.58090949058533,4.74751424789429 -"Sample_3_CTACATTCATGCCCGA",2.86111831665039,-8.59322357177734 -"Sample_3_CTACATTGTTTGGCGC",3.55541372299194,-7.97768497467041 -"Sample_3_CTACCCAAGGTGCACA",2.49249505996704,-5.08101177215576 -"Sample_3_CTACCCACACGACTCG",2.29235577583313,-6.9712700843811 -"Sample_3_CTACCCACAGCGATCC",-0.000262056826613843,-11.03053855896 -"Sample_3_CTACCCATCCAAATGC",-0.569362163543701,-7.32235813140869 -"Sample_3_CTACGTCAGAGCTGCA",-1.63184547424316,8.66928482055664 -"Sample_3_CTACGTCAGGCGATAC",3.45724582672119,6.82024717330933 -"Sample_3_CTACGTCGTCTCAACA",1.98126339912415,-8.65328121185303 -"Sample_3_CTACGTCTCGTCGTTC",1.53908729553223,-8.81343269348145 -"Sample_3_CTAGAGTCAATCTACG",1.1817626953125,-9.61760711669922 -"Sample_3_CTAGAGTCAGGAATCG",-0.525467693805695,0.754772186279297 -"Sample_3_CTAGAGTTCAACTCTT",3.66063380241394,5.87924528121948 -"Sample_3_CTAGTGACAGACGCTC",-1.25439083576202,6.63366413116455 -"Sample_3_CTAGTGACAGCGTAAG",-0.876407980918884,5.1278133392334 -"Sample_3_CTAGTGACATCTATGG",0.597473740577698,6.62446403503418 -"Sample_3_CTAGTGATCCTTGACC",-1.79250776767731,9.16676998138428 -"Sample_3_CTCACACTCACATACG",2.78831887245178,5.32570505142212 -"Sample_3_CTCAGAATCGGAAATA",-1.4486620426178,1.56519067287445 -"Sample_3_CTCATTAAGTCGTTTG",-0.745834171772003,8.1170825958252 -"Sample_3_CTCCTAGTCGGCGCTA",-1.25100517272949,0.948258221149445 -"Sample_3_CTCGAAAAGAACAATC",0.675923109054565,5.43063116073608 -"Sample_3_CTCGAAAAGACGACGT",-0.96488356590271,3.43763613700867 -"Sample_3_CTCGAAACACAGCGTC",4.64035177230835,-9.0172643661499 -"Sample_3_CTCGGGAGTTCGGCAC",2.55267524719238,6.29413318634033 -"Sample_3_CTCGTCACACACTGCG",1.04794156551361,-9.85681438446045 -"Sample_3_CTCGTCACACAGGAGT",0.54956579208374,-9.68562030792236 -"Sample_3_CTCGTCAGTTCCACGG",4.49196481704712,4.40223598480225 -"Sample_3_CTCTAATAGTCCGTAT",0.822036445140839,-10.6184358596802 -"Sample_3_CTCTAATAGTGGGTTG",4.19224500656128,4.79536104202271 -"Sample_3_CTCTACGAGGTGCACA",4.00816011428833,4.45665884017944 -"Sample_3_CTCTACGTCGAACGGA",4.73598289489746,6.81779766082764 -"Sample_3_CTCTGGTAGCGATATA",3.63283443450928,4.30411624908447 -"Sample_3_CTCTGGTAGGGAACGG",-0.711273550987244,8.10572147369385 -"Sample_3_CTCTGGTAGGTCGGAT",3.17550611495972,6.43571043014526 -"Sample_3_CTCTGGTCACTGCCAG",3.45649838447571,3.73452925682068 -"Sample_3_CTGAAACGTGCCTGTG",0.361612498760223,-8.25915050506592 -"Sample_3_CTGAAGTCACGAAGCA",-2.00648164749146,7.78593730926514 -"Sample_3_CTGAAGTTCCGCAAGC",2.18527150154114,-8.8297643661499 -"Sample_3_CTGAAGTTCGCAAACT",-2.62209677696228,8.13439750671387 -"Sample_3_CTGATAGAGCGTCAAG",-0.260763376951218,9.59890747070312 -"Sample_3_CTGATAGTCGAATCCA",1.23334419727325,8.5150728225708 -"Sample_3_CTGATCCTCATAACCG",5.11104297637939,6.06387662887573 -"Sample_3_CTGATCCTCGCAAGCC",-2.69808650016785,8.3016357421875 -"Sample_3_CTGATCCTCTACTATC",-1.57070219516754,6.87515306472778 -"Sample_3_CTGCCTAAGAAGGTGA",0.679190337657928,-9.94143390655518 -"Sample_3_CTGCCTAAGACAGAGA",0.915455222129822,2.87672543525696 -"Sample_3_CTGCCTACAGACAGGT",4.14146041870117,-9.93666744232178 -"Sample_3_CTGCTGTCAACGATCT",-0.000815199920907617,-7.89334583282471 -"Sample_3_CTGGTCTCAACCGCCA",4.63996553421021,4.63255786895752 -"Sample_3_CTGGTCTGTATCAGTC",-0.0864680558443069,-10.0939950942993 -"Sample_3_CTGGTCTGTTGACGTT",1.64510440826416,7.94259977340698 -"Sample_3_CTGGTCTGTTGTCGCG",-1.62071657180786,8.71461963653564 -"Sample_3_CTGTGCTCACATGGGA",-0.766901791095734,9.20287322998047 -"Sample_3_CTGTGCTCACGGCCAT",2.66919469833374,4.87982177734375 -"Sample_3_CTGTGCTTCAGCGATT",3.9865939617157,6.50607252120972 -"Sample_3_CTTAACTGTGGACGAT",-1.12898600101471,8.20018005371094 -"Sample_3_CTTACCGAGCGTCAAG",2.59037041664124,6.77875947952271 -"Sample_3_CTTAGGAAGCCAACAG",-1.72562718391418,8.09200954437256 -"Sample_3_CTTCTCTAGTATCGAA",3.01980757713318,5.15665864944458 -"Sample_3_CTTCTCTAGTGCGATG",1.4551203250885,-10.7393026351929 -"Sample_3_CTTTGCGTCGGGAGTA",3.50502991676331,7.63574504852295 -"Sample_3_GAAACTCAGTACGTTC",0.00674171047285199,3.12543368339539 -"Sample_3_GAAACTCGTCTGCCAG",1.45835280418396,1.71697854995728 -"Sample_3_GAAATGAAGAAGGGTA",3.8746325969696,4.60850095748901 -"Sample_3_GAAATGAGTGTTAAGA",3.95319557189941,6.11653804779053 -"Sample_3_GAACATCTCCTTGGTC",0.201914474368095,8.99405384063721 -"Sample_3_GAACCTACAACACCCG",-2.91090536117554,8.5191068649292 -"Sample_3_GAACGGAAGCAAATCA",2.62873125076294,5.96581315994263 -"Sample_3_GAAGCAGGTTCAGGCC",2.45716977119446,-10.4699020385742 -"Sample_3_GAAGCAGTCTGTGCAA",2.86909556388855,-10.628173828125 -"Sample_3_GAATAAGAGCGATTCT",3.11325097084045,4.77268218994141 -"Sample_3_GAATAAGAGTTGAGAT",0.281231075525284,-9.19375801086426 -"Sample_3_GAATAAGGTACCGTTA",-1.33329880237579,9.55158996582031 -"Sample_3_GAATAAGTCCACTCCA",0.357625365257263,-9.79894638061523 -"Sample_3_GACACGCCACGACGAA",1.6991970539093,6.34882926940918 -"Sample_3_GACACGCGTAACGTTC",-0.400865107774734,4.29860305786133 -"Sample_3_GACAGAGAGGCGTACA",1.65135204792023,-6.3402624130249 -"Sample_3_GACAGAGTCGAGAACG",-1.25732982158661,0.360542058944702 -"Sample_3_GACCAATGTGAGCGAT",-0.0230451580137014,3.36191582679749 -"Sample_3_GACCTGGCACTTCGAA",2.26678371429443,-9.71443367004395 -"Sample_3_GACCTGGCAGTAAGAT",-0.733364462852478,-0.0744711831212044 -"Sample_3_GACGGCTGTTGATTGC",0.0656752064824104,-10.1915435791016 -"Sample_3_GACGGCTGTTTGGGCC",3.64528775215149,8.17752552032471 -"Sample_3_GACGGCTTCAACGCTA",3.58273720741272,4.83066415786743 -"Sample_3_GACGTGCTCACCATAG",-1.2913783788681,0.387922555208206 -"Sample_3_GACGTTAAGAATAGGG",1.29515051841736,-8.8024263381958 -"Sample_3_GACGTTAAGTGTACGG",-1.19258511066437,7.63893699645996 -"Sample_3_GACTACAAGTACACCT",3.72931170463562,7.45560503005981 -"Sample_3_GACTACATCGGAATCT",1.95430636405945,-9.1561393737793 -"Sample_3_GACTGCGGTTCGGCAC",5.00520706176758,5.89709711074829 -"Sample_3_GACTGCGTCAGTTGAC",-2.29703307151794,7.52107763290405 -"Sample_3_GACTGCGTCTGTACGA",-3.2152361869812,7.74477863311768 -"Sample_3_GAGCAGAAGTGCTGCC",-2.97077965736389,8.75049018859863 -"Sample_3_GAGCAGAGTCTTGATG",0.209139749407768,-8.44672966003418 -"Sample_3_GAGGTGAGTCCTAGCG",-3.88871145248413,8.78944683074951 -"Sample_3_GAGGTGATCCCTAACC",4.42418956756592,-8.29409217834473 -"Sample_3_GATCAGTAGAGTTGGC",-1.67353904247284,8.26211738586426 -"Sample_3_GATCAGTAGTAGCGGT",-2.82380127906799,7.92816162109375 -"Sample_3_GATCAGTCAAAGCGGT",-1.13361001014709,8.07742881774902 -"Sample_3_GATCAGTTCCGAGCCA",0.552249789237976,-9.32385063171387 -"Sample_3_GATCAGTTCCTCGCAT",0.18181499838829,-8.91170883178711 -"Sample_3_GATCGATGTTCCCGAG",-1.40146863460541,7.86016750335693 -"Sample_3_GATCGATTCCACGCAG",2.06132817268372,-9.73900318145752 -"Sample_3_GATCGCGAGACCTTTG",3.70295929908752,4.33570718765259 -"Sample_3_GATCGCGCAAGAAAGG",2.39136147499084,-5.1098484992981 -"Sample_3_GATCGCGGTCAACTGT",4.37758731842041,-8.33300495147705 -"Sample_3_GATCGCGTCAAGGTAA",0.403240233659744,-7.25218534469604 -"Sample_3_GATCGTAAGGTACTCT",-2.07549667358398,8.38550853729248 -"Sample_3_GATGAAAAGACACTAA",-2.0687084197998,8.46674728393555 -"Sample_3_GATGAAATCGTCTGAA",0.628028690814972,-9.84457015991211 -"Sample_3_GATGAGGTCCTAGGGC",2.82471179962158,-7.63779401779175 -"Sample_3_GATGCTAAGTCCCACG",-2.67319893836975,7.82335233688354 -"Sample_3_GATGCTACAAAGGAAG",3.54630279541016,4.15844583511353 -"Sample_3_GATGCTAGTAGAGGAA",3.61978054046631,5.46509218215942 -"Sample_3_GATTCAGAGACAGGCT",0.541058480739594,2.35239124298096 -"Sample_3_GCAAACTCATCCCATC",0.180628418922424,1.79342031478882 -"Sample_3_GCAAACTGTGGACGAT",2.75032234191895,-7.48677253723145 -"Sample_3_GCAATCAAGTAGGCCA",3.80446290969849,-9.58636665344238 -"Sample_3_GCAATCAGTCTAGTCA",3.31701445579529,-8.01626777648926 -"Sample_3_GCAATCATCACCCTCA",1.27079272270203,-8.80252838134766 -"Sample_3_GCAGTTAGTTCAGCGC",-0.69513076543808,9.34218311309814 -"Sample_3_GCATACATCCTGCCAT",4.18068552017212,4.05074119567871 -"Sample_3_GCATACATCTTTAGGG",2.54883313179016,-10.1527528762817 -"Sample_3_GCATGCGGTCCCTTGT",0.589599907398224,5.54486227035522 -"Sample_3_GCATGTAAGACGCACA",2.05238628387451,6.30721235275269 -"Sample_3_GCATGTAAGAGTAATC",-1.73066627979279,1.21641004085541 -"Sample_3_GCATGTACACCTCGTT",-0.856661796569824,-0.0631516799330711 -"Sample_3_GCCTCTACAAACTGCT",1.82637798786163,2.88228917121887 -"Sample_3_GCGACCACATGCCCGA",4.43061447143555,4.72671699523926 -"Sample_3_GCGAGAAAGCTACCGC",-2.03265714645386,8.80957126617432 -"Sample_3_GCGAGAACAAGGCTCC",2.28752303123474,6.66260099411011 -"Sample_3_GCGAGAACACCAGTTA",0.642812252044678,-8.64038753509521 -"Sample_3_GCGCAACAGGGTGTGT",-0.842077255249023,4.96324491500854 -"Sample_3_GCGCAACCACCCAGTG",1.51763236522675,-8.33842754364014 -"Sample_3_GCGCAACTCCTATGTT",2.06628155708313,7.48973083496094 -"Sample_3_GCGCAACTCTTGCATT",0.481996268033981,-7.73344039916992 -"Sample_3_GCGCAGTTCCGTCATC",-0.381986886262894,-0.131981939077377 -"Sample_3_GCGCCAACACCGTTGG",-3.88529419898987,8.97266101837158 -"Sample_3_GCGCGATTCCGTAGTA",2.69661617279053,-8.66417598724365 -"Sample_3_GCGGGTTGTGCGCTTG",-1.11262214183807,5.31766939163208 -"Sample_3_GCTCCTAGTAGAAGGA",-0.252534300088882,0.621051430702209 -"Sample_3_GCTCTGTCAAGTCTAC",-0.126354888081551,-9.7888126373291 -"Sample_3_GCTGCAGAGTGGGATC",3.86400008201599,7.3141622543335 -"Sample_3_GCTGCAGCACCACCAG",1.1487762928009,-10.9851970672607 -"Sample_3_GCTGCAGGTCGATTGT",0.697325587272644,-10.0958766937256 -"Sample_3_GCTGCAGTCCTTTACA",2.13161373138428,2.47667670249939 -"Sample_3_GCTGCAGTCGGAGGTA",3.9808566570282,-8.55561351776123 -"Sample_3_GCTGCGAAGGCTACGA",-0.458487302064896,-0.133442103862762 -"Sample_3_GCTGCGAGTGTCAATC",-0.920918345451355,9.20080280303955 -"Sample_3_GCTGCGATCTGATACG",0.949126243591309,-8.46565246582031 -"Sample_3_GCTGGGTCATATGCTG",-0.39779606461525,-10.2490644454956 -"Sample_3_GCTTCCACACATCCGG",4.84063911437988,4.49413251876831 -"Sample_3_GCTTCCACATCTATGG",-0.670076906681061,-9.42746543884277 -"Sample_3_GCTTCCATCTCTGTCG",0.465932548046112,4.29997301101685 -"Sample_3_GCTTGAACAGTATAAG",4.10181093215942,6.11099433898926 -"Sample_3_GCTTGAATCTAACCGA",0.00235395273193717,-8.32886695861816 -"Sample_3_GGAAAGCTCCTTGCCA",1.84809458255768,-8.15682697296143 -"Sample_3_GGAACTTCAGATGGCA",4.02384996414185,4.82018566131592 -"Sample_3_GGAATAACAAATTGCC",4.19135332107544,-8.32954788208008 -"Sample_3_GGAATAACAGTTCATG",-2.55092263221741,8.75176525115967 -"Sample_3_GGACATTCACTTCGAA",2.78548836708069,-9.63747501373291 -"Sample_3_GGAGCAACAAGCGCTC",2.74655556678772,6.07033061981201 -"Sample_3_GGAGCAACATCTCCCA",0.0567693822085857,6.02639150619507 -"Sample_3_GGAGCAATCGCATGGC",-0.478161692619324,-7.05643606185913 -"Sample_3_GGATGTTAGTCCGTAT",-2.82938146591187,7.10598945617676 -"Sample_3_GGATTACCATTGAGCT",-0.0655752420425415,-9.62844753265381 -"Sample_3_GGATTACTCACCAGGC",-0.0680253654718399,5.07917022705078 -"Sample_3_GGCAATTAGAGGTAGA",3.77203989028931,4.17069864273071 -"Sample_3_GGCAATTAGTGTTGAA",-1.63958287239075,1.35030055046082 -"Sample_3_GGCAATTCAGATTGCT",-0.387528359889984,3.73221969604492 -"Sample_3_GGCAATTGTCAAACTC",1.11350476741791,-8.63481616973877 -"Sample_3_GGCAATTGTGATAAGT",4.70686292648315,4.24559020996094 -"Sample_3_GGCCGATGTTTCCACC",2.57983016967773,-10.46568775177 -"Sample_3_GGCGACTCATCCTAGA",0.759951114654541,-8.86960411071777 -"Sample_3_GGCGACTCATCCTTGC",-0.328466802835464,6.55317783355713 -"Sample_3_GGCGTGTCACATGACT",1.31636464595795,-8.78923988342285 -"Sample_3_GGGAATGAGATGGCGT",-1.87136578559875,8.77960395812988 -"Sample_3_GGGAATGGTCATCCCT",-1.29436635971069,7.82770586013794 -"Sample_3_GGGACCTTCTTGGGTA",-0.318974852561951,0.0206285491585732 -"Sample_3_GGGAGATAGTGTTTGC",-1.2744619846344,8.74278545379639 -"Sample_3_GGGAGATCAGATGGCA",0.56903612613678,4.57202053070068 -"Sample_3_GGGAGATCATACCATG",-1.9172614812851,7.91445732116699 -"Sample_3_GGGAGATGTCAAACTC",4.36907529830933,-9.78216743469238 -"Sample_3_GGGATGAGTCGTCTTC",3.44304156303406,6.28863000869751 -"Sample_3_GGGATGATCAGTTCGA",1.42094206809998,6.23867607116699 -"Sample_3_GGGCACTAGCAGGTCA",3.63338804244995,-8.57704162597656 -"Sample_3_GGGCACTCAAAGCAAT",-0.910828530788422,3.93954157829285 -"Sample_3_GGGCACTTCCTAGGGC",-2.32239317893982,7.58723592758179 -"Sample_3_GGGCACTTCTATCGCC",0.688692450523376,-9.35353565216064 -"Sample_3_GGGCATCCACGGCGTT",0.406541228294373,-9.07730960845947 -"Sample_3_GGGCATCCATCACAAC",0.132013455033302,3.39646291732788 -"Sample_3_GGGCATCGTTCCTCCA",5.1486930847168,5.77917337417603 -"Sample_3_GGGCATCTCCGCATCT",2.62838673591614,5.33065986633301 -"Sample_3_GGTATTGGTGAGGGTT",-1.80872070789337,6.55647802352905 -"Sample_3_GGTATTGGTTCCCGAG",0.349281162023544,3.89721393585205 -"Sample_3_GGTGAAGAGAGGTAGA",1.13299989700317,2.07345414161682 -"Sample_3_GGTGAAGTCCATGAGT",3.69966697692871,6.26280736923218 -"Sample_3_GGTGAAGTCCTCATTA",2.53847885131836,-8.13879585266113 -"Sample_3_GGTGCGTAGAGTACAT",1.36625242233276,-10.670449256897 -"Sample_3_GGTGCGTAGTACATGA",0.0283868946135044,5.63370847702026 -"Sample_3_GGTGTTAGTAGTAGTA",-0.904113829135895,0.473134130239487 -"Sample_3_GTAACGTAGGACAGCT",2.90893697738647,5.25704050064087 -"Sample_3_GTAACGTTCACTTACT",2.33033537864685,4.73113632202148 -"Sample_3_GTAACTGCATCGGTTA",3.4090416431427,-8.2517557144165 -"Sample_3_GTAACTGTCCGAACGC",-0.558022201061249,-7.31613445281982 -"Sample_3_GTACGTAAGACACTAA",-0.773694753646851,-6.8994402885437 -"Sample_3_GTACGTACATGCTGGC",3.39331603050232,4.64169025421143 -"Sample_3_GTACGTATCCTGTAGA",-2.90796804428101,8.29296398162842 -"Sample_3_GTACTTTGTGTCAATC",-0.0505617186427116,8.16171169281006 -"Sample_3_GTAGGCCAGCGCTCCA",2.79841494560242,7.69221496582031 -"Sample_3_GTAGGCCGTAACGTTC",0.127387374639511,-9.19612693786621 -"Sample_3_GTATCTTTCCTATGTT",2.77060389518738,6.44494438171387 -"Sample_3_GTATTCTCAGCTGCTG",3.21144890785217,6.6800856590271 -"Sample_3_GTATTCTGTCCCTACT",0.81026691198349,2.71471095085144 -"Sample_3_GTATTCTTCCAATGGT",3.17291951179504,4.8632173538208 -"Sample_3_GTCACAAGTCCGAAGA",-1.27039289474487,8.21162891387939 -"Sample_3_GTCACAAGTTCCACAA",-0.0641278252005577,-8.43955135345459 -"Sample_3_GTCACGGCACCGGAAA",-2.224205493927,7.83433103561401 -"Sample_3_GTCACGGGTTGCCTCT",4.90020656585693,5.54644870758057 -"Sample_3_GTCACGGTCAAGGCTT",-1.74379503726959,1.29663133621216 -"Sample_3_GTCATTTAGCTTTGGT",1.10458421707153,-10.1377220153809 -"Sample_3_GTCATTTTCAGTTTGG",3.36592602729797,5.80474376678467 -"Sample_3_GTCCTCAAGTATCGAA",-1.99205660820007,8.98696327209473 -"Sample_3_GTCGGGTGTCCTCTTG",3.5161714553833,7.54802227020264 -"Sample_3_GTCGTAATCCATGAGT",0.625626444816589,3.01829361915588 -"Sample_3_GTCTCGTAGTTCGCAT",-2.75748229026794,8.24715232849121 -"Sample_3_GTCTTCGGTATAAACG",3.51083850860596,3.53115463256836 -"Sample_3_GTGAAGGAGTACGACG",1.35986578464508,-9.38763236999512 -"Sample_3_GTGAAGGCAATAACGA",1.09948456287384,-8.39147281646729 -"Sample_3_GTGAAGGCATGTTGAC",3.60473895072937,6.62968587875366 -"Sample_3_GTGAAGGGTCCTCTTG",0.781003057956696,4.58572578430176 -"Sample_3_GTGAAGGTCATGTAGC",1.98217463493347,-4.8615460395813 -"Sample_3_GTGCAGCAGGCTACGA",4.86655521392822,4.53052425384521 -"Sample_3_GTGCAGCTCAAAGACA",-2.5434741973877,7.3812894821167 -"Sample_3_GTGCATACATCCGGGT",2.59546852111816,-9.90190029144287 -"Sample_3_GTGCATATCACCGTAA",1.46991276741028,-7.05592679977417 -"Sample_3_GTGCATATCATTATCC",2.26824808120728,-9.36928653717041 -"Sample_3_GTGCGGTAGAGGTACC",3.36321353912354,7.0063362121582 -"Sample_3_GTGCGGTCAAGCCGTC",0.160051852464676,-8.76552486419678 -"Sample_3_GTGCGGTCACATTCGA",-2.63863682746887,7.97915983200073 -"Sample_3_GTGCTTCAGCCAACAG",2.11035990715027,-7.5344877243042 -"Sample_3_GTGGGTCTCAAGAAGT",0.0581071972846985,0.667853593826294 -"Sample_3_GTGTGCGTCGGTGTCG",0.117205157876015,8.51464080810547 -"Sample_3_GTGTTAGCATCCTAGA",2.74114799499512,-9.48130226135254 -"Sample_3_GTGTTAGGTAGATTAG",3.29543018341064,5.5779390335083 -"Sample_3_GTTACAGCATTAGGCT",-2.18893027305603,7.94757509231567 -"Sample_3_GTTCATTCATGCATGT",3.22557020187378,-10.689902305603 -"Sample_3_GTTCATTTCCAAACAC",-0.983115494251251,7.75830030441284 -"Sample_3_GTTCTCGTCTAGCACA",1.73445081710815,-9.8812255859375 -"Sample_3_GTTTCTAAGATTACCC",-0.707846939563751,4.21703481674194 -"Sample_3_GTTTCTAAGGTGCAAC",-0.993634819984436,6.3808069229126 -"Sample_3_GTTTCTACATCACGAT",-1.75316417217255,8.79950523376465 -"Sample_3_TAAACCGAGTTCGATC",4.19515609741211,-8.48150825500488 -"Sample_3_TAAACCGGTCTGCGGT",2.40385437011719,6.69005441665649 -"Sample_3_TAAACCGGTTCGCGAC",4.24549341201782,5.60409355163574 -"Sample_3_TAAGAGAGTACTTAGC",-0.21730250120163,-9.07652282714844 -"Sample_3_TAAGAGATCCGAAGAG",-2.93015766143799,8.65698146820068 -"Sample_3_TAAGCGTAGGACTGGT",0.966175556182861,-7.38570308685303 -"Sample_3_TAAGTGCCAGACAAAT",3.13663983345032,-10.8593273162842 -"Sample_3_TAAGTGCTCGCCATAA",1.3595552444458,-9.09127044677734 -"Sample_3_TACACGAGTCACTTCC",2.86383318901062,-10.5342693328857 -"Sample_3_TACACGAGTTTCGCTC",-3.72021913528442,8.85080432891846 -"Sample_3_TACACGATCCCAGGTG",0.373452574014664,2.07866311073303 -"Sample_3_TACCTATGTCCAACTA",2.00653505325317,7.82164669036865 -"Sample_3_TACCTTAAGCTAACAA",1.99293041229248,-10.1733169555664 -"Sample_3_TACCTTATCTGGCGTG",3.46196556091309,-7.45022010803223 -"Sample_3_TACGGGCCATCCTAGA",-0.768954575061798,6.30992269515991 -"Sample_3_TACGGGCCATTACCTT",-0.871034741401672,3.27377104759216 -"Sample_3_TACGGTAAGGATGGAA",2.63725399971008,6.4339599609375 -"Sample_3_TACTCATTCGGCCGAT",-1.05317187309265,1.84786581993103 -"Sample_3_TACTCGCCAACTGCGC",0.110215649008751,-9.40203475952148 -"Sample_3_TACTCGCTCCGAATGT",-1.69399762153625,8.95248031616211 -"Sample_3_TACTCGCTCGTAGGTT",-1.24293482303619,8.54566478729248 -"Sample_3_TACTTACTCCGTTGTC",-0.438259273767471,-10.4138345718384 -"Sample_3_TACTTGTGTACTTAGC",0.520776748657227,-8.68942260742188 -"Sample_3_TAGCCGGCAATGTTGC",2.31410026550293,5.5320086479187 -"Sample_3_TAGCCGGTCGAGAACG",0.662798345088959,2.70067954063416 -"Sample_3_TAGGCATCAGTCCTTC",-0.187973454594612,4.97060775756836 -"Sample_3_TAGGCATGTTGTACAC",-1.81556928157806,9.47457790374756 -"Sample_3_TAGTGGTCAAGGGTCA",1.91261804103851,-8.98585319519043 -"Sample_3_TAGTGGTTCAATACCG",-2.03491187095642,7.88862085342407 -"Sample_3_TAGTGGTTCCTAGTGA",4.47494697570801,5.72257041931152 -"Sample_3_TAGTTGGAGTAGGCCA",1.65117359161377,-9.54350090026855 -"Sample_3_TATCAGGCAACTGCTA",3.18426465988159,-7.92161893844604 -"Sample_3_TATCAGGGTCGGATCC",2.58460474014282,6.08617830276489 -"Sample_3_TATCTCAAGACTTGAA",2.99773955345154,-8.38348960876465 -"Sample_3_TATCTCAAGGGAGTAA",1.30431699752808,-9.84789180755615 -"Sample_3_TATCTCAAGGTCATCT",0.00162555405404419,4.18080520629883 -"Sample_3_TATCTCAGTCTACCTC",0.412862569093704,3.92703747749329 -"Sample_3_TATCTCATCATGTAGC",4.6569652557373,5.60174798965454 -"Sample_3_TATGCCCCAAGCTGTT",4.76230573654175,5.88475561141968 -"Sample_3_TATGCCCCAATCACAC",-1.6865359544754,8.64875030517578 -"Sample_3_TATTACCCAAACCCAT",-1.33362746238708,9.75904941558838 -"Sample_3_TATTACCCACGAAAGC",-3.18336892127991,7.87725448608398 -"Sample_3_TATTACCGTGATGTGG",-0.731655657291412,-10.5908823013306 -"Sample_3_TCAACGACAATCTACG",0.96381026506424,-9.23630619049072 -"Sample_3_TCAACGATCCTTTCGG",-0.0893415212631226,1.7358912229538 -"Sample_3_TCAATCTGTGCAGACA",-0.648929834365845,0.864116430282593 -"Sample_3_TCAATCTGTGCGATAG",3.22662854194641,6.97017288208008 -"Sample_3_TCACAAGCAATCGAAA",2.98343801498413,-9.82026958465576 -"Sample_3_TCACAAGGTCTCATCC",-0.995560109615326,-9.28674602508545 -"Sample_3_TCACGAACATACTACG",-0.879132211208344,0.432495832443237 -"Sample_3_TCACGAAGTAGAAAGG",-0.0635165721178055,0.974073350429535 -"Sample_3_TCACGAATCATCGATG",3.85435628890991,5.69896459579468 -"Sample_3_TCACGAATCGTCACGG",3.06755256652832,5.27196311950684 -"Sample_3_TCAGATGAGTTGAGTA",-0.755187332630157,6.62595796585083 -"Sample_3_TCAGCAAGTGCTTCTC",4.56104469299316,6.38318777084351 -"Sample_3_TCAGCTCAGACAGGCT",0.518759548664093,3.02213215827942 -"Sample_3_TCAGCTCGTCGAGTTT",3.15685081481934,7.71561717987061 -"Sample_3_TCAGGATTCATTTGGG",3.59086990356445,5.60978174209595 -"Sample_3_TCAGGTAAGTCCGGTC",-0.9634969830513,3.4638819694519 -"Sample_3_TCAGGTACAGGATTGG",-1.36623060703278,1.05477225780487 -"Sample_3_TCAGGTAGTCCGTCAG",1.66215515136719,-6.90166330337524 -"Sample_3_TCAGGTATCGAGGTAG",2.65110397338867,-9.54476833343506 -"Sample_3_TCAGGTATCGCCTGTT",0.379516869783401,5.8350133895874 -"Sample_3_TCATTACCAGTATAAG",3.57474398612976,-9.45399188995361 -"Sample_3_TCATTTGAGGCAATTA",3.33750891685486,6.62061548233032 -"Sample_3_TCATTTGAGTGGTAGC",-1.83520519733429,0.636157929897308 -"Sample_3_TCATTTGGTGTTGAGG",-0.341910749673843,0.331509947776794 -"Sample_3_TCCACACAGTACACCT",-2.2033212184906,7.57005167007446 -"Sample_3_TCCACACCAGTTTACG",1.27929973602295,-10.4656229019165 -"Sample_3_TCCCGATAGACAGGCT",-2.31060814857483,7.84246826171875 -"Sample_3_TCCCGATCAAGTTCTG",0.903681933879852,-8.0204610824585 -"Sample_3_TCCCGATTCGTAGATC",-0.462989747524261,6.69073104858398 -"Sample_3_TCGAGGCGTAGAAGGA",-1.69976603984833,5.60164642333984 -"Sample_3_TCGAGGCTCCTCAACC",3.18223309516907,4.32811784744263 -"Sample_3_TCGAGGCTCGTTGACA",2.56907629966736,-9.21863174438477 -"Sample_3_TCGCGAGGTCAGTGGA",0.724505364894867,2.83857774734497 -"Sample_3_TCGCGAGTCGACAGCC",-0.561817646026611,-7.3774471282959 -"Sample_3_TCGGGACCAGACGTAG",2.38499140739441,7.31801319122314 -"Sample_3_TCGGGACGTGATAAGT",0.445334523916245,-8.74581909179688 -"Sample_3_TCGGGACTCACCCGAG",-1.90442276000977,7.23723936080933 -"Sample_3_TCGGGACTCCGAAGAG",1.22314023971558,-9.56136798858643 -"Sample_3_TCGGTAACACAGGCCT",2.94108319282532,5.41960763931274 -"Sample_3_TCGGTAACATTAACCG",2.74222040176392,-7.20949935913086 -"Sample_3_TCGTACCAGATCTGCT",2.75614905357361,4.98100423812866 -"Sample_3_TCGTACCGTTTGACAC",1.10085964202881,-8.25449848175049 -"Sample_3_TCGTACCTCAAACCGT",2.75823783874512,-6.84388065338135 -"Sample_3_TCGTACCTCCCGACTT",3.8091037273407,4.46155023574829 -"Sample_3_TCGTAGACATCTATGG",2.29687309265137,-9.14501285552979 -"Sample_3_TCTATTGAGGCGACAT",3.25663232803345,7.28457736968994 -"Sample_3_TCTATTGGTAGCTCCG",1.99517071247101,8.41094207763672 -"Sample_3_TCTCATAAGACTTTCG",0.0501250140368938,0.454271763563156 -"Sample_3_TCTCTAATCCGTAGTA",0.356933772563934,3.28167605400085 -"Sample_3_TCTGAGAAGAGTGAGA",0.17549355328083,-10.5956354141235 -"Sample_3_TCTGAGACACCACGTG",-0.276751011610031,0.564424753189087 -"Sample_3_TCTGAGACAGATCTGT",2.14804077148438,4.55136728286743 -"Sample_3_TCTGGAAAGTACGTTC",2.65169811248779,7.81635999679565 -"Sample_3_TCTTCGGAGGGATGGG",3.4784083366394,7.25637197494507 -"Sample_3_TCTTTCCAGACTGGGT",3.0172975063324,-9.29521560668945 -"Sample_3_TCTTTCCGTGCTTCTC",4.36383152008057,5.15998411178589 -"Sample_3_TGAAAGATCCCTAATT",0.376287162303925,-11.5120801925659 -"Sample_3_TGAAAGATCGTTGCCT",-1.81525540351868,0.675574064254761 -"Sample_3_TGACAACTCCTACAGA",4.18995571136475,-9.6337251663208 -"Sample_3_TGACTTTCAATGGATA",0.112384893000126,2.36812949180603 -"Sample_3_TGACTTTTCTAGCACA",4.13757562637329,5.68711519241333 -"Sample_3_TGAGAGGAGCAATCTC",3.17253613471985,-10.2919797897339 -"Sample_3_TGAGCATGTCAAAGCG",-0.163914382457733,3.77138733863831 -"Sample_3_TGAGCCGGTGCCTTGG",0.314369767904282,-11.2171726226807 -"Sample_3_TGAGGGAAGCATGGCA",-2.32948517799377,6.79531574249268 -"Sample_3_TGAGGGACATGGGAAC",-0.254692405462265,4.15532112121582 -"Sample_3_TGAGGGATCACATGCA",3.93977022171021,4.94442081451416 -"Sample_3_TGATTTCTCCTTCAAT",1.40044665336609,-8.283203125 -"Sample_3_TGCACCTAGGGCACTA",1.98788702487946,-9.65441703796387 -"Sample_3_TGCCCATCAGTAAGCG",2.67769265174866,4.50941944122314 -"Sample_3_TGCCCTAGTTCAACCA",0.437285214662552,-8.26924991607666 -"Sample_3_TGCCCTATCAGGCAAG",-0.565350949764252,7.79767227172852 -"Sample_3_TGCGGGTAGACCTAGG",3.31296491622925,6.15546321868896 -"Sample_3_TGCGGGTGTTATGTGC",1.83045446872711,-4.66947937011719 -"Sample_3_TGCGTGGGTAATTGGA",2.61938261985779,-7.94413328170776 -"Sample_3_TGCTACCAGCTCCCAG",-0.516371488571167,9.60774421691895 -"Sample_3_TGCTGCTGTCCAACTA",-0.121767766773701,5.52820634841919 -"Sample_3_TGGACGCAGGAGCGAG",-1.50911664962769,5.71784019470215 -"Sample_3_TGGACGCGTAACGCGA",3.73525214195251,7.26278209686279 -"Sample_3_TGGCGCAAGTACGCGA",3.43888449668884,6.22272634506226 -"Sample_3_TGGCGCAGTTCCCGAG",0.446681261062622,-8.0354118347168 -"Sample_3_TGGCGCATCCTACAGA",3.70231795310974,3.67331123352051 -"Sample_3_TGGGAAGAGGAACTGC",-2.96594023704529,8.59525012969971 -"Sample_3_TGGGAAGTCTATCCTA",-1.02171659469604,-8.13236618041992 -"Sample_3_TGGGCGTCAGTTAACC",2.43946218490601,7.96482610702515 -"Sample_3_TGGTTAGAGCGCCTTG",1.85190546512604,-8.87875270843506 -"Sample_3_TGGTTAGAGGAATTAC",0.200113266706467,3.16261410713196 -"Sample_3_TGGTTAGGTCAATACC",1.9065580368042,-7.11719608306885 -"Sample_3_TGGTTCCTCCACTCCA",-0.0732661783695221,5.54109144210815 -"Sample_3_TGTATTCAGATGTCGG",-0.168151706457138,4.74865818023682 -"Sample_3_TGTATTCCAGCGTCCA",2.03673529624939,-9.02143001556396 -"Sample_3_TGTATTCTCTGATACG",2.76250791549683,-9.44964122772217 -"Sample_3_TGTCCCAAGAGACTAT",4.28805255889893,-9.7507963180542 -"Sample_3_TGTCCCATCTTGTACT",1.97649824619293,-9.85092067718506 -"Sample_3_TGTGGTACATGCAACT",-2.53194093704224,8.87195873260498 -"Sample_3_TGTGGTAGTGCATCTA",-1.42099416255951,0.324003636837006 -"Sample_3_TGTGGTATCCCGACTT",4.03301286697388,4.91901445388794 -"Sample_3_TGTGTTTAGGCCCTCA",4.42280340194702,-8.73600959777832 -"Sample_3_TGTGTTTGTTGTGGAG",0.93818861246109,2.68866872787476 -"Sample_3_TGTGTTTTCATGCTCC",4.07108545303345,6.76891851425171 -"Sample_3_TGTTCCGAGCCACCTG",1.9491468667984,-7.54811573028564 -"Sample_3_TGTTCCGCAGTGACAG",2.15312361717224,-4.79551839828491 -"Sample_3_TTAGGACAGGGTCGAT",-1.71604371070862,1.0514919757843 -"Sample_3_TTAGGACGTGGTACAG",3.12388157844543,5.93119430541992 -"Sample_3_TTAGGCACATAAAGGT",3.84238266944885,7.77144432067871 -"Sample_3_TTAGTTCTCACTTACT",2.9904887676239,4.45491743087769 -"Sample_3_TTAGTTCTCTTGAGAC",4.01016855239868,5.30387735366821 -"Sample_3_TTATGCTTCACGAAGG",-0.769375145435333,8.93002605438232 -"Sample_3_TTCGGTCAGCCAGAAC",2.72353959083557,-11.0902070999146 -"Sample_3_TTCGGTCAGTTCCACA",-9.77152442932129,-2.86742925643921 -"Sample_3_TTCTACACATTACCTT",-0.885757148265839,0.874507784843445 -"Sample_3_TTCTACATCGCAAGCC",3.67795872688293,4.57094430923462 -"Sample_3_TTCTCAACACACTGCG",-0.397082179784775,4.93495035171509 -"Sample_3_TTCTCCTGTTCCCGAG",2.92777490615845,-10.8755970001221 -"Sample_3_TTCTCCTTCCGAATGT",3.29207158088684,5.65155792236328 -"Sample_3_TTCTTAGGTTTAGGAA",1.80687046051025,2.29979825019836 -"Sample_3_TTGAACGAGCCATCGC",1.52759397029877,-8.49742794036865 -"Sample_3_TTGAACGGTAGCTCCG",1.7077968120575,3.33323812484741 -"Sample_3_TTGAACGGTCTCAACA",0.16275642812252,0.10180226713419 -"Sample_3_TTGAACGTCAGTTCGA",-0.843507289886475,8.61132049560547 -"Sample_3_TTGACTTTCCCTAACC",2.670161485672,-10.9075269699097 -"Sample_3_TTGCCGTCACCTTGTC",4.27182245254517,-8.85572338104248 -"Sample_3_TTGCCGTTCTTGCAAG",2.52126812934875,4.84965372085571 -"Sample_3_TTGCGTCCAGCCTTGG",1.37443399429321,3.12751388549805 -"Sample_3_TTGCGTCTCACGGTTA",-0.902223527431488,7.22042655944824 -"Sample_3_TTGCGTCTCCTATTCA",4.59255695343018,6.41105365753174 -"Sample_3_TTGCGTCTCTTCTGGC",-1.47658431529999,5.85149574279785 -"Sample_3_TTGGCAAAGAGATGAG",3.57122921943665,-8.90363693237305 -"Sample_3_TTGGCAAGTATCACCA",4.32964086532593,4.9979190826416 -"Sample_3_TTGTAGGAGTCGTACT",4.21509695053101,5.29737854003906 -"Sample_3_TTTATGCAGCCACTAT",1.9352388381958,5.81448745727539 -"Sample_3_TTTATGCCAGTTAACC",4.59991979598999,6.713791847229 -"Sample_3_TTTGCGCAGGCTACGA",3.72126221656799,-10.3079385757446 -"Sample_3_TTTGCGCTCACTATTC",4.13837766647339,5.47839593887329 -"Sample_3_TTTGGTTTCTGTTTGT",-0.464156359434128,0.0898076370358467 -"Sample_3_TTTGTCACAATTGCTG",2.50085854530334,2.97856187820435 -"Sample_3_TTTGTCAGTAGGAGTC",-9.54189491271973,-2.63138031959534 -"Sample_3_TTTGTCATCCTCATTA",0.553207814693451,-11.1732807159424 -"Sample_4_AAACCTGTCTGCTGTC",-0.441392093896866,0.275921195745468 -"Sample_4_AAACGGGGTTTGCATG",-3.81197834014893,8.5780611038208 -"Sample_4_AAACGGGTCCTTTACA",4.31900024414062,5.99839448928833 -"Sample_4_AAAGTAGCAAACAACA",-2.38874292373657,7.42229270935059 -"Sample_4_AAAGTAGCATAGAAAC",-2.69520330429077,7.48875379562378 -"Sample_4_AAATGCCCATGATCCA",2.91867589950562,6.2869758605957 -"Sample_4_AAATGCCGTTAGTGGG",-1.4110689163208,7.4989595413208 -"Sample_4_AAATGCCTCAGTTAGC",2.36249089241028,6.8055534362793 -"Sample_4_AACACGTGTCGCTTCT",3.02072405815125,7.02020740509033 -"Sample_4_AACCATGGTCTCACCT",-1.00919008255005,4.96875524520874 -"Sample_4_AACTCAGGTACCGTAT",1.41154098510742,-10.2447385787964 -"Sample_4_AACTCAGGTCCATCCT",-0.418437451124191,-9.30278873443604 -"Sample_4_AACTCAGGTCGCTTCT",2.96149730682373,7.30643653869629 -"Sample_4_AACTCAGTCCTTTCTC",1.97340977191925,-10.2578430175781 -"Sample_4_AACTCTTCACACATGT",-0.216927230358124,2.67747974395752 -"Sample_4_AACTCTTGTGCCTGTG",-3.73407912254333,8.88540077209473 -"Sample_4_AACTCTTTCAAACAAG",2.95591306686401,-9.75669574737549 -"Sample_4_AACTCTTTCCTCGCAT",-0.368060797452927,0.877899050712585 -"Sample_4_AACTGGTGTGCGGTAA",2.96936535835266,6.76996660232544 -"Sample_4_AACTTTCGTAGCGATG",2.1764657497406,-11.2292776107788 -"Sample_4_AAGACCTAGATGTCGG",3.99948644638062,7.04505491256714 -"Sample_4_AAGCCGCAGCTGAACG",2.94951057434082,-7.05850124359131 -"Sample_4_AAGCCGCGTTCGCGAC",1.76625978946686,-4.37805414199829 -"Sample_4_AAGGAGCCAATACGCT",2.34533262252808,8.30580234527588 -"Sample_4_AAGGCAGAGAGGTAGA",-0.181867986917496,7.37893152236938 -"Sample_4_AAGGCAGAGGAATCGC",-2.26968955993652,6.40983819961548 -"Sample_4_AAGGTTCGTCATATGC",-2.1593713760376,8.575439453125 -"Sample_4_AAGGTTCTCAGTGCAT",2.85642051696777,7.04971075057983 -"Sample_4_AAGTCTGAGAGGTAGA",2.38644886016846,-4.7858362197876 -"Sample_4_AATCCAGCAAGCCCAC",1.76898717880249,-10.2729921340942 -"Sample_4_AATCCAGGTAAGCACG",-0.605815172195435,1.23974645137787 -"Sample_4_AATCCAGTCACAACGT",2.72673320770264,4.78290462493896 -"Sample_4_AATCGGTCATCACAAC",4.14252996444702,5.23344612121582 -"Sample_4_ACACCCTCAGCTGTGC",2.51007866859436,-8.91355895996094 -"Sample_4_ACACCCTCATACCATG",1.36900973320007,-7.36846113204956 -"Sample_4_ACACTGAAGCTGAACG",-1.82274782657623,6.15101003646851 -"Sample_4_ACACTGATCTTACCTA",1.40713441371918,-10.2318534851074 -"Sample_4_ACAGCCGAGCTACCGC",-0.271210670471191,-11.002818107605 -"Sample_4_ACAGCTAAGGCAGTCA",0.160717591643333,-8.74949645996094 -"Sample_4_ACATACGAGAGAACAG",2.0244038105011,5.17639875411987 -"Sample_4_ACATACGAGGCTAGAC",1.30527520179749,2.42105722427368 -"Sample_4_ACATACGTCAACCAAC",4.0503134727478,-9.83982181549072 -"Sample_4_ACATCAGAGGTCGGAT",1.95867764949799,-7.37573719024658 -"Sample_4_ACATCAGGTCCGTTAA",3.28871321678162,-9.49096870422363 -"Sample_4_ACATGGTGTCGCTTCT",4.65954446792603,7.08635759353638 -"Sample_4_ACCAGTACACAACTGT",-1.06519198417664,6.47612285614014 -"Sample_4_ACCAGTATCCTCGCAT",4.29034376144409,6.52041149139404 -"Sample_4_ACCAGTATCTGAAAGA",1.90517294406891,-7.83093500137329 -"Sample_4_ACCGTAACAAGAAGAG",-1.66275990009308,8.78939819335938 -"Sample_4_ACCGTAACATTGAGCT",0.541090607643127,4.68152570724487 -"Sample_4_ACCGTAATCGCTTGTC",4.85925436019897,5.95454120635986 -"Sample_4_ACCTTTAAGCGATATA",-1.62054979801178,7.79296588897705 -"Sample_4_ACGAGCCAGACCTAGG",-1.59242343902588,9.51449966430664 -"Sample_4_ACGAGCCGTCTAGTCA",-0.930519998073578,-9.59261512756348 -"Sample_4_ACGATACAGAGACGAA",3.52305340766907,5.31095886230469 -"Sample_4_ACGATACCAATACGCT",4.12706518173218,-9.71988773345947 -"Sample_4_ACGATACGTCTGCCAG",-0.126912474632263,8.97251224517822 -"Sample_4_ACGATGTAGAGCTTCT",3.04687356948853,5.49647092819214 -"Sample_4_ACGATGTCATTCACTT",3.14541602134705,6.48817300796509 -"Sample_4_ACGATGTGTCATATCG",-0.0723644122481346,4.10259532928467 -"Sample_4_ACGCAGCCAAGCGCTC",3.45015645027161,6.10743856430054 -"Sample_4_ACGCCGAAGGTGTTAA",3.34506034851074,-9.83516979217529 -"Sample_4_ACGGAGAAGACAGAGA",-1.32186377048492,5.45000076293945 -"Sample_4_ACGGCCAGTTGATTCG",-1.30683863162994,1.26951336860657 -"Sample_4_ACGTCAAAGGTAGCTG",2.12539958953857,-9.31902408599854 -"Sample_4_ACGTCAATCGGAGCAA",3.85719013214111,-9.97425556182861 -"Sample_4_ACGTCAATCGGCATCG",3.69266319274902,7.47711372375488 -"Sample_4_ACTATCTAGCTACCTA",1.33930218219757,-9.57081413269043 -"Sample_4_ACTGAACGTCAATGTC",-0.780478835105896,8.52621269226074 -"Sample_4_ACTGAACTCCCAACGG",-1.82173764705658,9.30308532714844 -"Sample_4_ACTGCTCCATTAGCCA",3.82089948654175,-9.03951740264893 -"Sample_4_ACTGTCCAGAAGAAGC",3.47375702857971,3.74435710906982 -"Sample_4_ACTGTCCGTACCGCTG",2.66862893104553,5.92043018341064 -"Sample_4_ACTTACTAGCCGGTAA",-0.79288113117218,6.4877233505249 -"Sample_4_ACTTACTGTACCGGCT",3.33052778244019,-10.4446430206299 -"Sample_4_ACTTGTTCAGATGGCA",3.00056266784668,-8.32422637939453 -"Sample_4_ACTTGTTTCACGATGT",1.645139336586,8.40946865081787 -"Sample_4_ACTTTCACACAGTCGC",-1.44753193855286,9.21219539642334 -"Sample_4_ACTTTCAGTAAGGATT",3.62578368186951,3.68228530883789 -"Sample_4_ACTTTCATCAGTTGAC",-2.88941478729248,8.2373218536377 -"Sample_4_AGAATAGTCAACACCA",0.351208239793777,-9.62041759490967 -"Sample_4_AGAGCGAAGGTCATCT",2.71201825141907,7.31613492965698 -"Sample_4_AGAGCGAGTACCGGCT",2.45053052902222,-8.99453449249268 -"Sample_4_AGAGCTTAGGAGTTTA",1.24902033805847,-9.70102977752686 -"Sample_4_AGAGCTTTCAAGAAGT",1.86892509460449,-7.19459819793701 -"Sample_4_AGAGTGGTCCAGTAGT",-1.92446672916412,8.39554691314697 -"Sample_4_AGAGTGGTCCTTGACC",-1.98877990245819,6.52427816390991 -"Sample_4_AGCAGCCAGTTCCACA",-0.928058445453644,8.49355983734131 -"Sample_4_AGCCTAACAGTCTTCC",4.18707275390625,-8.5496997833252 -"Sample_4_AGCGTATAGTGCAAGC",4.45372581481934,6.62870836257935 -"Sample_4_AGCGTATGTGACGGTA",3.5035514831543,-8.72855091094971 -"Sample_4_AGCGTCGCAGCGAACA",1.87029349803925,8.15264129638672 -"Sample_4_AGCTCCTTCAGAGCTT",0.331871658563614,-11.3042163848877 -"Sample_4_AGCTCTCCATTTGCCC",2.35161781311035,-4.86351203918457 -"Sample_4_AGCTTGAAGACTAGAT",2.54831480979919,-8.79245471954346 -"Sample_4_AGCTTGAGTCTCACCT",-2.7892439365387,8.27637100219727 -"Sample_4_AGGCCACCAAGCGAGT",4.77039432525635,-8.81098937988281 -"Sample_4_AGGCCACCAAGTAATG",4.44174575805664,-9.42769718170166 -"Sample_4_AGGCCACCACGGCTAC",0.618652403354645,2.32638740539551 -"Sample_4_AGGCCACTCTCTTATG",1.4733989238739,-10.2654304504395 -"Sample_4_AGGCCGTGTAGAGGAA",2.82257294654846,5.54938316345215 -"Sample_4_AGGCCGTTCATCGATG",5.11702871322632,6.80764961242676 -"Sample_4_AGGGATGGTCTCAACA",-0.474834144115448,-0.0524896271526814 -"Sample_4_AGGGTGACAGTAAGAT",2.5605194568634,-10.014256477356 -"Sample_4_AGGTCATCACAGACTT",4.12159156799316,-9.55960655212402 -"Sample_4_AGGTCATTCGAGAGCA",0.0379535295069218,-8.58825206756592 -"Sample_4_AGGTCCGCAGCTGTGC",2.07888078689575,-4.55546140670776 -"Sample_4_AGTAGTCTCACTCCTG",1.84760594367981,5.18111848831177 -"Sample_4_AGTCTTTAGCCTCGTG",-1.06231796741486,2.37414741516113 -"Sample_4_AGTCTTTAGGATTCGG",-0.751634538173676,4.36213159561157 -"Sample_4_AGTGAGGAGGCTCTTA",1.50854301452637,-9.88154029846191 -"Sample_4_AGTGAGGGTGCACCAC",2.03625583648682,8.07965278625488 -"Sample_4_AGTGAGGTCTGATACG",0.749421417713165,3.21944308280945 -"Sample_4_AGTGTCATCCTTTCGG",-1.31132245063782,1.51801705360413 -"Sample_4_AGTGTCATCTGGGCCA",-1.03675699234009,-8.14039325714111 -"Sample_4_AGTGTCATCTTACCTA",-0.519922316074371,0.56949657201767 -"Sample_4_AGTTGGTTCGTCCAGG",4.2142162322998,5.56794309616089 -"Sample_4_ATAACGCAGTGGCACA",2.46136498451233,-4.79567909240723 -"Sample_4_ATAACGCCAGTCAGAG",0.18731227517128,1.86297655105591 -"Sample_4_ATAACGCGTAGCTTGT",-1.28388905525208,7.99421119689941 -"Sample_4_ATAAGAGGTGCCTGGT",4.33509683609009,-9.30431747436523 -"Sample_4_ATAGACCTCTTTACGT",2.42587924003601,-9.35573577880859 -"Sample_4_ATCACGAAGTGGAGTC",-0.573104739189148,3.99318623542786 -"Sample_4_ATCACGATCAGCAACT",-2.70308804512024,7.64794588088989 -"Sample_4_ATCATCTCAACACGCC",-2.99547672271729,7.07107400894165 -"Sample_4_ATCATGGAGGTGATAT",-0.655697882175446,-10.1247329711914 -"Sample_4_ATCATGGCAGCCAGAA",-9.68507099151611,-2.77597045898438 -"Sample_4_ATCCACCAGTAGATGT",-0.884573578834534,-9.36307430267334 -"Sample_4_ATCCACCCATCGATGT",-9.51201343536377,-2.60097193717957 -"Sample_4_ATCCACCGTAACGACG",1.45116758346558,3.06866312026978 -"Sample_4_ATCCGAAAGAAGATTC",3.4659378528595,-8.54224681854248 -"Sample_4_ATCCGAACACTAGTAC",-0.416778743267059,-10.1551828384399 -"Sample_4_ATCCGAAGTACACCGC",1.54847550392151,-7.92299318313599 -"Sample_4_ATCGAGTCACAACTGT",3.3446958065033,6.76155614852905 -"Sample_4_ATCTACTCACGAGGTA",0.18793623149395,-9.83943843841553 -"Sample_4_ATCTACTCATGGGACA",0.396301358938217,6.64712142944336 -"Sample_4_ATCTACTTCGGCCGAT",-1.30414199829102,8.33154010772705 -"Sample_4_ATCTGCCAGCGCTCCA",2.31005382537842,6.42743062973022 -"Sample_4_ATCTGCCAGTGACATA",4.08317184448242,-9.11158275604248 -"Sample_4_ATCTGCCAGTTGAGTA",-0.478581637144089,-9.52898216247559 -"Sample_4_ATCTGCCCACTTAACG",1.52556729316711,6.79562330245972 -"Sample_4_ATCTGCCCATCCCACT",1.43322789669037,-7.66765117645264 -"Sample_4_ATCTGCCGTCTCTTTA",-0.74528568983078,3.05766201019287 -"Sample_4_ATCTGCCGTTAGTGGG",0.702815890312195,8.4635705947876 -"Sample_4_ATCTGCCTCGCTTAGA",0.577290594577789,3.95420861244202 -"Sample_4_ATGAGGGGTACCGTAT",-1.29292392730713,0.201977849006653 -"Sample_4_ATGCGATGTTCTGTTT",3.26878881454468,5.76602363586426 -"Sample_4_ATGGGAGAGTTGTAGA",3.09738969802856,-6.99935054779053 -"Sample_4_ATGTGTGAGCATCATC",1.15974271297455,-10.4609394073486 -"Sample_4_ATGTGTGAGGTCATCT",0.783474087715149,-9.22678279876709 -"Sample_4_ATGTGTGGTGTATGGG",2.91850590705872,4.4542670249939 -"Sample_4_ATTACTCAGGTCATCT",-0.583621621131897,-10.5457916259766 -"Sample_4_ATTACTCGTGGAAAGA",0.645171403884888,1.93687129020691 -"Sample_4_ATTATCCAGTAGCGGT",-0.0460267886519432,-10.6186895370483 -"Sample_4_ATTATCCCACCCATGG",4.82932233810425,6.57686901092529 -"Sample_4_ATTCTACCAAGCGATG",-0.321604818105698,3.93373942375183 -"Sample_4_ATTCTACCATTAACCG",1.39911735057831,5.96888399124146 -"Sample_4_ATTCTACGTCTAGTCA",0.721874713897705,-9.7070484161377 -"Sample_4_ATTCTACGTGGTAACG",-1.56551122665405,0.192362993955612 -"Sample_4_ATTGGACCAGCGTTCG",-1.76123487949371,0.738894641399384 -"Sample_4_ATTGGACCAGCTTCGG",1.4244966506958,2.57818412780762 -"Sample_4_ATTGGACGTAGCGTGA",-2.21579098701477,8.79263687133789 -"Sample_4_ATTGGACGTCGCATCG",-0.493488222360611,-7.53775548934937 -"Sample_4_ATTGGTGAGCCCGAAA",3.70485877990723,8.1004695892334 -"Sample_4_CAACCAATCCTTGGTC",1.60537970066071,-10.6601524353027 -"Sample_4_CAACTAGCATGGTAGG",0.868809759616852,-10.8845243453979 -"Sample_4_CAACTAGTCGCATGGC",3.62499237060547,4.68763732910156 -"Sample_4_CAAGAAAAGACTAAGT",-0.41252812743187,-9.95775032043457 -"Sample_4_CAAGAAAAGTTAGCGG",-0.856565356254578,-9.39055252075195 -"Sample_4_CAAGAAATCTATGTGG",2.74142980575562,-7.81566858291626 -"Sample_4_CAAGATCGTTACTGAC",-0.514392971992493,0.305047690868378 -"Sample_4_CAAGATCGTTCAACCA",4.06712341308594,-9.04968929290771 -"Sample_4_CAAGATCGTTCAGCGC",2.468829870224,6.86499357223511 -"Sample_4_CAAGATCGTTTAGGAA",-2.9012770652771,8.59417247772217 -"Sample_4_CAAGTTGGTAACGCGA",-2.31818532943726,6.47965097427368 -"Sample_4_CACAAACAGTTCGATC",2.83846855163574,4.81039619445801 -"Sample_4_CACACAACAATCACAC",-1.26386618614197,8.81221866607666 -"Sample_4_CACACAAGTCGCGTGT",-0.241774424910545,8.77503871917725 -"Sample_4_CACACCTCAAACCTAC",0.00739712221547961,-11.1960535049438 -"Sample_4_CACACCTTCCCATTTA",3.45293807983398,-9.95949554443359 -"Sample_4_CACACTCCAGTACACT",0.231815993785858,-9.88775539398193 -"Sample_4_CACACTCCATGTAAGA",-1.03064298629761,9.8472957611084 -"Sample_4_CACACTCTCTAACCGA",0.949794411659241,1.35567462444305 -"Sample_4_CACAGGCGTGGGTATG",2.09127378463745,-11.203236579895 -"Sample_4_CACAGTAAGTGACATA",-1.69412589073181,0.946304261684418 -"Sample_4_CACATAGCAAGCCGCT",-1.83098268508911,9.21330833435059 -"Sample_4_CACATAGGTAGCAAAT",-0.580059170722961,-10.4024906158447 -"Sample_4_CACATAGTCGCGTTTC",-2.26157689094543,7.09643888473511 -"Sample_4_CACATTTCATTGCGGC",-0.428848832845688,7.17079639434814 -"Sample_4_CACATTTTCCGTCATC",-1.3519401550293,8.29600811004639 -"Sample_4_CACCACTCACTCGACG",0.0745825469493866,2.07551598548889 -"Sample_4_CACCACTTCATACGGT",1.27495527267456,-8.21658706665039 -"Sample_4_CACCACTTCCGCAAGC",1.63321590423584,-9.586594581604 -"Sample_4_CACCACTTCTTGAGAC",1.59616422653198,-8.4629545211792 -"Sample_4_CACCAGGCACCTCGTT",-1.72269129753113,0.291505247354507 -"Sample_4_CACCAGGTCAAGGTAA",1.87611615657806,-5.33736085891724 -"Sample_4_CACCTTGTCTCTAAGG",0.290066599845886,6.03847646713257 -"Sample_4_CAGAGAGCATCACGTA",0.0296014714986086,5.67794036865234 -"Sample_4_CAGATCAAGATATGGT",4.75585556030273,5.61840200424194 -"Sample_4_CAGCAGCGTCGCCATG",0.031576968729496,9.14037704467773 -"Sample_4_CAGCAGCTCGGAATCT",-9.73073768615723,-2.82229518890381 -"Sample_4_CAGCAGCTCTGTCCGT",4.43347454071045,6.77809476852417 -"Sample_4_CAGCCGAAGCGACGTA",1.49459505081177,-8.54748916625977 -"Sample_4_CAGCGACAGTAGCGGT",1.97490048408508,-6.81227493286133 -"Sample_4_CAGCTGGAGTGTACGG",2.93184804916382,-9.3095703125 -"Sample_4_CAGCTGGTCTTTCCTC",0.555420994758606,-10.3475761413574 -"Sample_4_CAGTAACCATCTCGCT",-1.09221577644348,2.67556142807007 -"Sample_4_CAGTAACTCTGTCCGT",-0.694556534290314,9.16415882110596 -"Sample_4_CAGTCCTAGACAAGCC",1.10067844390869,-7.62287664413452 -"Sample_4_CAGTCCTAGATCTGCT",-1.92243087291718,8.37854480743408 -"Sample_4_CAGTCCTAGTAGATGT",4.6773853302002,5.20285940170288 -"Sample_4_CAGTCCTCAGGCGATA",3.46707391738892,6.65273904800415 -"Sample_4_CAGTCCTGTTCAGCGC",2.94031476974487,4.31161212921143 -"Sample_4_CAGTCCTTCTCTTGAT",3.8479175567627,4.97932195663452 -"Sample_4_CATATGGTCTGTGCAA",2.09922289848328,-4.70929527282715 -"Sample_4_CATATTCCAGTGACAG",-2.66324639320374,6.69612121582031 -"Sample_4_CATATTCGTAAATACG",2.51683568954468,8.21828174591064 -"Sample_4_CATCAAGAGGTGTTAA",0.246334820985794,5.78072595596313 -"Sample_4_CATCAAGCACGACGAA",5.16487169265747,6.68360328674316 -"Sample_4_CATCAAGCAGGCAGTA",3.76885175704956,-9.47387313842773 -"Sample_4_CATCAAGCATGTTGAC",0.753702700138092,2.57761478424072 -"Sample_4_CATCAAGGTCTCCACT",3.86490988731384,-9.48742485046387 -"Sample_4_CATCAGAAGAAACCAT",-0.281968593597412,5.86942911148071 -"Sample_4_CATCAGACACCCTATC",3.32271528244019,5.64789438247681 -"Sample_4_CATCAGAGTCCATGAT",0.542875111103058,-9.09690761566162 -"Sample_4_CATCAGAGTCCGAAGA",-1.28924214839935,-0.0808154940605164 -"Sample_4_CATCCACGTCCCTTGT",-0.0922311618924141,5.14866828918457 -"Sample_4_CATCCACGTGCATCTA",0.035347692668438,6.55699062347412 -"Sample_4_CATCCACTCTGAGGGA",3.06420087814331,-9.21440982818604 -"Sample_4_CATCGAAAGGAATCGC",-1.08698844909668,6.85142230987549 -"Sample_4_CATCGAACAGGCTGAA",-2.55363869667053,7.41505336761475 -"Sample_4_CATCGAACAGGGAGAG",3.0362401008606,7.06713724136353 -"Sample_4_CATCGGGCAAAGGTGC",0.0243236906826496,-11.2618923187256 -"Sample_4_CATGACAAGTGAACGC",2.67769050598145,7.30754327774048 -"Sample_4_CATGACACACAGACTT",2.00833654403687,-10.5155076980591 -"Sample_4_CATGACACACCGTTGG",2.93715476989746,3.84529709815979 -"Sample_4_CATGACACAGCTATTG",3.65375351905823,-9.3817195892334 -"Sample_4_CATGACAGTCTCACCT",2.97021102905273,4.77485466003418 -"Sample_4_CATGCCTCAATGAAAC",1.52851974964142,-9.78715324401855 -"Sample_4_CATGGCGAGTGTGGCA",2.75222754478455,-7.21822786331177 -"Sample_4_CATGGCGCACGAGGTA",1.96093702316284,-8.02787208557129 -"Sample_4_CATGGCGGTACAGTGG",0.305569916963577,-8.43215847015381 -"Sample_4_CATTATCAGGGTTCCC",2.49715375900269,-10.3624095916748 -"Sample_4_CATTATCCACAGCCCA",-3.83073496818542,8.62426280975342 -"Sample_4_CATTATCGTGGTCTCG",1.98200595378876,7.47884464263916 -"Sample_4_CCAATCCGTCTCTTAT",-0.541877031326294,9.56807136535645 -"Sample_4_CCAATCCTCTCATTCA",-1.3502916097641,8.88281917572021 -"Sample_4_CCACCTATCTTGCCGT",0.759855031967163,-11.1925487518311 -"Sample_4_CCACGGACAATCACAC",3.47255277633667,-9.91471290588379 -"Sample_4_CCACGGATCAGGCAAG",2.31029558181763,-4.83527517318726 -"Sample_4_CCACTACAGTGGACGT",3.74550342559814,6.19755125045776 -"Sample_4_CCAGCGAGTTCGTTGA",4.42884016036987,7.19988441467285 -"Sample_4_CCATTCGCAAGCCATT",-2.20666813850403,9.40115356445312 -"Sample_4_CCCAATCTCAACACCA",3.81726980209351,7.24668455123901 -"Sample_4_CCCTCCTAGTAGCCGA",-0.649967789649963,-9.48384952545166 -"Sample_4_CCCTCCTGTTGTACAC",-2.41132116317749,9.12191867828369 -"Sample_4_CCGGTAGAGTTACGGG",-1.78318679332733,9.40444183349609 -"Sample_4_CCGGTAGTCGCCTGAG",-0.362636178731918,-10.1273860931396 -"Sample_4_CCGTACTCAGGGTATG",3.99856400489807,6.13421297073364 -"Sample_4_CCGTACTTCTACTATC",2.19634461402893,-10.1909503936768 -"Sample_4_CCGTTCAAGCCCGAAA",1.09164237976074,-8.95924377441406 -"Sample_4_CCTAAAGAGCGATTCT",3.69366049766541,5.4032301902771 -"Sample_4_CCTAAAGCAAGTAATG",3.46142077445984,6.19719266891479 -"Sample_4_CCTACACTCTTTACGT",3.13015651702881,-10.9573211669922 -"Sample_4_CCTACCACATCGGAAG",-0.887716293334961,0.23839445412159 -"Sample_4_CCTAGCTAGAGGTACC",-1.3592209815979,8.04293060302734 -"Sample_4_CCTATTATCAAACCAC",0.774684965610504,7.9462718963623 -"Sample_4_CCTCTGAGTAAGAGGA",-0.826628684997559,0.233779400587082 -"Sample_4_CCTCTGAGTTATTCTC",3.36406683921814,7.10299730300903 -"Sample_4_CCTCTGATCAGCCTAA",0.749646902084351,-8.06550598144531 -"Sample_4_CCTTACGAGCACCGCT",2.25183844566345,-9.43730545043945 -"Sample_4_CCTTACGGTCGCATCG",1.83694529533386,-6.6435694694519 -"Sample_4_CCTTCCCCAAACCTAC",-0.198623076081276,9.47645950317383 -"Sample_4_CCTTCCCTCACTGGGC",-1.83327043056488,7.38699150085449 -"Sample_4_CCTTCGAAGCGTTTAC",3.47576665878296,-8.51034736633301 -"Sample_4_CCTTCGACAAGAAGAG",3.5662407875061,6.91966342926025 -"Sample_4_CGAACATAGTCAAGCG",1.89536428451538,-6.85836505889893 -"Sample_4_CGAACATCATCAGTCA",3.93132901191711,3.95299291610718 -"Sample_4_CGAATGTGTCATTAGC",1.10314679145813,8.5892972946167 -"Sample_4_CGACCTTAGCACCGCT",-0.253065824508667,5.34703874588013 -"Sample_4_CGACCTTAGTTGTAGA",-9.55367946624756,-2.64242100715637 -"Sample_4_CGACCTTTCAAGCCTA",-0.98992520570755,0.725528180599213 -"Sample_4_CGACTTCTCATAGCAC",3.08417701721191,-8.87587451934814 -"Sample_4_CGAGCACAGACGCAAC",1.32329297065735,-10.1918830871582 -"Sample_4_CGAGCACAGCTCAACT",5.32076787948608,6.49257898330688 -"Sample_4_CGAGCACAGGCGTACA",-2.94087171554565,7.7086443901062 -"Sample_4_CGAGCACTCTGCAAGT",-2.83979749679565,7.9252233505249 -"Sample_4_CGAGCCACATGCAACT",-2.08019113540649,7.25218200683594 -"Sample_4_CGAGCCATCGTGGTCG",0.190497323870659,4.32825374603271 -"Sample_4_CGATCGGAGACGCTTT",-0.773147284984589,1.41506958007812 -"Sample_4_CGATCGGGTCCCTTGT",-0.528613209724426,1.16456043720245 -"Sample_4_CGATGGCTCGGTCCGA",-0.350359976291656,9.59238719940186 -"Sample_4_CGATGTAAGGACATTA",2.70415425300598,5.82664203643799 -"Sample_4_CGATTGAGTGGTACAG",2.35494804382324,-7.35420608520508 -"Sample_4_CGCCAAGAGACCACGA",-1.44305884838104,0.32533672451973 -"Sample_4_CGCCAAGGTGTCCTCT",2.54869365692139,-11.2578582763672 -"Sample_4_CGCGGTAAGGCATGGT",3.35390257835388,-8.49584484100342 -"Sample_4_CGCGGTAAGTATCGAA",1.45929300785065,-10.3366947174072 -"Sample_4_CGCGGTACACAGGCCT",-0.607682168483734,-9.49770450592041 -"Sample_4_CGCGGTATCAACGCTA",1.54290068149567,-8.08938598632812 -"Sample_4_CGCGGTATCTCTGCTG",-1.1296398639679,5.07925653457642 -"Sample_4_CGCGTTTTCCAGATCA",1.4546959400177,2.61207580566406 -"Sample_4_CGCTATCCACTGTCGG",-2.3868350982666,7.15400409698486 -"Sample_4_CGCTATCGTGCAGACA",1.54044330120087,-10.227858543396 -"Sample_4_CGCTATCTCGCCATAA",1.062619805336,-7.86687278747559 -"Sample_4_CGCTATCTCTGTACGA",0.642769694328308,4.79257440567017 -"Sample_4_CGCTGGATCCTAGGGC",-0.349677801132202,7.43659067153931 -"Sample_4_CGCTTCACATTTCAGG",3.18506240844727,5.50397443771362 -"Sample_4_CGGACACAGCAGCGTA",-1.69395339488983,9.65801906585693 -"Sample_4_CGGACACAGGTGCTTT",0.723231375217438,-8.61409664154053 -"Sample_4_CGGACACTCCTCGCAT",-0.921796262264252,2.71702575683594 -"Sample_4_CGGACGTTCAGGTTCA",3.83988213539124,7.66978120803833 -"Sample_4_CGGACTGTCTCTGAGA",-0.45953157544136,7.32730531692505 -"Sample_4_CGGAGCTCAAACTGTC",3.28523921966553,3.92335724830627 -"Sample_4_CGGAGCTGTACCGTTA",3.91034436225891,7.25612497329712 -"Sample_4_CGGAGTCTCAGGCCCA",0.370376229286194,-9.9898853302002 -"Sample_4_CGTAGCGCAAACAACA",3.28239107131958,-7.27099084854126 -"Sample_4_CGTAGCGTCAGCACAT",2.7889256477356,-8.04408073425293 -"Sample_4_CGTAGCGTCCTAGAAC",1.96660578250885,-4.69207048416138 -"Sample_4_CGTAGGCGTCTGATTG",-0.540888547897339,7.58581209182739 -"Sample_4_CGTCAGGCAAGCTGTT",-0.0512369237840176,-9.26284599304199 -"Sample_4_CGTCAGGTCAAGGTAA",-2.36622452735901,9.19214534759521 -"Sample_4_CGTCCATAGCTATGCT",2.04772734642029,-8.20678806304932 -"Sample_4_CGTCCATCACCCTATC",2.80704021453857,5.87118864059448 -"Sample_4_CGTCTACCATAGGATA",-1.05429327487946,8.8824520111084 -"Sample_4_CGTGAGCCATTCTTAC",1.83727610111237,8.04073715209961 -"Sample_4_CGTGAGCTCAATCTCT",-9.71537780761719,-2.8054986000061 -"Sample_4_CGTTGGGTCAGAGACG",4.21422386169434,7.29021406173706 -"Sample_4_CGTTGGGTCCTTGCCA",-1.31996488571167,5.95880365371704 -"Sample_4_CTAACTTAGCCCAGCT",3.60165405273438,5.64022207260132 -"Sample_4_CTAAGACAGTGTCTCA",0.662050008773804,-10.4070987701416 -"Sample_4_CTAAGACCAGATCGGA",1.88608813285828,8.11819362640381 -"Sample_4_CTAAGACGTCGGATCC",1.87449193000793,-4.63822603225708 -"Sample_4_CTAAGACTCCACTGGG",1.93803751468658,-4.91024684906006 -"Sample_4_CTAATGGCACAGACTT",-1.43137001991272,7.56065654754639 -"Sample_4_CTAATGGGTTCAGGCC",1.77365148067474,-7.02016925811768 -"Sample_4_CTACACCCAACGCACC",2.23020839691162,-8.5327091217041 -"Sample_4_CTACACCGTTCAGCGC",3.53624677658081,-9.87158203125 -"Sample_4_CTACACCTCGGTTCGG",2.065110206604,5.52213191986084 -"Sample_4_CTACATTCATGCCCGA",3.07442283630371,-8.81100177764893 -"Sample_4_CTACATTGTTTGGCGC",3.41028308868408,-7.68258142471313 -"Sample_4_CTACCCAAGGTGCACA",2.45562028884888,-5.15124702453613 -"Sample_4_CTACCCACAAGGTGTG",-1.37074077129364,7.78947496414185 -"Sample_4_CTACCCACACGACTCG",2.2301549911499,-7.07437515258789 -"Sample_4_CTACCCACAGCGATCC",-0.0115415276959538,-10.9189796447754 -"Sample_4_CTACGTCAGAGCTGCA",-1.30370879173279,8.59014511108398 -"Sample_4_CTACGTCAGGCGATAC",3.14234137535095,7.08226776123047 -"Sample_4_CTACGTCGTCTCAACA",1.80032920837402,-8.60989379882812 -"Sample_4_CTACGTCTCGTCGTTC",2.01942753791809,-8.54211235046387 -"Sample_4_CTAGAGTCAATCTACG",1.06161785125732,-9.60361099243164 -"Sample_4_CTAGAGTCAGGAATCG",-0.441415190696716,0.802632212638855 -"Sample_4_CTAGAGTTCAACTCTT",3.16670107841492,6.16503572463989 -"Sample_4_CTAGTGACAGACGCTC",-1.17568814754486,7.01918458938599 -"Sample_4_CTAGTGACAGCGTAAG",-0.58835506439209,7.02424383163452 -"Sample_4_CTAGTGACATCTATGG",0.903838694095612,6.63483953475952 -"Sample_4_CTAGTGATCCTTGACC",-0.151729226112366,9.07928848266602 -"Sample_4_CTCACACTCACATACG",2.84571480751038,5.239098072052 -"Sample_4_CTCAGAATCGGAAATA",-1.29622542858124,1.72433984279633 -"Sample_4_CTCATTAAGTCGTTTG",-0.0216594841331244,8.19995021820068 -"Sample_4_CTCCTAGTCGGCGCTA",-1.00029146671295,0.814678966999054 -"Sample_4_CTCGAAAAGAACAATC",0.732138156890869,5.21873617172241 -"Sample_4_CTCGAAAAGACGACGT",-0.980774581432343,3.15572476387024 -"Sample_4_CTCGAAACACAGCGTC",4.61449813842773,-8.86505794525146 -"Sample_4_CTCGGGAGTTCGGCAC",0.658286869525909,5.38132286071777 -"Sample_4_CTCGTCACACACTGCG",0.784248530864716,-9.96391105651855 -"Sample_4_CTCGTCACACAGGAGT",0.52077579498291,-9.72446823120117 -"Sample_4_CTCGTCAGTTCCACGG",4.50846242904663,4.42498111724854 -"Sample_4_CTCTAATAGTCCGTAT",0.644814908504486,-11.0041341781616 -"Sample_4_CTCTAATAGTGGGTTG",4.23060894012451,5.00617361068726 -"Sample_4_CTCTACGAGGTGCACA",3.95114231109619,4.6629433631897 -"Sample_4_CTCTACGTCGAACGGA",4.7053050994873,7.01615238189697 -"Sample_4_CTCTGGTAGCGATATA",3.47962355613708,4.87794256210327 -"Sample_4_CTCTGGTAGGGAACGG",-0.412609487771988,8.2377872467041 -"Sample_4_CTCTGGTAGGTCGGAT",2.86601662635803,7.1994047164917 -"Sample_4_CTCTGGTCACTGCCAG",3.33236575126648,3.91104912757874 -"Sample_4_CTGAAACGTGCCTGTG",0.545451879501343,-8.09907054901123 -"Sample_4_CTGAAGTCACGAAGCA",-2.11571025848389,8.14039993286133 -"Sample_4_CTGAAGTTCCGCAAGC",2.40491962432861,-8.86037921905518 -"Sample_4_CTGAAGTTCGCAAACT",-2.02889680862427,8.51068782806396 -"Sample_4_CTGATAGAGCGTCAAG",-0.148602157831192,9.55273246765137 -"Sample_4_CTGATCCTCATAACCG",4.8563346862793,6.07413005828857 -"Sample_4_CTGATCCTCGCAAGCC",-2.53957796096802,8.39149475097656 -"Sample_4_CTGATCCTCTACTATC",-1.1503632068634,7.01016616821289 -"Sample_4_CTGCCTAAGAAGGTGA",0.904857516288757,-9.31028175354004 -"Sample_4_CTGCCTAAGACAGAGA",0.756010711193085,3.19539856910706 -"Sample_4_CTGCCTACAGACAGGT",4.09913921356201,-9.85410976409912 -"Sample_4_CTGCTGTCAACGATCT",0.0339944921433926,-7.80442380905151 -"Sample_4_CTGGTCTCAACCGCCA",4.55977630615234,4.9577751159668 -"Sample_4_CTGGTCTGTATCAGTC",-0.163820058107376,-10.1280717849731 -"Sample_4_CTGGTCTGTTGACGTT",1.89174854755402,7.96364307403564 -"Sample_4_CTGGTCTGTTGTCGCG",-1.52351379394531,9.24864387512207 -"Sample_4_CTGTGCTCACATGGGA",-0.523701667785645,9.35876655578613 -"Sample_4_CTGTGCTCACGGCCAT",2.57604312896729,5.46818542480469 -"Sample_4_CTGTGCTTCAGCGATT",4.13501739501953,7.36677169799805 -"Sample_4_CTTAACTGTGGACGAT",-1.08851373195648,8.33471870422363 -"Sample_4_CTTACCGAGCGTCAAG",2.03011083602905,7.48927068710327 -"Sample_4_CTTAGGAAGCCAACAG",-1.03774440288544,8.06512546539307 -"Sample_4_CTTCTCTAGTATCGAA",3.13842058181763,5.21912717819214 -"Sample_4_CTTCTCTAGTGCGATG",1.56030488014221,-10.5971689224243 -"Sample_4_CTTTGCGTCGGGAGTA",3.21726894378662,7.81453084945679 -"Sample_4_GAAACTCAGTACGTTC",-0.0722941383719444,3.0442430973053 -"Sample_4_GAAACTCGTCTGCCAG",1.41671407222748,1.76972341537476 -"Sample_4_GAAATGAAGAAGGGTA",2.49100112915039,5.23477792739868 -"Sample_4_GAAATGAGTGTTAAGA",2.20198965072632,5.61635732650757 -"Sample_4_GAACATCTCCTTGGTC",0.417959570884705,8.93068408966064 -"Sample_4_GAACCTACAACACCCG",-2.55022239685059,8.62898349761963 -"Sample_4_GAACGGAAGCAAATCA",2.76051425933838,6.69839286804199 -"Sample_4_GAAGCAGGTTCAGGCC",2.66348147392273,-10.1153745651245 -"Sample_4_GAAGCAGTCTGTGCAA",2.85053014755249,-10.7694835662842 -"Sample_4_GAATAAGAGCGATTCT",2.34409928321838,6.32629680633545 -"Sample_4_GAATAAGAGTTGAGAT",0.451137244701385,-9.29934120178223 -"Sample_4_GAATAAGGTACCGTTA",-0.906922996044159,9.71813869476318 -"Sample_4_GAATAAGTCCACTCCA",0.364830940961838,-10.4551219940186 -"Sample_4_GACACGCCACGACGAA",1.85791599750519,6.332200050354 -"Sample_4_GACACGCGTAACGTTC",-0.385885506868362,4.16464567184448 -"Sample_4_GACAGAGAGGCGTACA",1.7986261844635,-5.76750612258911 -"Sample_4_GACAGAGTCGAGAACG",-1.22528493404388,0.469820827245712 -"Sample_4_GACCAATGTGAGCGAT",-0.586983323097229,3.49679851531982 -"Sample_4_GACCTGGCACTTCGAA",2.07273411750793,-9.86728572845459 -"Sample_4_GACCTGGCAGTAAGAT",-0.490604519844055,0.0147863179445267 -"Sample_4_GACGGCTGTTGATTGC",-0.20732544362545,-10.2955532073975 -"Sample_4_GACGGCTGTTTGGGCC",3.59995174407959,8.15137004852295 -"Sample_4_GACGGCTTCAACGCTA",2.86558723449707,6.3006157875061 -"Sample_4_GACGTGCTCACCATAG",-1.23814153671265,0.261791884899139 -"Sample_4_GACGTTAAGAATAGGG",0.81626033782959,-8.64441967010498 -"Sample_4_GACGTTAAGTGTACGG",-0.723692119121552,8.21931552886963 -"Sample_4_GACTACAAGTACACCT",3.7502076625824,7.46552705764771 -"Sample_4_GACTACATCGGAATCT",2.0419807434082,-9.06209373474121 -"Sample_4_GACTGCGGTTCGGCAC",5.03668117523193,6.16010570526123 -"Sample_4_GACTGCGTCAGTTGAC",-1.81715440750122,7.72609281539917 -"Sample_4_GACTGCGTCTGTACGA",-3.03465247154236,7.62672424316406 -"Sample_4_GAGCAGAAGTGCTGCC",-2.15155577659607,9.19285678863525 -"Sample_4_GAGCAGAGTCTTGATG",-0.660983443260193,-9.23805904388428 -"Sample_4_GAGGTGAGTCCTAGCG",-3.88304853439331,8.91674423217773 -"Sample_4_GAGGTGATCCCTAACC",4.53762006759644,-8.25377750396729 -"Sample_4_GATCAGTAGAGTTGGC",-0.716016054153442,9.0363187789917 -"Sample_4_GATCAGTAGTAGCGGT",-2.31725335121155,8.80664157867432 -"Sample_4_GATCAGTCAAAGCGGT",-0.69595068693161,8.8181734085083 -"Sample_4_GATCAGTTCCGAGCCA",0.507834613323212,-9.86602973937988 -"Sample_4_GATCAGTTCCTCGCAT",0.417182296514511,-8.63998222351074 -"Sample_4_GATCGATGTTCCCGAG",-1.19271612167358,7.84647941589355 -"Sample_4_GATCGATTCCACGCAG",1.98945033550262,-9.87321281433105 -"Sample_4_GATCGCGAGACCTTTG",3.5277054309845,4.56473541259766 -"Sample_4_GATCGCGCAAGAAAGG",2.41729044914246,-4.97959327697754 -"Sample_4_GATCGCGGTCAACTGT",4.43166017532349,-8.29131317138672 -"Sample_4_GATCGCGTCAAGGTAA",0.375902056694031,-7.13705205917358 -"Sample_4_GATCGTAAGGTACTCT",-1.23263216018677,8.73010063171387 -"Sample_4_GATGAAAAGACACTAA",-1.36091589927673,8.98454761505127 -"Sample_4_GATGAAATCGTCTGAA",0.539239883422852,-9.85723876953125 -"Sample_4_GATGAGGTCCTAGGGC",2.81008338928223,-7.77062845230103 -"Sample_4_GATGCTAAGTCCCACG",-2.26488327980042,7.98031425476074 -"Sample_4_GATGCTACAAAGGAAG",3.18330240249634,4.13144826889038 -"Sample_4_GATGCTAGTAGAGGAA",3.32802820205688,5.94809436798096 -"Sample_4_GATTCAGAGACAGGCT",0.495007395744324,2.60995268821716 -"Sample_4_GCAAACTCATCCCATC",0.166068121790886,1.93350827693939 -"Sample_4_GCAAACTGTGGACGAT",2.61057472229004,-7.3010835647583 -"Sample_4_GCAATCAAGTAGGCCA",3.87194108963013,-9.58748722076416 -"Sample_4_GCAATCAGTCTAGTCA",3.41527366638184,-8.00953960418701 -"Sample_4_GCAATCATCACCCTCA",1.19235920906067,-8.71486949920654 -"Sample_4_GCAGTTACAACGATGG",1.09853327274323,-10.6549663543701 -"Sample_4_GCAGTTAGTTCAGCGC",-0.361817866563797,9.3632698059082 -"Sample_4_GCATACATCCTGCCAT",4.35754728317261,4.31616115570068 -"Sample_4_GCATACATCTTTAGGG",2.58932089805603,-10.238655090332 -"Sample_4_GCATGCGGTCCCTTGT",0.817392826080322,5.41855812072754 -"Sample_4_GCATGTAAGACGCACA",2.19864106178284,6.52956342697144 -"Sample_4_GCATGTAAGAGTAATC",-1.76698863506317,1.24423217773438 -"Sample_4_GCATGTACACCTCGTT",-0.655803799629211,-0.140363126993179 -"Sample_4_GCCAAATAGTGGAGAA",5.30239868164062,5.68880414962769 -"Sample_4_GCCTCTACAAACTGCT",1.73125517368317,2.98586392402649 -"Sample_4_GCGACCACATGCCCGA",4.33183002471924,4.73381328582764 -"Sample_4_GCGAGAAAGCTACCGC",-0.993173360824585,9.38336372375488 -"Sample_4_GCGAGAACAAGGCTCC",2.07136607170105,6.93732023239136 -"Sample_4_GCGAGAACACCAGTTA",0.560653984546661,-8.54242038726807 -"Sample_4_GCGCAACAGGGTGTGT",-0.809902310371399,5.02618360519409 -"Sample_4_GCGCAACCACCCAGTG",1.20489227771759,-8.8532018661499 -"Sample_4_GCGCAACTCCTATGTT",1.62560045719147,7.97047710418701 -"Sample_4_GCGCAACTCTTGCATT",0.67878657579422,-7.54980564117432 -"Sample_4_GCGCAGTTCCGTCATC",-0.300557881593704,-0.168840706348419 -"Sample_4_GCGCCAACACCGTTGG",-3.91555547714233,8.94174385070801 -"Sample_4_GCGCGATTCCGTAGTA",2.58385920524597,-9.09524822235107 -"Sample_4_GCGGGTTGTGCGCTTG",-1.16308581829071,5.42116785049438 -"Sample_4_GCTCCTAGTAGAAGGA",-0.27324190735817,0.797622442245483 -"Sample_4_GCTCTGTCAAGTCTAC",0.0685698688030243,-9.65400886535645 -"Sample_4_GCTGCAGAGTGGGATC",3.84816479682922,7.48202133178711 -"Sample_4_GCTGCAGCACCACCAG",1.15521311759949,-11.0129737854004 -"Sample_4_GCTGCAGGTCGATTGT",0.640073597431183,-9.90541553497314 -"Sample_4_GCTGCAGTCCTTTACA",3.11285805702209,3.99834227561951 -"Sample_4_GCTGCAGTCGGAGGTA",4.17864513397217,-8.263991355896 -"Sample_4_GCTGCGAAGGCTACGA",-0.362784326076508,0.0673495456576347 -"Sample_4_GCTGCGAGTGTCAATC",-0.692948698997498,9.30006790161133 -"Sample_4_GCTGCGATCTGATACG",0.915777981281281,-9.51488590240479 -"Sample_4_GCTGGGTCATATGCTG",-0.131171464920044,-10.562292098999 -"Sample_4_GCTTCCACACATCCGG",4.79168033599854,4.68096017837524 -"Sample_4_GCTTCCACATCTATGG",-0.816711783409119,-9.61010360717773 -"Sample_4_GCTTCCATCTCTGTCG",0.454681098461151,4.40617227554321 -"Sample_4_GCTTGAACAGTATAAG",3.7943229675293,6.67590999603271 -"Sample_4_GCTTGAATCTAACCGA",0.198622986674309,-8.32938003540039 -"Sample_4_GGAAAGCTCCTTGCCA",2.06951451301575,-8.07919406890869 -"Sample_4_GGAACTTCAGATGGCA",4.04993963241577,4.82511138916016 -"Sample_4_GGAATAACAAATTGCC",4.28509712219238,-8.38352298736572 -"Sample_4_GGAATAACAGTTCATG",-2.45329236984253,8.85737133026123 -"Sample_4_GGACATTCACTTCGAA",2.99975943565369,-9.64812660217285 -"Sample_4_GGAGCAACAAGCGCTC",2.59820199012756,6.5576343536377 -"Sample_4_GGAGCAACATCTCCCA",-0.0336346812546253,6.35661029815674 -"Sample_4_GGAGCAATCGCATGGC",-0.439368009567261,-7.0039529800415 -"Sample_4_GGAGCAATCGTAGATC",-0.202099934220314,-11.1647386550903 -"Sample_4_GGATGTTAGTCCGTAT",-2.57144355773926,8.1316967010498 -"Sample_4_GGATTACCATTGAGCT",0.136021092534065,-9.53519821166992 -"Sample_4_GGATTACTCACCAGGC",-0.0264776572585106,5.1288480758667 -"Sample_4_GGCAATTAGAGGTAGA",3.19847536087036,4.46668100357056 -"Sample_4_GGCAATTAGTGTTGAA",-1.66317391395569,1.38391137123108 -"Sample_4_GGCAATTCAGATTGCT",-0.536256670951843,3.78329348564148 -"Sample_4_GGCAATTGTCAAACTC",1.06483554840088,-9.30867767333984 -"Sample_4_GGCAATTGTGATAAGT",4.52600193023682,4.40410566329956 -"Sample_4_GGCCGATGTTTCCACC",2.55496287345886,-10.5852136611938 -"Sample_4_GGCGACTCATCCTAGA",0.921071350574493,-8.94488143920898 -"Sample_4_GGCGACTCATCCTTGC",-0.113928690552711,6.72214889526367 -"Sample_4_GGCGTGTCACATGACT",1.04193603992462,-9.52906322479248 -"Sample_4_GGGAATGAGATGGCGT",-2.07443404197693,8.92208099365234 -"Sample_4_GGGAATGGTCATCCCT",-1.16411626338959,8.04463863372803 -"Sample_4_GGGACCTTCTTGGGTA",-0.335170537233353,-0.107734657824039 -"Sample_4_GGGAGATAGTGTTTGC",-0.548690497875214,8.77071762084961 -"Sample_4_GGGAGATCAGATGGCA",0.170355260372162,4.6270956993103 -"Sample_4_GGGAGATCATACCATG",-1.58050084114075,7.70808506011963 -"Sample_4_GGGAGATGTCAAACTC",4.46578645706177,-9.69121074676514 -"Sample_4_GGGATGAGTCGTCTTC",3.20505475997925,6.23378133773804 -"Sample_4_GGGATGATCAGTTCGA",1.5357780456543,6.34644269943237 -"Sample_4_GGGCACTAGCAGGTCA",3.95869493484497,-8.9282808303833 -"Sample_4_GGGCACTCAAAGCAAT",-0.971709907054901,3.7784104347229 -"Sample_4_GGGCACTTCCTAGGGC",-1.39314639568329,8.38517284393311 -"Sample_4_GGGCACTTCTATCGCC",0.747305631637573,-9.23353481292725 -"Sample_4_GGGCATCCACGGCGTT",0.18043614923954,-9.09004497528076 -"Sample_4_GGGCATCCATCACAAC",0.146525129675865,3.4598753452301 -"Sample_4_GGGCATCGTTCCTCCA",4.93719673156738,6.10484170913696 -"Sample_4_GGGCATCTCCGCATCT",2.24211454391479,6.00154256820679 -"Sample_4_GGTATTGGTGAGGGTT",-1.33870267868042,6.66053485870361 -"Sample_4_GGTATTGGTTCCCGAG",0.0838364586234093,4.14972019195557 -"Sample_4_GGTGAAGAGAGGTAGA",0.845558822154999,1.97352039813995 -"Sample_4_GGTGAAGTCCATGAGT",0.551440060138702,6.90953397750854 -"Sample_4_GGTGAAGTCCTCATTA",2.13707089424133,-8.1862325668335 -"Sample_4_GGTGCGTAGAGTACAT",1.35472738742828,-10.6533288955688 -"Sample_4_GGTGCGTAGTACATGA",-0.0204605031758547,5.67822170257568 -"Sample_4_GGTGTTAGTAGTAGTA",-0.917377471923828,0.193556562066078 -"Sample_4_GTAACGTAGGACAGCT",2.43856263160706,5.90525007247925 -"Sample_4_GTAACGTTCACTTACT",2.50395035743713,5.04548740386963 -"Sample_4_GTAACTGTCCGAACGC",-0.584291279315948,-7.26534557342529 -"Sample_4_GTACGTAAGACACTAA",-0.799187779426575,-6.87278032302856 -"Sample_4_GTACGTACATGCTGGC",3.2635440826416,5.23559284210205 -"Sample_4_GTACGTATCCTGTAGA",-2.88656187057495,8.48159027099609 -"Sample_4_GTACTTTGTGTCAATC",0.34433051943779,8.4898681640625 -"Sample_4_GTAGGCCAGCGCTCCA",2.61811876296997,7.78613138198853 -"Sample_4_GTAGGCCGTAACGTTC",0.250970870256424,-9.64937686920166 -"Sample_4_GTATCTTTCCTATGTT",2.28350973129272,7.14672803878784 -"Sample_4_GTATTCTCAGCTGCTG",2.91013836860657,6.39682960510254 -"Sample_4_GTATTCTGTCCCTACT",0.851823031902313,2.56911420822144 -"Sample_4_GTATTCTTCCAATGGT",3.60863018035889,6.21348762512207 -"Sample_4_GTCACAAGTCCGAAGA",-1.28522157669067,8.54194831848145 -"Sample_4_GTCACAAGTTCCACAA",-0.0721996501088142,-8.38572883605957 -"Sample_4_GTCACGGCACCGGAAA",-2.00060367584229,8.01577186584473 -"Sample_4_GTCACGGGTTGCCTCT",4.92973470687866,6.07403612136841 -"Sample_4_GTCACGGTCAAGGCTT",-1.67163908481598,1.37842273712158 -"Sample_4_GTCATTTAGCTTTGGT",1.09552788734436,-10.0902194976807 -"Sample_4_GTCATTTTCAGTTTGG",2.96609401702881,6.45203733444214 -"Sample_4_GTCCTCAAGTATCGAA",-1.4478896856308,9.45058059692383 -"Sample_4_GTCCTCAAGTGCCATT",4.51319742202759,5.84762859344482 -"Sample_4_GTCGGGTGTCCTCTTG",3.55742073059082,7.83484125137329 -"Sample_4_GTCGTAATCCATGAGT",0.585071384906769,3.36078572273254 -"Sample_4_GTCTCGTAGTTCGCAT",-2.14251923561096,8.80745220184326 -"Sample_4_GTCTTCGGTATAAACG",3.39027881622314,3.55169010162354 -"Sample_4_GTGAAGGAGTACGACG",1.56009018421173,-9.32473182678223 -"Sample_4_GTGAAGGCAATAACGA",1.55118072032928,-8.4436206817627 -"Sample_4_GTGAAGGCATGTTGAC",3.53449463844299,6.94433641433716 -"Sample_4_GTGAAGGGTCCTCTTG",0.738539397716522,4.61432361602783 -"Sample_4_GTGAAGGTCATGTAGC",1.77418756484985,-5.24987745285034 -"Sample_4_GTGCAGCAGGCTACGA",4.86850214004517,4.44997310638428 -"Sample_4_GTGCAGCTCAAAGACA",-2.06569147109985,7.60121011734009 -"Sample_4_GTGCATACATCCGGGT",2.64701294898987,-9.94343280792236 -"Sample_4_GTGCATATCACCGTAA",1.5187873840332,-7.03052091598511 -"Sample_4_GTGCATATCATTATCC",2.2845983505249,-9.22703075408936 -"Sample_4_GTGCGGTAGAGGTACC",3.43324160575867,7.60318183898926 -"Sample_4_GTGCGGTCAAGCCGTC",0.292840480804443,-8.64549160003662 -"Sample_4_GTGCGGTCACATTCGA",-2.3158552646637,8.16868591308594 -"Sample_4_GTGCTTCAGCCAACAG",2.07118368148804,-7.51876640319824 -"Sample_4_GTGGGTCTCAAGAAGT",0.222942650318146,1.21270966529846 -"Sample_4_GTGTTAGCATCCTAGA",2.85655403137207,-9.55843448638916 -"Sample_4_GTGTTAGGTAGATTAG",2.79255104064941,6.22996044158936 -"Sample_4_GTTACAGCATTAGGCT",-1.99654519557953,8.27771663665771 -"Sample_4_GTTCATTCATGCATGT",3.09515738487244,-10.7979593276978 -"Sample_4_GTTCATTTCCAAACAC",-0.84246289730072,7.89794683456421 -"Sample_4_GTTCTCGTCTAGCACA",2.39494609832764,-9.791090965271 -"Sample_4_GTTTCTAAGATTACCC",-0.919292807579041,4.48605394363403 -"Sample_4_GTTTCTAAGGTGCAAC",-1.00798451900482,6.93204879760742 -"Sample_4_GTTTCTACATCACGAT",-1.85675251483917,9.05569267272949 -"Sample_4_TAAACCGAGTTCGATC",4.19866371154785,-8.50193119049072 -"Sample_4_TAAACCGGTCTGCGGT",2.30050086975098,6.72130680084229 -"Sample_4_TAAACCGGTTCGCGAC",4.2095832824707,5.65017557144165 -"Sample_4_TAAGAGAGTACTTAGC",-0.11424458026886,-9.08722400665283 -"Sample_4_TAAGAGATCCGAAGAG",-1.82684791088104,9.10421085357666 -"Sample_4_TAAGCGTAGGACTGGT",0.920004725456238,-7.46937894821167 -"Sample_4_TAAGTGCCAGACAAAT",3.17179274559021,-10.8727874755859 -"Sample_4_TAAGTGCTCGCCATAA",1.04929757118225,-8.90903568267822 -"Sample_4_TACACGAGTCACTTCC",3.08858442306519,-10.2619571685791 -"Sample_4_TACACGAGTTTCGCTC",-3.60197019577026,8.94740962982178 -"Sample_4_TACACGATCCCAGGTG",0.514220774173737,2.18630695343018 -"Sample_4_TACCTATGTCCAACTA",1.84309017658234,8.06527042388916 -"Sample_4_TACCTTAAGCTAACAA",2.09974789619446,-10.1599760055542 -"Sample_4_TACCTTATCTGGCGTG",3.48149061203003,-7.41972875595093 -"Sample_4_TACGGGCCATCCTAGA",-0.844205915927887,6.71220302581787 -"Sample_4_TACGGGCCATTACCTT",-0.839768171310425,3.0155553817749 -"Sample_4_TACGGTAAGGATGGAA",2.46150755882263,6.47106790542603 -"Sample_4_TACTCATTCGGCCGAT",-1.00912308692932,2.30642175674438 -"Sample_4_TACTCGCCAACTGCGC",0.13128200173378,-9.45115852355957 -"Sample_4_TACTCGCTCCGAATGT",-1.00198912620544,9.41030693054199 -"Sample_4_TACTCGCTCGTAGGTT",-0.959559381008148,8.21357917785645 -"Sample_4_TACTTACTCCGTTGTC",-0.362355411052704,-10.3800296783447 -"Sample_4_TACTTGTGTACTTAGC",0.670386970043182,-8.56776523590088 -"Sample_4_TAGCCGGCAATGTTGC",2.16743731498718,6.43622064590454 -"Sample_4_TAGCCGGTCGAGAACG",0.547460794448853,2.6642107963562 -"Sample_4_TAGGCATCAGTCCTTC",-0.145308628678322,5.16295385360718 -"Sample_4_TAGGCATGTTGTACAC",-1.31761491298676,9.72503471374512 -"Sample_4_TAGTGGTCAAGGGTCA",1.92702996730804,-8.72838306427002 -"Sample_4_TAGTGGTTCAATACCG",-2.47714757919312,7.55079889297485 -"Sample_4_TAGTGGTTCCTAGTGA",4.05702829360962,6.43235635757446 -"Sample_4_TAGTTGGAGTAGGCCA",1.99177372455597,-9.35480880737305 -"Sample_4_TATCAGGCAACTGCTA",2.43378567695618,-7.68731164932251 -"Sample_4_TATCAGGGTCGGATCC",2.36107325553894,5.75515699386597 -"Sample_4_TATCTCAAGACTTGAA",2.91889572143555,-8.28905010223389 -"Sample_4_TATCTCAAGGGAGTAA",0.480999857187271,-9.71246814727783 -"Sample_4_TATCTCAAGGTCATCT",-0.017389003187418,4.13004684448242 -"Sample_4_TATCTCAGTCTACCTC",0.0704030841588974,4.35082292556763 -"Sample_4_TATCTCATCATGTAGC",3.93963956832886,7.08222341537476 -"Sample_4_TATGCCCCAAGCTGTT",4.16375589370728,5.55267763137817 -"Sample_4_TATGCCCCAATCACAC",-1.8670300245285,8.80719566345215 -"Sample_4_TATTACCCAAACCCAT",-0.482553839683533,9.69484043121338 -"Sample_4_TATTACCCACGAAAGC",-3.17549705505371,8.19273090362549 -"Sample_4_TATTACCGTGATGTGG",-0.746126651763916,-10.4878740310669 -"Sample_4_TCAACGACAATCTACG",0.665214478969574,-9.13584232330322 -"Sample_4_TCAACGATCCTTTCGG",-0.415875136852264,1.52327859401703 -"Sample_4_TCAATCTGTGCAGACA",-0.770709633827209,0.933943748474121 -"Sample_4_TCAATCTGTGCGATAG",3.40839815139771,7.42054653167725 -"Sample_4_TCACAAGCAATCGAAA",2.91567301750183,-9.90912055969238 -"Sample_4_TCACAAGGTCTCATCC",-0.871197760105133,-9.28185272216797 -"Sample_4_TCACGAACATACTACG",-0.820149719715118,0.495873957872391 -"Sample_4_TCACGAAGTAGAAAGG",0.0441069416701794,1.09257328510284 -"Sample_4_TCACGAATCATCGATG",2.9005286693573,5.38338279724121 -"Sample_4_TCACGAATCGTCACGG",3.10099363327026,5.36832809448242 -"Sample_4_TCAGATGAGTTGAGTA",-0.751651346683502,7.00010061264038 -"Sample_4_TCAGCAAGTGCTTCTC",4.4470009803772,6.65266704559326 -"Sample_4_TCAGCTCAGACAGGCT",0.458611339330673,2.99758625030518 -"Sample_4_TCAGCTCGTCGAGTTT",2.03235292434692,7.38896465301514 -"Sample_4_TCAGGATTCATTTGGG",3.29937410354614,5.87556791305542 -"Sample_4_TCAGGTAAGTCCGGTC",-1.0563530921936,3.57738852500916 -"Sample_4_TCAGGTACAGGATTGG",-1.21626663208008,0.924502432346344 -"Sample_4_TCAGGTAGTCCGTCAG",1.92283749580383,-6.73725891113281 -"Sample_4_TCAGGTATCGAGGTAG",2.98783874511719,-9.52119827270508 -"Sample_4_TCAGGTATCGCCTGTT",0.255892157554626,5.87352418899536 -"Sample_4_TCATTACCAGTATAAG",3.66268086433411,-9.30105972290039 -"Sample_4_TCATTTGAGGCAATTA",3.78855443000793,7.51539611816406 -"Sample_4_TCATTTGAGTGGTAGC",-1.83909428119659,0.619179964065552 -"Sample_4_TCATTTGGTGTTGAGG",-0.250400215387344,0.497301280498505 -"Sample_4_TCCACACAGTACACCT",-1.77428078651428,7.45681715011597 -"Sample_4_TCCACACCAGTTTACG",1.18594563007355,-10.3424205780029 -"Sample_4_TCCCGATAGACAGGCT",-1.27544140815735,8.54353523254395 -"Sample_4_TCCCGATCAAGTTCTG",1.33855617046356,-8.05630970001221 -"Sample_4_TCCCGATTCGTAGATC",-0.443473815917969,6.76876735687256 -"Sample_4_TCGAGGCGTAGAAGGA",-1.79598498344421,5.72872543334961 -"Sample_4_TCGAGGCTCCTCAACC",3.23569369316101,4.53603506088257 -"Sample_4_TCGAGGCTCGTTGACA",2.65242218971252,-9.13494110107422 -"Sample_4_TCGCGAGGTCAGTGGA",0.728972792625427,2.68103456497192 -"Sample_4_TCGGGACCAGACGTAG",2.38268089294434,7.41542291641235 -"Sample_4_TCGGGACGTGATAAGT",0.489630103111267,-8.87857818603516 -"Sample_4_TCGGGACTCACCCGAG",-1.36291360855103,7.99363327026367 -"Sample_4_TCGGGACTCCGAAGAG",0.903656601905823,-9.68252086639404 -"Sample_4_TCGGTAACACAGGCCT",2.85324573516846,5.64202499389648 -"Sample_4_TCGGTAACATTAACCG",2.65202403068542,-7.35500621795654 -"Sample_4_TCGTACCAGATCTGCT",2.60163307189941,5.32825088500977 -"Sample_4_TCGTACCGTTTGACAC",0.968153119087219,-9.37406063079834 -"Sample_4_TCGTACCTCAAACCGT",2.59955477714539,-6.90439701080322 -"Sample_4_TCGTACCTCCCGACTT",4.00438213348389,5.02660799026489 -"Sample_4_TCGTAGACATCTATGG",2.30730247497559,-9.23996448516846 -"Sample_4_TCTATTGAGGCGACAT",2.88637137413025,7.49854755401611 -"Sample_4_TCTCATAAGACTTTCG",-0.209001183509827,0.405786663293839 -"Sample_4_TCTCTAATCCGTAGTA",0.27355620265007,3.34794640541077 -"Sample_4_TCTGAGAAGAGTGAGA",0.350656270980835,-10.630410194397 -"Sample_4_TCTGAGACACCACGTG",-0.399697095155716,0.553774416446686 -"Sample_4_TCTGAGACAGATCTGT",2.12117457389832,4.87308359146118 -"Sample_4_TCTGGAAAGTACGTTC",2.51449203491211,7.88939237594604 -"Sample_4_TCTTCGGAGGGATGGG",3.14019060134888,7.71518182754517 -"Sample_4_TCTTTCCAGACTGGGT",2.96888399124146,-9.07755470275879 -"Sample_4_TCTTTCCGTGCTTCTC",4.1909499168396,5.40419483184814 -"Sample_4_TGAAAGATCCCTAATT",0.470678865909576,-11.4633293151855 -"Sample_4_TGAAAGATCGTTGCCT",-1.74231564998627,0.758616387844086 -"Sample_4_TGACAACTCCTACAGA",3.92455816268921,-9.49243545532227 -"Sample_4_TGACTTTCAATGGATA",0.0692679584026337,2.24117708206177 -"Sample_4_TGACTTTTCTAGCACA",4.48354244232178,6.19219303131104 -"Sample_4_TGAGAGGAGCAATCTC",3.33779454231262,-10.1234188079834 -"Sample_4_TGAGCATGTCAAAGCG",-0.673156976699829,4.23046112060547 -"Sample_4_TGAGCCGGTGCCTTGG",0.362415611743927,-11.2780523300171 -"Sample_4_TGAGGGAAGCATGGCA",-2.13364672660828,6.72815418243408 -"Sample_4_TGAGGGACATGGGAAC",-0.504172325134277,4.0880560874939 -"Sample_4_TGAGGGATCACATGCA",3.77803564071655,5.18264055252075 -"Sample_4_TGATTTCTCCTTCAAT",1.60831272602081,-8.37379264831543 -"Sample_4_TGCACCTAGGGCACTA",2.06760120391846,-9.67107200622559 -"Sample_4_TGCCCATCAGTAAGCG",2.64246010780334,4.86371421813965 -"Sample_4_TGCCCTAGTTCAACCA",0.515762507915497,-8.25684452056885 -"Sample_4_TGCCCTATCAGGCAAG",-0.16435644030571,8.28202247619629 -"Sample_4_TGCGGGTAGACCTAGG",3.29564356803894,5.8372688293457 -"Sample_4_TGCGGGTGTTATGTGC",1.85008931159973,-4.5346508026123 -"Sample_4_TGCGTGGGTAATTGGA",2.53721952438354,-7.9424901008606 -"Sample_4_TGCTACCAGCTCCCAG",-0.325230658054352,9.47831344604492 -"Sample_4_TGCTGCTGTCCAACTA",-0.131751537322998,5.76944589614868 -"Sample_4_TGGACGCAGGAGCGAG",-1.31168878078461,5.5460319519043 -"Sample_4_TGGACGCGTAACGCGA",3.54717874526978,7.46514654159546 -"Sample_4_TGGCGCAAGTACGCGA",3.24612593650818,6.42028093338013 -"Sample_4_TGGCGCAGTTCCCGAG",0.465166866779327,-8.14164638519287 -"Sample_4_TGGCGCATCCTACAGA",3.66203498840332,3.68850636482239 -"Sample_4_TGGGAAGAGGAACTGC",-2.82387828826904,8.82495975494385 -"Sample_4_TGGGAAGTCTATCCTA",-1.11498761177063,-8.13706016540527 -"Sample_4_TGGGCGTCAGTTAACC",2.32784938812256,8.0306453704834 -"Sample_4_TGGTTAGAGCGCCTTG",2.07264256477356,-9.16225624084473 -"Sample_4_TGGTTAGAGGAATTAC",0.203374922275543,3.18812251091003 -"Sample_4_TGGTTAGGTCAATACC",1.95705592632294,-7.06457042694092 -"Sample_4_TGGTTCCTCCACTCCA",0.095970444381237,5.62136697769165 -"Sample_4_TGTATTCAGATGTCGG",0.309991002082825,4.9100193977356 -"Sample_4_TGTATTCCAGCGTCCA",2.39181566238403,-8.9264554977417 -"Sample_4_TGTATTCTCTGATACG",2.76540899276733,-9.35356140136719 -"Sample_4_TGTCCCAAGAGACTAT",4.32136631011963,-9.61208057403564 -"Sample_4_TGTCCCATCTTGTACT",2.46080851554871,-10.1187019348145 -"Sample_4_TGTGGTACATGCAACT",-1.83528137207031,9.07018280029297 -"Sample_4_TGTGGTAGTGCATCTA",-1.335524559021,0.265484571456909 -"Sample_4_TGTGGTATCCCGACTT",4.01675748825073,4.8514666557312 -"Sample_4_TGTGTTTAGGCCCTCA",4.44736909866333,-8.74761486053467 -"Sample_4_TGTGTTTGTTGTGGAG",1.02691054344177,2.78939437866211 -"Sample_4_TGTGTTTTCATGCTCC",3.97195076942444,6.94306755065918 -"Sample_4_TGTTCCGAGCCACCTG",1.91381812095642,-7.61367893218994 -"Sample_4_TGTTCCGCAGTGACAG",2.26311993598938,-4.78206968307495 -"Sample_4_TTAGGACAGGGTCGAT",-1.65904605388641,1.26755845546722 -"Sample_4_TTAGGACGTGGTACAG",2.66826295852661,6.35769081115723 -"Sample_4_TTAGGCACATAAAGGT",3.94447326660156,7.88790941238403 -"Sample_4_TTAGTTCTCACTTACT",2.73116230964661,4.99942207336426 -"Sample_4_TTAGTTCTCTTGAGAC",3.97892212867737,5.81872844696045 -"Sample_4_TTATGCTTCACGAAGG",-0.802635133266449,9.03095722198486 -"Sample_4_TTCGGTCAGCCAGAAC",2.7600417137146,-10.9972047805786 -"Sample_4_TTCGGTCAGTTCCACA",-9.81457042694092,-2.91104578971863 -"Sample_4_TTCTACACATTACCTT",-0.844274461269379,0.622758090496063 -"Sample_4_TTCTACATCGCAAGCC",3.33834671974182,4.66835880279541 -"Sample_4_TTCTCAACACACTGCG",-0.508517324924469,4.94144010543823 -"Sample_4_TTCTCCTGTTCCCGAG",2.48332285881042,-10.8556251525879 -"Sample_4_TTCTCCTTCCGAATGT",2.6969051361084,5.8803653717041 -"Sample_4_TTCTTAGGTTTAGGAA",1.65020656585693,2.23946690559387 -"Sample_4_TTGAACGAGCCATCGC",1.95342314243317,-8.48818969726562 -"Sample_4_TTGAACGGTAGCTCCG",1.78384149074554,3.5133900642395 -"Sample_4_TTGAACGGTCTCAACA",0.147097870707512,0.238351210951805 -"Sample_4_TTGAACGTCAGTTCGA",-0.660673975944519,8.70661067962646 -"Sample_4_TTGCCGTCACCTTGTC",4.3368935585022,-8.76253604888916 -"Sample_4_TTGCCGTTCTTGCAAG",2.39001798629761,5.41715240478516 -"Sample_4_TTGCGTCCAGCCTTGG",1.30715334415436,3.275723695755 -"Sample_4_TTGCGTCTCACGGTTA",-0.67403382062912,7.46691465377808 -"Sample_4_TTGCGTCTCCTATTCA",4.65778398513794,6.47557497024536 -"Sample_4_TTGCGTCTCTTCTGGC",-1.45244705677032,5.65656280517578 -"Sample_4_TTGGCAAAGAGATGAG",3.81781363487244,-8.77507019042969 -"Sample_4_TTGGCAAGTATCACCA",4.60452604293823,5.25131034851074 -"Sample_4_TTGTAGGAGTCGTACT",3.18819141387939,6.20803546905518 -"Sample_4_TTTATGCAGCCACTAT",1.9394063949585,5.74754047393799 -"Sample_4_TTTATGCCAGTTAACC",4.10701560974121,7.55610036849976 -"Sample_4_TTTGCGCAGGCTACGA",4.01955270767212,-10.1364908218384 -"Sample_4_TTTGCGCTCACTATTC",3.95524954795837,5.41893863677979 -"Sample_4_TTTGGTTTCTGTTTGT",-0.497337251901627,0.13110388815403 -"Sample_4_TTTGTCACAATTGCTG",2.50592064857483,2.97795271873474 -"Sample_4_TTTGTCAGTAGGAGTC",-9.51404285430908,-2.60254168510437 -"Sample_4_TTTGTCATCCTCATTA",0.464586406946182,-11.3375959396362 diff --git a/scTCRpy/__init__.py b/scTCRpy/__init__.py deleted file mode 100644 index fcb71c6fa..000000000 --- a/scTCRpy/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - -import distanceMetrics -import clusterTools -import layoutExp -import parseGex -import parseTCR -import plotGraphs -import reportHTML -import resultData -import talkR -import utils - - -print 'Single cell TCR analysis script for 10x data imported' \ No newline at end of file diff --git a/scTCRpy/__init__.pyc b/scTCRpy/__init__.pyc deleted file mode 100644 index e24d78d51a66b6b916579f074c33cf102a268af4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 487 zcmZXPy-ve05XYUQr5`F{;Q>5$B7nq(5E7tvpbTv$3n{V^LoJ-x@?E56At4}pX5r~5i^X5KaQA)LOf<74xBKN1RgLk2s~tB z82E&VlfWY;MuEpni~~=Yn6NTtizQXw%oFNDf0Mny4dF)D??&-L8N;)OHJ7zCEok6S zc0) -traTab <- as.matrix(traTab) -#traTab <- head(traTab, n=100) -traTab <- t(traTab) -traTab <- as.data.frame(traTab) -traDiv <- aindex(traTab, index='Shannon') -names(traDiv) <- c('Shannon_nucA') -traDiv <- as.data.frame(traDiv) -divTab <- merge(divTab, traDiv, all=TRUE, by='row.names') -row.names(divTab) <- divTab$Row.names -divTab$Row.names <- NULL - -trbTab <- subset(clonotypes, select=G_nTRB:C_nTRB) -trbTab <- subset(trbTab, rowSums(trbTab)>0) -trbTab <- as.matrix(trbTab) -#trbTab <- head(trbTab, n=100) -trbTab <- t(trbTab) -trbTab <- as.data.frame(trbTab) -trbDiv <- aindex(trbTab, index='Shannon') -names(trbDiv) <- c('Shannon_nucB') -trbDiv <- as.data.frame(trbDiv) -divTab <- merge(divTab, trbDiv, all=TRUE, by='row.names') -row.names(divTab) <- divTab$Row.names -divTab$Row.names <- NULL - -trcTab <- subset(clonotypes, select=G_nTRC:C_nTRC) -trcTab <- subset(trcTab, rowSums(trcTab)>0) -trcTab <- as.matrix(trcTab) -#trcTab <- head(trcTab, n=100) -trcTab <- t(trcTab) -trcTab <- as.data.frame(trcTab) -trcDiv <- aindex(trcTab, index='Shannon') -names(trcDiv) <- c('Shannon_nucC') -trcDiv <- as.data.frame(trcDiv) -divTab <- merge(divTab, trcDiv, all=TRUE, by='row.names') -row.names(divTab) <- divTab$Row.names -divTab$Row.names <- NULL - -#Assess Shannon diversity scores for each amino acid sequence of the TRA and TRB chains - -traTab <- subset(clonotypes, select=G_aTRA:T_aTRA) -traTab <- subset(traTab, rowSums(traTab)>0) -traTab <- as.matrix(traTab) -#traTab <- head(traTab, n=100) -traTab <- t(traTab) -traTab <- as.data.frame(traTab) -traDiv <- aindex(traTab, index='Shannon') -names(traDiv) <- c('Shannon_aaA') -traDiv <- as.data.frame(traDiv) -divTab <- merge(divTab, traDiv, all=TRUE, by='row.names') -row.names(divTab) <- divTab$Row.names -divTab$Row.names <- NULL - -trbTab <- subset(clonotypes, select=G_aTRB:T_aTRB) -trbTab <- subset(trbTab, rowSums(trbTab)>0) -trbTab <- as.matrix(trbTab) -#trbTab <- head(trbTab, n=100) -trbTab <- t(trbTab) -trbTab <- as.data.frame(trbTab) -trbDiv <- aindex(trbTab, index='Shannon') -names(trbDiv) <- c('Shannon_aaB') -trbDiv <- as.data.frame(trbDiv) -divTab <- merge(divTab, trbDiv, all=TRUE, by='row.names') -row.names(divTab) <- divTab$Row.names -divTab$Row.names <- NULL - -trcTab <- subset(clonotypes, select=G_aTRC:T_aTRC) -trcTab <- subset(trcTab, rowSums(trcTab)>0) -trcTab <- as.matrix(trcTab) -#trcTab <- head(trcTab, n=100) -trcTab <- t(trcTab) -trcTab <- as.data.frame(trcTab) -trcDiv <- aindex(trcTab, index='Shannon') -names(trcDiv) <- c('Shannon_aaC') -trcDiv <- as.data.frame(trcDiv) -divTab <- merge(divTab, trcDiv, all=TRUE, by='row.names') -row.names(divTab) <- divTab$Row.names -divTab$Row.names <- NULL - -write.table(divTab, file=outFile, col.names=FALSE, quote=FALSE, sep="\t") \ No newline at end of file diff --git a/scTCRpy/cloneDiversityCalc.r b/scTCRpy/cloneDiversityCalc.r deleted file mode 100644 index 817ae129a..000000000 --- a/scTCRpy/cloneDiversityCalc.r +++ /dev/null @@ -1,15 +0,0 @@ -#! /usr/local/bioinf/bin/Rscript - -library(vegan, lib="/home/singlecell/scripts/Tamas/scTCRpy/rpackages") -library(DiversitySeq, lib="/home/singlecell/scripts/Tamas/scTCRpy/rpackages") - -args = commandArgs(trailingOnly=TRUE) -clonotypeFile <- args[1] -outFile <- args[2] - -clonotypes <- read.delim(clonotypeFile, header=TRUE, sep='\t', row.names='CLONOTYPE') -clonotypes <- clonotypes[, colSums(clonotypes != 0) > 0] - -alphadiv <- aindex(clonotypes, index='Shannon') - -write.table(alphadiv, file=outFile, col.names=FALSE, quote=FALSE, sep="\t") \ No newline at end of file diff --git a/scTCRpy/clusterTools.py b/scTCRpy/clusterTools.py deleted file mode 100644 index 710a62812..000000000 --- a/scTCRpy/clusterTools.py +++ /dev/null @@ -1,163 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - -from sklearn.cluster import SpectralClustering -from sklearn.manifold import TSNE - -from utils import stampTime, tabWriter, tabReader, readORrun, objectContainer - -def __main__(): - print 'This module is not intended for direct command line usage. Currently supports import to Python only.' - moduleTest() - return - -def moduleTest(): - pr = projectionData('hi') - bg, coord = pr.readManifold('testdata/projection.csv') - pr.createAnnotation('testdata/test_annotation.tsv', coordict=coord) - return - -class manyMani(objectContainer): - def add(self, name, **kwargs): - self.order.append(name) - a = locals() - a = a['kwargs'] - self.data[name] = projectionData(name, **a) - return - -class projectionData: - def __init__(self, name, coordinates={}, annotations={}, barcodecollision={}, gex=None, matrix=None, labels=None, projectRoot='', proFile=None, coordFile=None, annotFile=None, conflictresolve='drop', keepextra=True, numCellType=12, numCores=8, force_recompute=False, verbose=False): - self.name = name - self.label = name - self.verbose=verbose - if gex != None: - matrix = gex.matrix - if labels == None: - labels = gex.cells - if proFile != None: - self.background, self.coordinates, self.annotations = readORrun(proFile, force_recompute, self.readAnnMan, self.createAnnMan, [[[], []], {}, {}], parentDir=projectRoot, kwargs={'matrix': matrix, 'labels':labels, 'numCellType': numCellType, 'numCores': numCores}) - else: - self.background, self.coordinates = readORrun(coordFile, force_recompute, self.readManifold, self.createManifold, [[[], []], {}], parentDir=projectRoot, kwargs={'matrix': matrix, 'labels':labels}) - self.annotations = readORrun(annotFile, False, self.readAnnotation, self.createAnnotation, {}, parentDir=projectRoot, kwargs={'coordict': self.coordinates, 'numCellType': numCellType, 'numCores': numCores}) - else: - if proFile != None: - self.background, self.coordinates, self.annotations = self.readAnnMan(projectRoot+proFile) - else: - self.background, self.coordinates = self.readManifold(projectRoot+coordFile) - self.annotations = readORrun(annotFile, False, self.readAnnotation, self.createAnnotation, {}, parentDir=projectRoot, kwargs={'coordict': self.coordinates, 'numCellType': numCellType, 'numCores': numCores}) - if len(barcodecollision) > 0: - self.coordinates, self.annotations = self.recodeBarcodes(barcodecollision, conflictresolve) - cellTypes = {} - for k, v in self.annotations.iteritems(): - if v not in cellTypes: - cellTypes[v] = [] - cellTypes[v].append(k) - self.cellTypes = cellTypes - return - - def recodeBarcodes(self, bc, conflictresolve): - cb = {} - for k, v in bc.iteritems(): - s = set(v) - if len(s) == 1: - s = list(s) - cb[s[0]] = k - else: - if conflictresolve == 'keepall': - for a in s: - cb[a] = k - coordinates, annotations = {}, {} - for k, v in self.coordinates.iteritems(): - if k in cb: - coordinates[cb[k]] = v - else: - coordinates[k] = v - for k, v in self.annotations.iteritems(): - if k in cb: - annotations[cb[k]] = v - else: - annotations[k] = v - if self.verbose: - stampTime('Barcodes recoded to match experimental layout.') - return coordinates, annotations - - def createManifold(self, fn, matrix=None, labels=[], save_output=True): - bg, coord = [[], []], {} - if matrix == None: - return bg, coord - else: - X = matrix.transpose() - X = X.toarray() - X_embedded = [[0, 0], [0, 0]] - X_embedded = TSNE(n_components=2).fit_transform(X) - coord = dict(zip(labels,X_embedded)) - if save_output: - tabWriter(X_embedded, fn, rownames=labels, tabformatter='list_of_list') - X_embedded = zip(*X_embedded) - if self.verbose: - stampTime('The manifold ' + self.name + ' was calculated.') - return X_embedded, coord - - def readManifold(self, fn, matrix=None, labels=None, save_output=True): - coord = tabReader(fn, tabformat='dict_of_list', floating=[1, 2], add_nan=False) - ncoord = {} - for k, v in coord.iteritems(): - if k[-2] != '-': - ncoord[k+'-1'] = v - else: - ncoord[k] = v - coord = ncoord - bg = [[], []] - if labels == None: - labels = list(coord.keys()) - for i in range(0, len(labels)): - bg[0].append(coord[labels[i]][0]) - bg[1].append(coord[labels[i]][1]) - if self.verbose: - stampTime('The manifold ' + self.name + ' was read from file.') - return bg, coord - - def createAnnotation(self, fn, coordict={}, numCellType=12, numCores=8, save_output=True): - Y, X = zip(*coordict.items()) - labels = SpectralClustering(n_clusters=numCellType, n_jobs=numCores, assign_labels="discretize", random_state=0).fit_predict(X) - annotations = {} - for i in range(len(labels)): - annotations[Y[i]] = 'Cell type ' + str(labels[i]) - if save_output: - tabWriter(annotations, fn) - if self.verbose: - stampTime('The manifold ' + self.name + ' was read from file.') - return annotations - - def readAnnotation(self, fn, coordict={}, numCellType=12, numCores=8, save_output=True): - annotations = tabReader(fn, tabformat='dict_of_list', evenflatter=True) - if self.verbose: - stampTime('Annotation of the manifold ' + self.name + ' was read from file.') - return annotations - - def createAnnMan(self, fn, matrix=None, labels=[], numCellType=12, numCores=8): - bg, coord = self.createManifold(fn, matrix=matrix, labels=labels, save_output=False) - annotations = self.createAnnotation(fn, coordict=coord, numCellType=numCellType, numCores=numCores, save_output=False) - odict = {} - for k, v in annotations.iteritems(): - odict[k] = [v]+coord[k] - tabWriter(odict, fn) - if self.verbose: - stampTime('Annotation and coordinates for the manifold ' + self.name + ' were created and written into a single file.') - return bg, coord, annotations - - def readAnnMan(self, fn, matrix=None, labels=[], numCellType=12, numCores=8): - bg, coord, annotations = [[], []], {}, {} - data = tabReader(fn, tabformat='dict_of_list', floating=[1, 2], add_nan=False) - for cell, dat in data.iteritems(): - x, y, l = dat - annotations[cell] = l - coord[cell] = [x, y] - bg[0].append(x) - bg[1].append(y) - if self.verbose: - stampTime('Annotation and coordinates for the manifold ' + self.name + ' were read from file.') - return bg, coord, annotations - -if __name__ == '__main__': - __main__() \ No newline at end of file diff --git a/scTCRpy/clusterTools.pyc b/scTCRpy/clusterTools.pyc deleted file mode 100644 index 8207afab8d7d4182816fef34ec0798d37e4c90fd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7523 zcmcIpOK&7s6+Ttn?uXr;@p$57oQJ4MNa%#bCPbPLWI~9YhZNz2vWXLCtX8?J+EaFQ zRl9E4V@GaSc*G(KO9U)fM&bvsKmsAbh8-(d^9Ld}?1Aq)w;#6UETWik)$RML`#9%2 z-#PbA>EF|n_4`v>Z5jM4;rBJX<~K+}JdPwHT}L(@aaeXGaiv?5&5|vbBq>XGLN+I) zTanF*bgQyim2OQoYtpUDW*z;?@<3#BQo;$%Oi5Ugs4g8x!YXS{MLyhYNLZ5)X?;{R zEzvazCuPr-{MQmqvZIq?6;ra-oW=^{|KeZML9UoZvet{*MII!}$$=@NJWh9PTN!QZ zYpZWGC)l%uidw;p#REfvA`MjqpfVabnza8SyTeb>(q(&9S4%eHqh#d4MwL7YbU zNN!xhGRK`k0-?S2cA?v%%+{JNDpw7%vVJc%UN;L5lE_0Y%?dA0izp4F(CcKm7sffp z_u5&v8>FF^U=r`Z1Uu1!w|tQ2QCcL&o;m3Cvb-=}+~u!VWZrwn#l0-`vNSnfuzQ}w zY<8mc$P`VKWSp2gC@$X1y3wM6l#-|&CCQ>`=W(wvi|au*Fet4r`@Q4EwiV`jmL+DP zcU;u)X{*)6{#z{-Wrbrq6He7pag6qpV=iqJb)E`-edqcd{fbsLM8Lis%QU;q2F3Q!m%0ZSNFZr;dIl z0`{vIj>|KYv^WiC&bjI67J!Lku1@8wjYwX{Yi=NkU}KQ2sExVeuPG4PTr2g#ZNG!p zd=ClrpsF}g%{bV~sYt&hC(wc;^T-t_I4u7hDf&V;jx1sL0iFx$eeZsd?-*-W6az#I zyJ1Vv>jBo9O|o{77=H@21y@29Hkr|qiIR?H(x4mpG~>_&Mg!RM?2@uH^2DF%a} zvL+A8r0jE*;A2#Ry>&F(8KlfgF!|ZaDXnY}dSqu9{!rt2){ALhG3DK+lBp5aEaNpl zfk+58h<}tp@rKrbAh0K3tt9#T4)TCvzal5t4JzRp-e!m03F%h_&f%m!?EEJI{7^iZ zQ2wwE;2$*=|Vo3#D@4@)bc+cLcXFt=Bh{I<*0&9d^OA*U|A z12-`gF%%0>_GjhjEnVM~?A|10t!&*EnU9{sX zgfjatyBn;;40D9bTC)xuuB!j<1@yBkac;?oWY!IeJU+5y7Hmg}DM)7NL3bH`bN#p% zDLu2C<&p8pBUmli<6$KJEhOqB5`J4$Q9O+D?aV~Vd#hO*`QES(J+bl0rAkM1Nbo*j zef3Wxcf<-#vmLbecJk~1RLdni6s*bR_ELIM=5(-mU3l&Y5yhz zGw>qm_NE*?#+H?>8B*?`XH}N=yRw0vcs8&OJZ`@x{kj~jsL7T`D|%_XBK!4TMe@IK zX(Sy?Tjrdf4J*nR`ZL)W3VLq1)@(iYX#Q~H+@ni!s zgtB7C1rt!9t1-2%HmryvY62MM0&0iY_pB*|jK4t;?R#Ey)Pp5-VJSi4CBboaP%Jd* z@%(QiQEN{De*IZJ+U*|0*lEM&r-3JhH1R zfvEo?#^jvlIV1w$%>#I|cy9oZ1mLtn@rHYC^jk+=o3g9pCC4c!|A`E}?a<-6Kq27; zG+F2Utg5FX6g_HjIyeP}0jsL)+MpC_Qjl5?iaauSfGtSWh9jrqFg~p+v{4VFV-6so zMpt6-4*lo96=;*H#()ZNICnVqQ`aR1_)Sed#q{U_l++~u2k8tRmPI&2I4e5NAz?#pEj2y*6w1EHf;Lzq2(zEpMw|>j8f}(l*pid=$?=Q6Y>mH0x+RN zVDsn=Lehe7W$R_-s;XiFNUX{{BNl|Bp#lkzK`r8`rTv%36)bFkA~EI&{2)Rga)A2J zNC2D#Yz#I7A(t4t9&HeW2(Ag!66*oqm95vLk2nZGoRoQ>k^~2AqcG1J0+Nj&%?pjF zVdMTEEbG-J_Z;X3*C)jD6!6>_;~C4FP%G5f-G)MAoD!r&%n+oC=}wa2jGgXSvQ(%O zd^m5lQegK#xSUZyyr4&(7hY5wxdh$}8x-CQKDJ#wwiexT{E2n`glK+Sdkvt^IPetJgq*du30B>Rpo*nQ; zMnO)3lkzSQGvr6&9B%mzMF`=MCG{F$W$Q^I4IsBvNQj~35xP?V9i^rHkAS5K$$zO3 zMZH2)3DGj<@0Bqvfs!%pQA0b_;aut2R+S$>llErGU^24iZnkZds{&)lmXP**YkrxWKOcNZi+jx2Mo3BOC+x6jjIvv zA|`u)cW^g%Yb;P2wxW=&e?~${4Sf#79T+7n68CEAZSEp~QLDA`9RwtBB``-=E#!{K zWYD>ccw}I*i!@f4@+nWgx7GG1r4K%zk{#GSyatR^?R65-ZXg?UGb1bIT(I5Wsgtom zP4#?GqwWk-bV|b`@kOdUFJD9Oo>43>tAL`%xZw_81y^{N_Fmkhlit zWq9TVp1qVFI03qGpPyMLKzv(IP-5E4QJ5cv8YxyHo{S`V$Y#OSf&;}3aqaN zE6-x!Pw*OEcZu^2{4tK6N1Qw|L#~DUQ#1TKGNW(-3dd8T34+@h><+>7g#ipGJ)*XU zq07cOeB3Qbzajl;>Eluf4=5TK!DY({E~)@d#m*8>e28)AEpQFP$O09NEKo7675nel zXaeE%UrCw>hls1nd9FVPBx2?aJNQ3Fc^otN@3V+kzC4}d8ean#7hp1pmCwCtbU~Fv8Kt@fQX~~0vxn)?bD9D1|OmFT&p;=J-i z{n6PQvp44&bCtO%zlN4oI~#?sw=ZTdiGm#en`1))ANBTzXxK133)vG5ZT6suaZ#q0 zuSB4_R(C;Xq_gOMrRcr8E=fgnb=%Ysc**{6!K<8Iw>#s^xJ|b{QLj8)yZN(*^FN!B B$D05E diff --git a/scTCRpy/distanceMetrics.py b/scTCRpy/distanceMetrics.py deleted file mode 100644 index 048f8de44..000000000 --- a/scTCRpy/distanceMetrics.py +++ /dev/null @@ -1,243 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - -from utils import stampTime, tabWriter, tabReader, readORrun, objectContainer -from Bio import pairwise2 -from Bio.SubsMat import MatrixInfo as matlist -import numpy as np -import scipy.cluster.hierarchy as hcluster -import scipy.spatial as ssd - - -def __main__(): - print 'This module is not intended for direct command line usage. Currently suports import to Python only.' - return - -class distList(objectContainer): - def add(self, *args, **kwargs): - a = locals() - name = a['args'][0] - self.order.append(name) - self.data[name] = distMat(*a['args'], **a['kwargs']) - return - -class distMat: - def __init__(self, name, cells, clonotypes, metric='TCR', projectRoot='', matrixFile=None, maxGroupNum=10, penalties=[-10, -2], include_secondary_chains=True, force_recompute=False, verbose=False): - self.not_kidera = True - self.clabels = None - self.label = name - self.verbose = verbose - self.include_secondary_chains=include_secondary_chains - if metric == 'Kidera': - self.not_kidera = False - self.clabels = ['K'+str(x) for x in range(1, 11)] - matrixFile = matrixFile+'_matrix_kidera.tsv' - self.forheat, self.distances, self.labels = self.makeDisKid(cells) - else: - matrixFile = matrixFile+'_matrix_tcr.tsv' - self.distances, self.labels = readORrun(matrixFile, force_recompute, self.readDisTCR, self.runDisTCR, [[], []], parentDir=projectRoot, kwargs={'cells': cells, 'clonotypes': clonotypes, 'penalties': penalties, 'secondary': self.include_secondary_chains}) - self.forheat = self.distances - self.labels = tuple(self.labels) - self.disDic = self.makeDisDic() - self.dendro, self.linkage = self.dendroFromDist(self.distances, self.labels) - self.treeGroups = readORrun(matrixFile[:-4]+'_annotation.tsv', force_recompute, self.readTreeGroups, self.findTreeGroups, {}, kwargs={'linkage': self.linkage, 'labels': self.labels, 'maxGroupNum': maxGroupNum}) - return - - def runDisTCR(self, fn, cells, clonotypes, penalties, secondary): - clones = {} - matrix = matlist.blosum62 - gap_open, gap_extend = penalties - for clone, info in clonotypes.iteritems(): - cdrs = info['cdrSym'] - if cdrs != '|||': - tcrs = [] - cdrs = cdrs.split('|') - for i in range(0, len(cdrs)): - aa = cdrs[i] - if aa == '': - aa = 'XXXXXXXXXXXX' - cdrs[i] = aa - tcrs.append(cdrs[:2]) - if secondary: - if cdrs[2] + cdrs[3] != 'XXXXXXXXXXXXXXXXXXXXXXXX': - tcrs.append(cdrs[2:4]) - clones[clone] = tcrs - similarities = {} - selfsimilarities = {} - ldict = {} - cl = list(clones.keys()) - for c1 in cl: - for c2 in cl: - clonescore = [] - for a1, b1 in clones[c1]: - for a2, b2 in clones[c2]: - similarity_score = 0 - similarity_score += pairwise2.align.globalds(a1, a2, matrix, gap_open, gap_extend, one_alignment_only=True, score_only=True) - similarity_score += pairwise2.align.globalds(b1, b2, matrix, gap_open, gap_extend, one_alignment_only=True, score_only=True) - clonescore.append(similarity_score) - clonescore = np.max(clonescore) - for cell1 in clonotypes[c1]['cells']: - ldict[cell1] = c1 - if cell1 not in selfsimilarities: - selfsimilarities[cell1] = -10000 - if clonescore > selfsimilarities[cell1]: - selfsimilarities[cell1] = clonescore - if cell1 not in similarities: - similarities[cell1] = {} - for cell2 in clonotypes[c2]['cells']: - if cell2 not in selfsimilarities: - selfsimilarities[cell2] = -10000 - if clonescore > selfsimilarities[cell2]: - selfsimilarities[cell2] = clonescore - if cell2 not in similarities: - similarities[cell2] = {} - if cell1 not in similarities[cell2]: - similarities[cell2][cell1] = [] - if cell2 not in similarities[cell1]: - similarities[cell1][cell2] = [] - similarities[cell1][cell2].append(clonescore ) - similarities[cell2][cell1].append(clonescore ) - distances = [] - labels = list(similarities.keys()) - #labels.sort(key=lambda x: ldict[x]) #Uncomment if need to order clonotypes. Not very useful since renaming from cell ranger clonotype names. - for l1 in labels: - distance_row = [] - for l2 in labels: - if ldict[l1] == ldict[l2]: - distance_row.append(0.0) - else: - distance_row.append(1-(np.mean(similarities[l1][l2])/selfsimilarities[l1])) - distances.append(distance_row) - #This part contains a bit of cosmetics to make a nice, simmetric matrix. Most of if might become pointless later - distances1 = np.array(distances) - distances2 = np.rot90(np.flipud(distances1), 3) - distances = np.mean(np.array([distances1, distances2]), axis=0) - tabWriter(distances, fn, lineformatter=lambda y: ['{:.5f}'.format(x) for x in y], tabformatter='list_of_list', colnames=labels, rowname='') - if self.verbose: - stampTime('Computed TCR distances and saved to ' + fn) - return distances, labels - - def readDisTCR(self, fn, cells, clonotypes, penalties, secondary): - def lineParse(data, line, lineNum, firstline, includend, test_only=False): - if test_only: - return locals() - if firstline: - firstline = False - data[1] = line[1:] - else: - data[0].append([float(x) for x in line]) - return data, firstline, includend - distances, labels = tabReader(fn, tabformatter=lambda x: [[], []], lineformatter=lineParse) - if self.verbose: - stampTime('Read TCR distances for ' + str(len(labels)) + ' cells from ' + fn) - return np.array(distances), labels - - def makeDisKid(self, cells): - features, labels = [], [] - for k, v in cells.iteritems(): - d, validator = [], True - eli = v['kideras']['C'] - if 'NaN' in eli: - validator = False - else: - for e in eli: - try: - d.append(float(e)) - except: - validator = False - if validator: - features.append(d) - labels.append(k) - distances = ssd.distance.pdist(features, 'minkowski') - #distances = ssd.distance_matrix(features, features) - distances = ssd.distance.squareform(distances) - if self.verbose: - stampTime('A Kidera-based distance matrix is computed.') - return np.array(features), distances, labels - - def makeDisDic(self): - d = {} - distances, labels = self.distances, self.labels - N = len(labels) - for i in range(0, N): - for j in range(0, N): - c = [labels[i], labels[j]] - c.sort() - c = '|'.join(c) - d[c] = distances[i][j] - if self.verbose: - stampTime('Quick access distance dictionary created.') - return d - - def dendroFromDist(self, distances, labels, ax=None): - #distances = ssd.distance.squareform(distances) #This would switch between condensed and redundant matrix formats. Currently produces error beacuse it finds negative distances. - linkage = hcluster.linkage(distances) - dendro = hcluster.dendrogram(linkage, labels=labels, ax=ax, distance_sort=True, link_color_func=lambda k: 'black') - if self.verbose: - stampTime('Dendrogram created based on distance matrix.') - return dendro, linkage - - def readTreeGroups(self, fn, linkage, labels, maxGroupNum): - members = tabReader(fn, tabformat='dict_of_list') - if self.verbose: - stampTime('Groups of distance matrix read from file' + fn) - return members - - def findTreeGroups(self, fn, linkage, labels, maxGroupNum): - groups = hcluster.fcluster(self.linkage, maxGroupNum, criterion='maxclust') - members = {} - for i in range(0, len(groups)): - member = 'Group ' + str(groups[i]) - if member not in members: - members[member] = [] - members[member].append(labels[i]) - tabWriter(members, fn, lineformatter=lambda x: x, tabformatter='dict_of_list') - if self.verbose: - stampTime('Groups isolated from distance matrix.') - return members - - def meanDistances(self, groups, order): - disDic = self.disDic - labels = self.labels - distances = [] - for name1 in order: - group1 = groups[name1] - distancerow = [] - for name2 in order: - group2 = groups[name2] - distance = [] - for c1 in group1: - if c1 in labels: - for c2 in group2: - if c2 in labels: - kl = [c1, c2] - kl.sort() - c = '|'.join(kl) - if c in disDic: - distance.append(disDic[c]) - if len(distance) > 0: - distancerow.append(np.mean(distance)) - else: - distancerow.append(1.0) - distances.append(distancerow) - return distances - - def groupTree(self, groups, order=None, ax=None): - if order == None: - order = list(groups.keys()) - distances = self.meanDistances(groups, order) - tree = self.dendroFromDist(distances, order, ax=ax) - return tree - - def biClustMap(self, ax1=None, ax2=None): - l_rows = hcluster.linkage(self.forheat) - l_cols = hcluster.linkage(self.forheat.T) - v_rows, v_cols = hcluster.leaves_list(l_rows), 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z2$(X;_smuGDnMh2thuk7nM<_CYp;W>5nM6#;K@uBB?p_kKC;Qq) zJtgFS3nz)LlxWHO%9F;NDe{>05~=FWqG+~i51?0K-_}v zJ|Z_Eu9cBiu=SM;d;nE+vY(2~y*K6f02=9Jzo@3h_u3-kR9oZ&>MZhdi&bTys!jDa zen@eR(Bd?;7Oilr4YkIyR#*bcJEBu~flI}ePPJ6gDFUD;7X3NIoGI@BsabVE?>n3B z*DB_4LBr|UG1 z1y&#xP2r^hTm@WtP-{n5k*A;!isG(>}`Lf@{cqbo`1U)+=3&viRrd z);xw}jODI3>&-UqMDyd`!*?T>=oREX@!^pRn^qxe^$tWe9ohtz#BwOI5tgyT#NDJr zJm}bA@-1og!B~DrWOreC)cf!5>3uw_Ka0$b=Vs*~T;Uw3W@swV7gR$*P>^46?nL$D zf@+6!*w(vHJ%erQ3GLRFf;b15(|il}Ot2LWTtc*@I>)L!_Ir^_vorn`!!Ndc%kYxF z#-YbNWsfzx3p|BXN$d`{GKdJ+e;{{B#O)ZexGp#UZ$Jk8IpEiL%#%oJ5Z2(E9uaC? zGhacm=X607a_ru*kAN}W*vAW=W09s#0zhZ1$l}2dm~i;9T1oD{Z3Azq7-Gm7w7ln^ z?le2ilXLTDJ7+ozL1#1)PcHZr60u=}s5F`;5f#c^!?ux=>Z|xkie_Dmr08x%Uh72x z(@Zs#-)L)%enBJDiL3icnwOzX(M}Fswt$n6U!vq^CmHhrtzOo$;zIbZb7JCmbQ>qV SlVSl+Hae4?srg@Zz5f6M 0: - #add an upper wing - blist = [] - for g in todo: - blist.append([self.gex.genInd[g], g]) - blist.sort(key=lambda x: x[0]) - indices, genes = zip(*blist) - putUp = self.gex.matrix[indices,:] - putDown = sparse.csc_matrix((len(indices),newMatrix.Ncells)) - upperMat = sparse.vstack([upperMat, putUp]) - lowerMat = sparse.vstack([lowerMat, putDown]) - newgenes += genes - newalts += list(self.gex.alts[_] for _ in indices) - todo = g2-g1 - if len(todo) > 0: - #add a lower wing - blist = [] - for g in todo: - blist.append([newMatrix.genInd[g]-20, g]) - blist.sort(key=lambda x: x[0]) - indices, genes = zip(*blist) - putUp = sparse.csc_matrix((len(indices),self.gex.Ncells)) - putDown = newMatrix.matrix[indices,:] - upperMat = sparse.vstack([upperMat, putUp]) - lowerMat = sparse.vstack([lowerMat, putDown]) - newgenes += genes - newalts += list(newMatrix.alts[_] for _ in indices) - self.gex.matrix = sparse.hstack([upperMat, lowerMat]) - self.gex.alts = newalts - self.gex.genes = newgenes - self.gex.cells += newMatrix.cells - self.gex.Ngenes = len(self.gex.genes) - self.gex.Ncells = len(self.gex.cells) - self.gex.makeIndices() - self.genranks.update(newMatrix.rankInCell) - for k, v in newMatrix.genMax.iteritems(): - if k in self.genorm: - other = self.genorm[k] - if v > other: - self.genorm[k] = v - else: - self.genorm[k] = v - return - -class gexMat: - def __init__(self, fn, genome='matrix', sample_prefix='', mincell=5, minread=0, rankedgenes=100, genenames=True, keeponlygenes=None, filtered=False, barcodehistory={}, matrixfn=None, ranksfn=None, force_recompute=False, verbose=False): - self.verbose=verbose - self.barcodehistory=barcodehistory - self.genome=genome - self.readMatrix(fn, genenames=genenames, genome=genome) - if sample_prefix != '': - self.renameCells(sample_prefix, self.barcodehistory) - if keeponlygenes == None: - keeponlygenes = [] - else: - if not isinstance(keeponlygenes, (list)): - try: - keeponlygenes = tabReader(keeponlygenes, tabformat='list_of_list', evenflatter=True) - except: - keeponlygenes = [] - if len(keeponlygenes) > 0: - self.throwGenesAway(keeponlygenes) - else: - if filtered: - self.filterMatrix(mincell=mincell, minread=minread) - if matrixfn != None: - if os.path.isfile(matrixfn): - if force_recompute: - if genenames: - self.saveMatrix(matrixfn, self.matrix, self.genome, self.cells, self.genes, self.alts) - else: - self.saveMatrix(matrixfn, self.matrix, self.genome, self.cells, self.alts, self.genes) - else: - if os.access(os.path.dirname(matrixfn), os.W_OK): - if genenames: - self.saveMatrix(matrixfn, self.matrix, self.genome, self.cells, self.genes, self.alts) - else: - self.saveMatrix(matrixfn, self.matrix, self.genome, self.cells, self.alts, self.genes) - self.makeIndices() #It is important to create indexes only after we don't plan more modifications - if ranksfn == None: - self.rankGenes(minread=minread, rankedgenes=rankedgenes) - else: - if os.path.isfile(ranksfn): - if force_recompute: - self.rankGenes(minread=minread, rankedgenes=rankedgenes) - self.saveRanks(ranksfn) - else: - self.readRanks(ranksfn) - else: - self.rankGenes(minread=minread, rankedgenes=rankedgenes) - if os.access(os.path.dirname(ranksfn), os.W_OK): - self.saveRanks(ranksfn) - return - - def readMatrix(self, fn, genenames=True, genome='matrix'): - f = open(fn) - f.close() - with h5py.File(fn, 'r') as f: - ds = f[genome] - if genenames: - genes = tuple(ds['features']['name']) - alts = tuple(ds['features']['id']) - else: - genes = tuple(ds['features']['id']) - alts = tuple(ds['features']['name']) - barcodes = tuple(ds['barcodes']) - shape = tuple(ds['shape']) - data = np.array(ds['data']) - indices = np.array(ds['indices']) - indptr = np.array(ds['indptr']) - matrix = sparse.csc_matrix((data, indices, indptr), shape=shape) - self.matrix = matrix - self.genes = genes - self.cells = barcodes - self.alts = alts - self.Ngenes = len(genes) - self.Ncells = len(barcodes) - self.makeIndices() - if self.verbose: - stampTime('Gene expression matrix for ' + str(self.Ncells) + ' cells and ' + str(self.Ngenes) + ' genes read into memory.') - return - - def throwGenesAway(self, gl): - keppos = [] - for i in range(0, self.Ngenes): - if self.genes[i] in gl: - keppos.append(i) - if self.alts[i] in gl: - keppos.append(i) - keppos = list(set(keppos)) - keppos.sort() - self.matrix = self.matrix[keppos,:] - self.genes = list(self.genes[_] for _ in keppos) - self.alts = list(self.alts[_] for _ in keppos) - self.Ngenes = len(self.genes) - if self.verbose: - stampTime('Gene expression matrix filtered against genes list of '+str(self.Ngenes)+' genes.') - return - - - def saveMatrix(self, fn, matrix, genome, cells, names, genes): - with h5py.File(fn, 'w') as f: - dgroup = f.create_group(genome) - dgroup.create_dataset('data', data=matrix.data) - dgroup.create_dataset('indices', data=matrix.indices) - dgroup.create_dataset('indptr', data=matrix.indptr) - dgroup.create_dataset('shape', data=matrix.shape) - dgroup.create_dataset('barcodes', data=cells) - fgroup = f.create_group('features') - fgroup.create_dataset('id', data=genes) - fgroup.create_dataset('name', data=names) - if self.verbose: - stampTime('Filtered gene expression matrix saved.') - return - - def saveSmall(self, fn, ngene, ncell=None): - matrix = self.matrix[range(0, ngene),:] - if ncell != None: - matrix = matrix[:,range(0, ncell)] - cells = self.cells[:ncell] - else: - cells = self.cells - genes = self.genes[:ngene] - alts = self.alts[:ngene] - self.saveMatrix(fn, matrix, self.genome, cells, genes, alts) - return - - def renameCells(self, prefix, barcodehistory): - cells = [] - for cell in self.cells: - n = prefix+cell - if n not in barcodehistory: - barcodehistory[n] = [] - barcodehistory[n].append(cell) - cells.append(n) - self.cells = cells - if self.verbose: - stampTime('Prefix "'+prefix+'" added to barcodes.') - return barcodehistory - - def makeIndices(self): - self.genInd, self.cellInd = {}, {} - for i in range(0, self.Ngenes): - self.genInd[self.genes[i]] = i - for i in range(0, self.Ncells): - self.cellInd[self.cells[i]] = i - if self.verbose: - stampTime('Matrix indices linked to names.') - return - - def filterMatrix(self, mincell=5, minread=0): - keepgenes = [] - for i in range(0, self.Ngenes): - numBigEnough = 0 - matrow = self.matrix.getrow(i) - for j in range(0, self.Ncells): - if matrow.getcol(j).mean() > minread: - numBigEnough += 1 - if numBigEnough >= mincell: - keepgenes.append(i) - self.matrix = self.matrix[keepgenes,:] - self.genes = list(self.genes[_] for _ in keepgenes) - self.alts = list(self.alts[_] for _ in keepgenes) - self.Ngenes = len(self.genes) - if self.verbose: - stampTime('Gene expression matrix filtered for a minimum of more than '+str(minread)+' reads in '+str(mincell)+' cells.') - return - - def rankGenes(self, rankedgenes=100, minread=0): - self.genMax, self.rankInCell = {}, {} - for i in range(0, self.Ngenes): - self.genMax[self.genes[i]] = self.matrix.getrow(i).max() - for i in range(0, self.Ncells): - cell = self.cells[i] - self.rankInCell[cell] = {} - genlist = self.matrix.getcol(i).toarray() - genlist = zip(range(0, self.Ngenes), genlist) - genlist.sort(key=lambda x: x[1], reverse=True) - if rankedgenes < len(genlist): - genlist = genlist[:rankedgenes] - else: - rankedgenes = len(genlist) - rank, prevread, c, still_good = 0, 0, 0, True - while c < rankedgenes and still_good: - a, b = genlist[c] - if b != prevread: - rank +=1 - if b > minread: - self.rankInCell[cell][self.genes[a]] = rank - else: - still_good = False - c += 1 - prevread = b - if self.verbose: - stampTime('Genes ranked based on expression within a cell (top '+str(rankedgenes)+') with more than '+str(minread)+' reads.') - return - - def saveRanks(self, fn): - o = '\t'.join(['', 'MaxRead'] + list(self.cells))+'\n' - for gene in self.genes: - l = [] - for cell in self.cells: - generank = '' - if gene in self.rankInCell[cell]: - generank = str(self.rankInCell[cell][gene]) - l.append(generank) - o += gene + '\t' + str(self.genMax[gene]) + '\t' + '\t'.join(l) + '\n' - f = open(fn, 'w') - f.write(o) - f.close() - if self.verbose: - stampTime('Gene ranks saved to file '+fn) - return - - def readRanks(self, fn): - self.genMax, self.rankInCell = {}, {} - with open(fn) as f: - firstline = True - for line in f: - line = line.split('\n')[0] - line = line.split('\r')[0] - if firstline: - firstline = False - header = line.split('\t') - N = len(header) - for cell in header[2:]: - self.rankInCell[cell] = {} - else: - dat = line.split('\t') - gene = dat[0] - self.genMax[gene] = dat[1] - for i in range(2, N): - v = dat[i] - if v != '': - try: - v = float(v) - right_val = True - except: - right_val = False - if right_val: - self.rankInCell[self.cells[i-2]][gene] = dat[i] - if self.verbose: - stampTime('Genes ranks read from file '+fn) - return - - def __getitem__(self, key): - if key in self.cellInd: - i = self.cellInd[key] - return self.cell(self.matrix.getcol(i), self.genInd) - else: - return self.cell(sparse.csc_matrix((self.Ngenes, 1)), self.genInd) - - def __str__(self): - return str(self.Ngenes) + ' genes for ' + str(self.Ncells) + ' cells' - - class 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+++ /dev/null @@ -1,314 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - -import json - -from utils import stampTime, tabReader, namedMatrix - -def __main__(): - print 'This module is not intended for direct command line usage. Currently supports import to Python only.' - #A table maker function could be added in the future so that parse data could be saved and passed to R - moduleTest() - return - -def moduleTest(): - print 'Testing module by parsing test TCR data' - prefix, name = 'S1_', 'Control' - consensusFile = 'testdata/pbmc_t_consensus_annotations.json' - contigTabFile = 'testdata/pbmc_t_filtered_contig_annotations.csv' - tcr = scTCR(verbose=True) - tcr.addSampleTCR(contigTabFile, consensusFile, prefix, name) - print 'Number of cells:', len(tcr.cellTable) - print 'Cell keys:', tcr.cellTable.keys()[:5] - print 'A cell example:' - print tcr.cellTable[tcr.cellTable.keys()[0]] - print 'Consensus keys:', tcr.consensusTable.keys()[:5] - print 'A consensus example:' - print tcr.consensusTable[tcr.consensusTable.keys()[0]] - print 'Clonotype keys:', tcr.clonotypeTable.keys()[:5] - print 'A clonotype example:' - print tcr.clonotypeTable[tcr.clonotypeTable.keys()[0]] - return - -defaultCellGroups = { - 'samples': {}, - 'chain pairing': { - 'Single pair': [], - 'Double alpha': [], - 'Double beta': [], - 'Double orphan alpha': [], - 'Double orphan beta': [], - 'Orphan alpha': [], - 'Orphan beta': [], - 'Full doublet': [] - }, - 'TRA V segment usage': {}, - 'TRA D segment usage': {}, - 'TRA J segment usage': {}, - 'TRB V segment usage': {}, - 'TRB D segment usage': {}, - 'TRB J segment usage': {}, - 'major clonotypes': {} - } - -class scTCR: - def __init__(self, contigTable=None, barcodeRe={}, cellTable={}, consensusTable={}, clonotypeTable={}, clonaliasTable=[{}, {}], cdrTable={}, cellGroups=None, criticalClonotypeSize=4, verbose=False): - self.barcodeRe=barcodeRe - self.cellTable=cellTable - if contigTable == None: - self.contigTable = namedMatrix(unique=False) - else: - self.contigTable=contigTable - if cellGroups == None: - self.cellGroups = defaultCellGroups.copy() - else: - self.contigTable=contigTable - self.consensusTable=consensusTable - self.clonotypeTable=clonotypeTable - self.clonaliasTable=clonaliasTable - self.cdrTable=cdrTable - self.cellGroups=cellGroups - self.criticalClonotypeSize=criticalClonotypeSize - self.verbose=verbose - return - - def addSampleTCR(self, contigTabFile, consensusFile, prefix, sname, barcodeRe=None): - if barcodeRe != None: - self.barcodeRe = barcodeRe - self.contigTable, self.barcodeRe, self.cellTable, self.clonotypeTable, self.clonaliasTable, self.cdrTable, self.cellGroups = self.clonotypeFromContigs(contigTabFile, prefix, sname, contigTable=self.contigTable, barcodeRe=self.barcodeRe, cellTable=self.cellTable, clonotypeTable=self.clonotypeTable, clonaliasTable=self.clonaliasTable, cdrTable=self.cdrTable, cellGroups=self.cellGroups) - self.consensusTable, self.parseConsensus(consensusFile, prefix, self.consensusTable) - if self.verbose: - stampTime('TCR data of a new sample with prefix ' + prefix + ' added.') - return - - def clonotypeFromContigs(self, fn, prefix, sname, contigTable=None, barcodeRe={}, cellTable={}, clonotypeTable={}, clonaliasTable=[{}, {}], cdrTable={}, cellGroups=None): - if cellGroups == None: - cellGroups = defaultCellGroups.copy() - - def lineParse(data, line, lineNum, firstline, includend, test_only=False): - if test_only: - return locals() - if firstline: - firstline = False - header = line[1:] - data.setColNames(line[1:]) - else: - key = prefix+line[0] - line = line[1:] - readcol = data.colnames['reads'] - reads = line[readcol] - try: - reads = int(reads) - except: - pass - line[readcol] = reads - cdr3 = line[data.colnames['cdr3']] - if cdr3 == 'None': - cdr3 = '' - cdr3 = cdr3.replace('*', 'X') - line[data.colnames['cdr3']] = cdr3 - cdr3_nt = line[data.colnames['cdr3_nt']] - if cdr3_nt == 'None': - cdr3_nt = '' - line[data.colnames['cdr3_nt']] = cdr3_nt - dat = data.matrixRow(key, line, data) - data.add(dat) - return data, firstline, includend - - contigTable = tabReader(fn, data=contigTable, tabformatter=lambda x: x, lineformatter=lineParse) - bySample = cellGroups['samples'] - bySample[sname] = [] - bySample = bySample[sname] - byChain = cellGroups['chain pairing'] - bySegment = { - 'TRAV': cellGroups['TRA V segment usage'], - 'TRAD': cellGroups['TRA D segment usage'], - 'TRAJ': cellGroups['TRA J segment usage'], - 'TRBV': cellGroups['TRB V segment usage'], - 'TRBD': cellGroups['TRB D segment usage'], - 'TRBJ': cellGroups['TRB J segment usage'] - } - n = 0 - for m, cell, rowdata in contigTable: - bc = cell.split(prefix) - if len(bc) == 2: - bc = bc[1] - if cell not in barcodeRe: - barcodeRe[cell] = [] - barcodeRe[cell].append(bc) - bySample.append(cell) - for g in ['v_gene', 'd_gene', 'j_gene', 'c_gene']: - e = rowdata[g] - for s in e: - if s[:4] in bySegment: - seg = bySegment[s[:4]] - if s not in seg: - seg[s] = [] - bySegment[s[:4]][s].append(cell) - numcellchains, cellchains = len(e), {'TRA': ['', 0, '', ''], 'TRB': ['', 0, '', ''], '_TRA': ['', 0, '', ''], '_TRB': ['', 0, '', '']} - n += numcellchains - for i in range(0, numcellchains): - cdr3 = rowdata['cdr3'][i] - cdr3_nt = rowdata['cdr3_nt'][i] - chain = rowdata['chain'][i] - reads = rowdata['reads'][i] - cons = prefix+rowdata['raw_consensus_id'][i] - if chain in ['TRA', 'TRB']: - if cellchains[chain][0] == '': - cellchains[chain] = [cdr3, reads, cdr3_nt, cons] - else: - if reads > cellchains[chain][1] : - if cdr3 == '': - if cellchains['_'+chain] == '' and reads > cellchains['_'+chain][1]: - cellchains['_'+chain] = [cdr3, reads, cdr3_nt, cons] - else: - cellchains['_'+chain] = cellchains[chain][:] - cellchains[chain] = [cdr3, reads, cdr3_nt, cons] - else: - cellchains['_'+chain] = [cdr3, reads, cdr3_nt, cons] - if cellchains['TRA'][0] == '': - if cellchains['TRB'][0] == '': - pass - else: - if cellchains['_TRB'][0] == '': - byChain['Orphan beta'].append(cell) - else: - byChain['Double orphan beta'].append(cell) - else: - if cellchains['TRB'][0] == '': - if cellchains['_TRA'][0] == '': - byChain['Orphan alpha'].append(cell) - else: - byChain['Double orphan alpha'].append(cell) - else: - if cellchains['_TRB'][0] == '': - if cellchains['_TRA'][0] == '': - byChain['Single pair'].append(cell) - else: - byChain['Double alpha'].append(cell) - else: - if cellchains['_TRA'][0] == '': - byChain['Double beta'].append(cell) - else: - byChain['Full doublet'].append(cell) - cloneID = '|'.join([cellchains[x][0] for x in ['TRA', 'TRB', '_TRA', '_TRB']]) - if cloneID in clonaliasTable[0]: - clonotype = clonaliasTable[0][cloneID] - else: - clonotype = 'ct_'+cell - clonaliasTable[0][cloneID] = clonotype - clonotypeTable[clonotype] = { - 'cells': [], - 'cdrSym': cloneID, - 'alias': '', - 'chains': { - 'TRA': { - 'primary_cdr3_aa': cellchains['TRA'][0], - 'primary_cdr3_nt': cellchains['TRA'][2], - 'primary_consensus': cellchains['TRA'][3], - 'secondary_cdr3_aa': cellchains['_TRA'][0], - 'secondary_cdr3_nt': cellchains['_TRA'][2], - 'secondary_consensus': cellchains['_TRA'][3] - }, - 'TRB': { - 'primary_cdr3_aa': cellchains['TRB'][0], - 'primary_cdr3_nt': cellchains['TRB'][2], - 'primary_consensus': cellchains['TRB'][3], - 'secondary_cdr3_aa': cellchains['_TRB'][0], - 'secondary_cdr3_nt': cellchains['_TRB'][2], - 'secondary_consensus': cellchains['_TRB'][3] - } - } - } - clonotypeTable[clonotype]['cells'].append(cell) - for i in range(0, numcellchains): - cdr = rowdata['cdr3'][i] - inalfa, inbeta = 0, 0 - chain = rowdata['chain'][i] - if chain == 'TRA': - inalfa += 1 - if chain == 'TRB': - inbeta += 1 - if cdr not in cdrTable: - cdrTable[cdr] = {'nt_seq': [rowdata['cdr3_nt'][i]], 'cells': [cell], 'clonotypes': [clonotype]} - else: - cdrTable[cdr]['nt_seq'].append(rowdata['cdr3_nt'][i]) - cdrTable[cdr]['cells'].append(cell) - cdrTable[cdr]['clonotypes'].append(clonotype) - cellTable[cell] = {'clonotype': clonotype, 'chain_epression': {'TRA': cellchains['TRA'][1], 'TRB': cellchains['TRB'][1], '_TRA': cellchains['_TRA'][1], '_TRB': cellchains['_TRB'][1]}} - numPairTcells = len(set(bySample)&set(cellGroups['chain pairing']['Single pair'])) - if self.verbose: - stampTime('TCR sequences of ' + str(n) + ' contigs of ' + str(len(bySample)) + ' cells parsed (' + str(numPairTcells) + ' of these have a single TCR chain pair).') - return contigTable, barcodeRe, cellTable, clonotypeTable, clonaliasTable, cdrTable, cellGroups - - def collectSegments(self, annotations, cdrStart, cdrStop): - segments, segline = [], [] - for anne in annotations: - segments.append([anne['feature']['gene_name'], anne['contig_match_start'], anne['contig_match_end']]) - chain = anne['feature']['chain'] - segments.sort(key=lambda x: x[2]) - previ, inserted = 0, 0 - for s in segments: - difi = s[1] - previ - if previ >= cdrStart: - if s[1] <= cdrStop: - inserted += difi - previ = s[2] - if previ >= cdrStart: - if s[1] <= cdrStop: - segline.append(s[0]) - return inserted, segments, segline, chain - - def parseConsensus(self, fn, prefix, consensusTable): - with open(fn) as f: - data = json.load(f) - for clone in data: - contid, clonotype, seq, aas, info = prefix+clone['contig_name'], prefix+clone['clonotype'], clone['sequence'], clone['aa_sequence'], clone['info'] - cdrAA, cdrNuc, cdrStart, cdrStop = clone['cdr3'], clone['cdr3_seq'], clone['cdr3_start'], clone['cdr3_stop'] - highConf, mayProd, beenFilt = clone['high_confidence'], clone['productive'], clone['filtered'] - inserted, segments, segline, chain = self.collectSegments(clone['annotations'], cdrStart, cdrStop) - consensusTable[contid] = { - 'clonotype': clonotype, - 'nseq': seq, - 'aseq': aas, - 'ncdr': cdrNuc, - 'acdr': cdrAA, - 'conf': highConf, - 'prod': mayProd, - 'filt': beenFilt, - 'chain': chain, - 'segments': segline, - 'cdrlen': len(cdrAA), - 'addednuc': inserted - } - for k, v in info.iteritems(): - if k not in ['cells', 'cell_contigs']: - consensusTable[contid][k] = v - consensusTable[contid]['cells'] = [prefix+x for x in info['cells']] - consensusTable[contid]['cell_contigs'] = [prefix+x for x in info['cell_contigs']] - if self.verbose: - stampTime('Consensus contig annotations parsed.') - return consensusTable - - def reassessClonotypes(self): - sortand = [] - for clone, info in self.clonotypeTable.iteritems(): - N = len(info['cells']) - info['cellNum'] = N - if N >= self.criticalClonotypeSize: - if info['cdrSym'] != '|||': - sortand.append([clone, N]) - sortand.sort(key= lambda x: x[1], reverse=True) - n = 0 - for a, b in sortand: - n += 1 - newname = 'clonotype_'+str(n) - self.clonotypeTable[a]['alias'] = newname - self.cellGroups['major clonotypes'][newname] = self.clonotypeTable[a]['cells'][:] - self.clonaliasTable[1][newname] = a - if self.verbose: - stampTime('After reassignment of clonotypes, ' + str(len(self.cellGroups['major clonotypes'])) + ' major clonotypes were found.') - return - -if __name__ == '__main__': - __main__() \ No newline at end of file diff --git a/scTCRpy/parseTCR.pyc b/scTCRpy/parseTCR.pyc deleted file mode 100644 index a365c35de7542c76e890b423a00eb5469eeae1b5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 11146 zcmb_i+ix6MT0f`S?soeU+p!&|b8#xSnVwt1Zg!oS;F(QglYxQtgtBKm8cocb6;d*@vqCGxKd=I=0P%ptN(ilhC*IILB7_hi@qmzcfCtzS+Nb6B`%YC~ z?2I%d>vo+w=R4o|&h5LM?^N!8CPqK47amkq`Zt94O+4|xA@T69Rb8ojCU2>_Rt-zt zvs5Fe?&VZtNZlJ!+RLe@KpIxwkYw^ID5?N$hSg#)qPCFBt3|#F<~3qoMe`apuQBr) zH?Ik`=p93IG@3N8Df5~(ujA%5V_vi7HK!Ku6_j^E)zEC7V|k*~x9*K7zo@*E>Y=5g ze^vf4p*jSLR%_)FCK~(JE8`VlusW!vd593x` zaYOKqn*;z{*@=-}eFl0hs3(5pdu*2kTLX=%@gwQ(ksoa~V_z6|wbj`4BfD9%$q(_3 zm`t>cto_hGh;Ibk+ISMlEkUsTeMm{&zX2IdD=>I7K+__gHOO6P)KN&~H^}ID16l=Jwb;t}QnloB=H|xIOb3Vy2 zg0rESIss(^5Kx&NCG2-M@@v7qG^YwkCi3fS}$H=gMs zG$SB{Dc|1d^$>_31rVwPqmoMBMp14U|I|6UOqFeXf-T0c7)3FDf%rcXH8@x zpd?f|ERc)wMK*m%Xx)Paj1yZJA8)$$`s5a1$ImRwhLn*@63WL3t zU#7AV@!fQzYSZ%_-yye&5Uw^u-=S$0U2w;($G+HSqwI`4Qx$uD&27~a>almDW@|T= z*3~BL8TA_#gMm8~Fh{z~^nhCr+}Mzk&G#Zxm}A2zNP?7>^u{Wz1NGgz~VV3XPMYcq&+gmTECc~4#FU*RIYt~# zyeymnPJ$4yYD7KB7491@=`twT!myO#C&NqzN7!gbcxVRA(x=s&iNau>9GV4*oJI_G6TDI+;NaUMB{cB(f{ z)5!y~KyP$L$vaRu-%+Xt9?|MktvX|r7HTByB=`kPBh4DMpOwKzR2t-vN&2WJB}>Q70ez(7Uv3Pr;x>=g4yH9;zZGb zKAKgXNwxpic?@Xf!Jp^v=Tv7(b*5G4xN1+6n?HkxkFn2CDYj>r_vS>Q{+LCaK}x6t zDg@|ZK}8Fi1ILV})e^-=awW)sX{#|A7N$u^?|;VQ3r;LvNqMK#7RQjN&a7&q_gPS$ zQ~R!J&jI``)j6TsCzN+u+3jQMVO~WwjZ6wEb>^9E&&wcwAcGKy45Ehy92t!1f3K$T zzp?ke-r(%!I4GDG?+n+4=^2j6JBu^gA<)86(7B@^TP<3hld4lv?Mc->sjhRa-`6-` zfIiAkEX?d3-8sbxd*@ZDeF~F4Eu{-->5P;vrlqq|x|Eg{xI1im*#PD{=T!Tw@>XPh zZdz@0d=`6+rNjhi1^?D+gZzSOgX}p@&Y&#qeLF{JijPHlIHV#s2jsNj*K?@!ULw8u zjM&F%n=UwQ2l$arQk2YT0C1^u0oqS{zd$jcN1=^>gNoWE2pIeN z3RKZ~<$cvi%{2rz;6XwN`!@4yKuL>pSmdwwii;>-H^pqqZ}y5reydj`@(pwYF-|+k zwlVF1#l8+$?CT)gqqGAS`#NAT>wrq(7tt^D+Xbj?d6h8HK8>^gziLqW|6LuD{)@`{ zB?%;4Qk~0(Wgi=KR^<4TeESlW@Ab|l@ls^xDB$1Ew=bKD%TjSs!3m{e{+E0Jp98ug zRV-Ujsw{GuS%gOQz5x&z1qNpC#|r<{-SBZ?SuiSQ zVBugTJqQf|H_5KhTkxCUD*b1HuF_3~Lo8G>s^-00Y91x(e<2{S+X8F9WH6SyE)}dK zEgEpKxp26!P@}3=fS+{-{=llZmUM}R@X%3--$IlMEh2Ovk+L-y^yoSr1w@AE3TSn% z19BK0*2k1F0v&!XsvQ7JmF7F>EydM>3)dkBIl~B0h7-^!s)H%zTw?`l@v2(L_0)`h$rv^aGPZpo&GOQ4mxt7bE*MNaOiM@!k7a>HMo^8D7 zn)serS~y!odsWhpBrZ@zbtOzp9EJX4y@_K?>@W~2Cny}GmYele_>QrJ{Ho159m!Y% zDu`DKmB`<%yHyh)ZAdiKX+9PVc);TR$_Yr~TikWVGNmCih&0Kuu7+?#A~lTB1Yxz_ z@(>L*QCDtSflOMp2qGkFb(3<14L7*t&0GN9^tPf{Eg z2Y8oZ(2q!Hk>n72DM$onRL@$+^r)V|lhadphLD?vcU(kdW;)GH!%Lpf&NZ}@FjeAi zBHW8*Lwv5v{mGs+?79aKcuxb9IHPQP5dgb|u9au$pgXzU}B z~P7cLU}m#!oP`pOj( z!1V#!3U3FYTd%okmO_{Hdl!N9b&&yXvg#R|=d?4< zMNnDC6aO>m!F)_w#~04Sl$c*mk9B{?tUQAKlQ1u%z?*=HnLzlTCTL0@6UZf9LHPtq z=64*>SzWSb@qS&~)(rCJ0h!e~z-BG{SQ>m=(|QpUr>#*?$-&(5m&0$e`0=f?9$!PMw3VY@r}t*nMusyCw-0_3+QBshR)Jx1gtj%pK=Z15f}^$^bKkHimTO-?=3QijVohQmcs?fpnRIrxbP zFwx=cw%R|`>T^1}oC9#-lFD#DS9|;X-=Gg_eCL9y4Z-GfX;y_RYrdPbBI9AvFjeH* ziN?Sv9$dsG)$I!Is-r~A$p9GAZ+QL&8&DefP_D$sprFFX;G(8+cQJ;2+)U{(MP#1W z*Kls4?N9NDy)+7q<1n3x`9;Ziu|@@YD2h&qaknk2A>mDNO#5ymk5WCQPa8?$0AX zUpPGS1t#VTasiIL50M>Lh`b_biYOi{AP{`vN}MkQU2|41b}Gj#!Y5FT_#+ORIUzE` z_$C6I2xkUzRRm$@iJM=6P;?lrMT7=lf#66a0oy4CY)>!BdX7 zYB427x1rF4!kr@clp7iQwnUI!L1TV{w zra(wdGNfZv)ptlp{5CJ*#XV;y_bFkk>U_XTYDb#g^pl1-z68K>=JTZ&aFOV3xNhwH9$N~!7u~v*Ey611{Rydm%4#kL_XYE7k^L#Oo;o_(C^~4%a;obriozQt$|Pz`d!j zXj@|+s8 z*l&C}1c5`W2DS@g!pkB2qii9zh@5B`nr~ctn0OEz1zun#)SS4Gq`V^5iVG=v4vE@3 z!T$|b$|WV#-BXyx0(tR?vNRMPy^@CUN_-gyVnXmSfL??*fY&HJNt8iyKJ^AL4hS$* zCp4i%c+wJvy}&^t@(bMvzhMbT1VGmrFJx)&`$i#*s_0D(xrVX?c}~0A4jr*CXvcwT zP0H@7k(C0nlobS9oDY$Y$!qO)+xgNS0X8Fb0hs$aan4PUdIR*#37Uh174{Kbp%|s7 zacZ0Hb!o-fKvBL>I{Zn7d=7J~2I;gB{=$K6E0m8%_z6>Xs*7K@V`@TENS>w3Ap=kOgosw}nVkrT=Rxok_*u{3Vj!bR_q@ z2cr>?V#rO7+h($ diff --git a/scTCRpy/plotGraphs.py b/scTCRpy/plotGraphs.py deleted file mode 100644 index dfcaa0dd3..000000000 --- a/scTCRpy/plotGraphs.py +++ /dev/null @@ -1,669 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - - -import math, collections, matplotlib -matplotlib.use('Agg') -import matplotlib.pyplot as plt -import numpy as np -import seaborn as sns -import pandas as pd - -def __main__(): - print 'This module is not intended for direct command line usage. Currently suports import to Python only.' - return - -class colorPalette: - def __init__(self, colors, expandcorrection=1, shiftcorrection=0, is_divergent=False): - self.N = len(colors) - self.colors = tuple(colors) - self.a = expandcorrection - self.b = shiftcorrection - if is_divergent: - self.a = self.a*0.5 - self.b = self.b+0.5 - return - - def __getitem__(self, i): - if type(i) == str: - return i - else: - if type(i) == list: - colorl = [] - for e in i: - color = self.colors[0] - if isinstance(e, int): - color = self.colorOfInt(e) - else: - if isinstance(e, float): - color = self.colorOfFloat(e) - colorl.append(color) - return colorl - else: - if isinstance(i, int): - return self.colorOfInt(i) - else: - if isinstance(i, float): - return self.colorOfFloat(i) - else: - return self.colors[0] - - def colorOfInt(self, i): - if i < self.N: - return self.colors[i] - else: - return self.colors[-1] - return - - def colorOfFloat(self, i): - x = self.a*i + self.b - if x > 0: - if x < 1: - return self.colors[int(self.N*x)] - else: - return self.colors[-1] - else: - return self.colors[0] - -discretePal = colorPalette(['#008E9B', '#0087AF', '#407CB8', '#746DB1', '#9B5A99', '#B14A73']) -discretePal = colorPalette(['#4980d8', '#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#ffe900', '#800000', '#aaffc3', '#808000', '#ffd8b1', '#000075', '#808080']) -continousPal = colorPalette([(1.0, 1.0, 0.8980392156862745, 1.0), (0.9990157631680123, 0.9996309111880046, 0.8926259131103422, 1.0), (0.9980315263360247, 0.9992618223760092, 0.8872126105344099, 1.0), (0.9970472895040369, 0.9988927335640139, 0.8817993079584775, 1.0), (0.9950788158400615, 0.9981545559400231, 0.8709727028066129, 1.0), (0.9940945790080739, 0.9977854671280277, 0.8655594002306806, 1.0), (0.9931103421760861, 0.9974163783160324, 0.8601460976547481, 1.0), (0.9921261053440984, 0.9970472895040369, 0.8547327950788158, 1.0), 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(0.9595176584705882, 0.7669728545098039, 0.6741447150392157, 1.0), (0.9605811984235294, 0.7625010185254902, 0.6679635471019607, 1.0), (0.9616447383764706, 0.7580291825411765, 0.6617823791647058, 1.0), (0.9627082783294117, 0.7535573465568628, 0.655601211227451, 1.0), (0.9648353582352941, 0.7446136745882352, 0.6432388753529412, 1.0), (0.9658988981882353, 0.7401418386039216, 0.6370577074156862, 1.0), (0.9669624381411764, 0.7356700026196078, 0.6308765394784314, 1.0), (0.9675442976352941, 0.7308497161882352, 0.6246854782352941, 1.0), (0.968203399, 0.7208441, 0.6122929913333334, 1.0), (0.9685329496823529, 0.7158412919058823, 0.6060967478823529, 1.0), (0.9688625003647059, 0.7108384838117647, 0.5999005044313725, 1.0), (0.9695216017294117, 0.7008328676235294, 0.5875080175294117, 1.0), (0.9698511524117647, 0.6958300595294117, 0.5813117740784314, 1.0), (0.9696829796666666, 0.6904839307372549, 0.5751383613647059, 1.0), (0.9692885689999999, 0.6849817470823529, 0.5689753262588235, 1.0), (0.9684997476666667, 0.673977379772549, 0.5566492560470588, 1.0), (0.968105337, 0.6684751961176472, 0.5504862209411766, 1.0), (0.9677109263333333, 0.6629730124627451, 0.5443231858352942, 1.0), (0.966922105, 0.6519686451529412, 0.5319971156235295, 1.0), (0.9660167198392157, 0.6461297415882352, 0.5258903482588235, 1.0), (0.9649113881372549, 0.6401590780588234, 0.5198055987058824, 1.0), (0.963806056435294, 0.6341884145294118, 0.5137208491529413, 1.0), (0.9615953930313725, 0.6222470874705882, 0.5015513500470589, 1.0), (0.9604900613294117, 0.6162764239411764, 0.49546660049411767, 1.0), (0.9593847296274509, 0.6103057604117648, 0.4893818509411765, 1.0), (0.9566532109764706, 0.598033822717647, 0.4773022923529412, 1.0), (0.9548534056117647, 0.5916223450078432, 0.4713374634901961, 1.0), (0.9530536002470588, 0.5852108672980392, 0.465372634627451, 1.0), (0.951253794882353, 0.5787993895882353, 0.4594078057647059, 1.0), (0.9476541841529411, 0.5659764341686274, 0.4474781480392157, 1.0), (0.9458543787882353, 0.5595649564588235, 0.44151331917647063, 1.0), (0.9440545734235294, 0.5531534787490197, 0.4355484903137255, 1.0), (0.9417279298235294, 0.5464134770196079, 0.429707070372549, 1.0), (0.9367796132117647, 0.5327495001098039, 0.41809333948627453, 1.0), (0.9343054549058823, 0.525917511654902, 0.4122864740431373, 1.0), (0.9318312966, 0.5190855232, 0.4064796086, 1.0), (0.9268829799882353, 0.5054215462901961, 0.3948658777137255, 1.0), (0.9244088216823529, 0.49858955783529413, 0.38905901227058826, 1.0), (0.921406221227451, 0.49142041718431373, 0.38340843537647057, 1.0), (0.9182816725843137, 0.48417347218039214, 0.37779392507058823, 1.0), (0.9120325752980393, 0.469679582172549, 0.36656490445882356, 1.0), (0.908908026654902, 0.46243263716862765, 0.36095039415294133, 1.0), (0.9057834780117647, 0.4551856921647059, 0.35533588384705883, 1.0), (0.8995343807254902, 0.4406918021568627, 0.34410686323529416, 1.0), (0.8958845948352941, 0.43307455670588235, 0.3386806345176471, 1.0), (0.8921375427882353, 0.4253887370980392, 0.33328927276078435, 1.0), (0.8883904907411765, 0.41770291749019606, 0.32789791100392157, 1.0), (0.8808963866470588, 0.4023312782745098, 0.3171151874901961, 1.0), (0.8771493346, 0.39464545866666667, 0.31172382573333335, 1.0), (0.8734022825529412, 0.3869596390588235, 0.3063324639764706, 1.0), (0.8653913329372549, 0.3711276720470588, 0.2957689564156863, 1.0), (0.8610536002941176, 0.3629157635294118, 0.2906281271764706, 1.0), (0.8567158676509804, 0.3547038550117647, 0.2854872979372549, 1.0), (0.8523781350078431, 0.34649194649411763, 0.2803464686980392, 1.0), (0.8437026697215686, 0.3300681294588235, 0.2700648102196078, 1.0), (0.8393649370784314, 0.32185622094117644, 0.26492398098039216, 1.0), (0.8350272044352941, 0.3136443124235294, 0.25978315174117644, 1.0), (0.8301865219490197, 0.30473276355294115, 0.25489142806666665, 1.0), (0.8204010983882353, 0.2867649126352941, 0.2451595198, 1.0), (0.8155083866078432, 0.2777809871764706, 0.24029356566666665, 1.0), (0.810615674827451, 0.26879706171764706, 0.23542761153333333, 1.0), (0.8008302512666666, 0.2508292108, 0.22569570326666666, 1.0), (0.7959375394862745, 0.24184528534117647, 0.22082974913333334, 1.0), (0.7905615319411765, 0.23139699905882352, 0.21624203829411764, 1.0), (0.7851533046784314, 0.2208510887215686, 0.21167287700784312, 1.0), (0.7743368501529412, 0.19975926804705882, 0.2025345544352941, 1.0), (0.7689286228901963, 0.18921335770980421, 0.19796539314901973, 1.0), (0.763520395627451, 0.17866744737254903, 0.1933962318627451, 1.0), (0.7527039411019608, 0.1575756266980392, 0.1842579092901961, 1.0), (0.7468380122117647, 0.14002101948235293, 0.17999609695686275, 1.0), (0.7409573187529412, 0.12224032527058823, 0.17574419910588235, 1.0), (0.7350766252941177, 0.10445963105882351, 0.17149230125490195, 1.0), (0.7233152383764706, 0.06889824263529411, 0.16298850555294117, 1.0), (0.717434544917647, 0.05111754842352939, 0.15873660770196077, 1.0), (0.7115538514588235, 0.03333685421176469, 0.1544847098509804, 1.0)], is_divergent=True) -grayscalePal = colorPalette(['#000000', '#080808', '#101010', '#181818', '#202020', '#282828', '#303030', '#383838', '#404040', '#484848', '#505050', '#585858', '#606060', '#686868', '#696969', '#707070', '#787878', '#808080', '#888888', '#909090', '#989898', '#A0A0A0', '#A8A8A8', '#A9A9A9', '#B0B0B0', '#B8B8B8', '#BEBEBE', '#C0C0C0', '#C8C8C8', '#D0D0D0', '#D3D3D3', '#D8D8D8', '#DCDCDC', '#E0E0E0', '#E8E8E8', '#F0F0F0', '#F5F5F5', '#F8F8F8', '#FFFFFF']) - -def divBar(l, c, palette=discretePal): - h = '
' - N = np.sum(l) - for i in range(0, len(l)): - e = l[i] - w = 5+int((float(e)/c)*45) - g = int((float(e)/N)*20) - h+='
' - return h + '
' - -def hide_fake_axis(ax, prop=None, handletextpad=None, msize=None): - ax.set_xlim(1,2) - ax.set_ylim(1,2) - ax.axis('off') - lgnd = ax.legend(frameon=False, loc=2, handletextpad=handletextpad, prop=prop) - if msize != None: - for h in lgnd.legendHandles: - h.set_width(msize) - return ax - -def emptyFig(): - fig = plt.figure() - ax = plt.subplots(111) - return ax, fig - -def plotCellTree(order, dis, cellcolors, collabels, title='', figdim={'figsize': (7.2,2.2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - fig = plt.figure(**figdim) - ax = plt.subplot2grid((5, 7), (0, 0), rowspan=3, colspan=5) - ax2 = plt.subplot2grid((5, 7), (3, 0), rowspan=2, colspan=5, sharex=ax) - ax4 = plt.subplot2grid((5, 7), (0, 5), rowspan=5, colspan=1) - ax5 = plt.subplot2grid((5, 7), (0, 6), rowspan=5, colspan=1) - fig.subplots_adjust(left=0.02, bottom=0, wspace = 0.1) - oldLw = matplotlib.rcParams['lines.linewidth'] - matplotlib.rcParams['lines.linewidth'] = 0.2 - dendro, linkage = dis.dendroFromDist(dis.distances, dis.labels, ax=ax) - matplotlib.rcParams['lines.linewidth'] = oldLw - tl = ax.get_xticks() - step = tl[1]-tl[0] - bot = 0 - for l in dendro['leaves']: - l2, l3, l4 = cellcolors[dendro['ivl'][l]] - ax2.barh(9, step, left=bot, color=palette[l4], linewidth=0, height=1, alpha=0.7) - ax2.barh(5, step, left=bot, color=palette[l2], linewidth=0, height=1, alpha=0.7) - ax2.barh(1, step, left=bot, color=palette[l3], linewidth=0, height=1, alpha=0.7) - bot += step - ax2.text(50, 10, 'Cluster', fontsize=fonthi[0]) - ax2.text(50, 6, 'Sample', fontsize=fonthi[0]) - ax2.text(50, 2, 'Cell type', fontsize=fonthi[0]) - ax2.set_ylim(0, 12) - for i in range(0, len(collabels[0][1])): - ax4.scatter(-1, -1, color=palette[i], label=collabels[0][1][i], alpha=0.9) - for i in range(0, len(collabels[1][1])): - ax5.scatter(-1, -1, color=palette[i], label=collabels[1][1][i], alpha=0.4) - ax.axis('off') - ax2.axis('off') - ax4 = hide_fake_axis(ax4, prop={'size': fonthi[0]}) - ax5 = hide_fake_axis(ax5, prop={'size': fonthi[0]}) - fig.suptitle(title, fontsize=fonthi[1]) - return fig - -def plotGrTree(distances, groups, colors, title='', figdim={'figsize': (7.2,2.2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - fig = plt.figure(**figdim) - ax = plt.subplot2grid((5, 7), (0, 0), rowspan=4, colspan=7) - try: - distances.groupTree(groups, ax=ax) - except: - pass - fig.subplots_adjust(bottom=0.4) - fig.suptitle(title, fontsize=fonthi[1]) - for t in ax.get_xticklabels(): - t.set_color(palette[colors[t.get_text().split(', ')[0]]]) - t.set_rotation(45) - t.set_horizontalalignment('right') - t.set_fontsize(fonthi[0]) - ax.get_yaxis().set_visible(False) - return fig - -def plotSimpleHeat(distance, title='', figdim={'figsize': (9.7,7.2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - fig = plt.figure(**figdim) - ax = plt.subplot2grid((9, 7), (1, 1), rowspan=8, colspan=6) - ax_t = plt.subplot2grid((9, 7), (0, 1), rowspan=1, colspan=6)#, sharex=ax) - ax_s = plt.subplot2grid((9, 7), (1, 0), rowspan=8, colspan=1)#, sharex=ax) - fig.subplots_adjust(bottom=0.1, hspace=0.4) - f, t1, t2 = distance.biClustMap(ax1=ax_s, ax2=ax_t) - ax.imshow(f, aspect='auto') - nxl = [] - for xl in ax_t.get_xticklabels(): - nxl.append('K'+str(1+int(xl.get_text()))) - ax_t.set_xticklabels(nxl, fontsize=fonthi[0]) - ax.axis('off') - ax_s.axis('off') - ax_t.get_yaxis().set_visible(False) - ax_t.spines['top'].set_visible(False) - ax_t.spines['bottom'].set_visible(False) - ax_t.spines['left'].set_visible(False) - ax_t.spines['right'].set_visible(False) - ax_t.tick_params(bottom="off", left="off") - fig.suptitle(title, fontsize=fonthi[1]) - return fig - -def plotHorGrStBar(data, labels, xticks=[], title= '', ylabel='', ydim=None, ax=None, figdim={'figsize': (3.5,2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal, secondarypalette=grayscalePal, hubysec=False): - if ax == None: - fig = plt.figure(**figdim) - ax = plt.subplot2grid((1, 7), (0, 0), colspan=5) - ax2 = plt.subplot2grid((1, 7), (0, 5), colspan=2) - else: - fig = ax.get_figure() - #fig.set_size_inches(3.5, 2)) - fig.subplots_adjust(left=0.17, bottom=0.2, wspace = -0.15) - if hubysec: - ppalette = secondarypalette - hpalette = palette - else: - ppalette = palette - N = len(xticks) - M = len(labels) - labels = labels[::-1] - bot = [0 for x in range(0, M)] - for i in range(0, N): - y = [data[j][i] for j in range(0, M)] - x = [x+1 for x in range(0, M)] - ifi = i+0 - edgecol = None - linewidth = 0 - if hubysec: - ifi = float(i)/N - edgecol = [hpalette[j] for j in range(0, M)] - linewidth = 1 - ax.barh(x, y, left=bot, color=ppalette[ifi], edgecolor=edgecol, linewidth=linewidth, height=0.6, alpha=0.7) - ax2.bar(-1, -1, color=ppalette[ifi], label=xticks[i], width=0, alpha=0.7) - bot = [bot[x]+y[x] for x in range(0, M)] - groupWidth = 1 - ax2 = hide_fake_axis(ax2, handletextpad=0.1, prop={'size': fonthi[0]}) - ax.set_xlabel(ylabel, fontsize=fonthi[0]) - for yl in ax.get_xticklabels(): - yl.set_fontsize(fonthi[0]) - if ydim=='%': - ax.set_xticks([x for x in np.arange(0, 1.1, 0.25)]) - ax.set_xticklabels([str(int(100*x))+'%' for x in np.arange(0, 1.1, 0.25)], fontsize=fonthi[0]) - ax.set_yticks([0.5+x+(groupWidth*0.5) for x in range(0, M)]) - ax.set_yticklabels(labels, fontsize=fonthi[0]) - fig.suptitle(title) - return ax, fig - -def plotGrStBar(data, labels, xticks=[], ylabel='', ydim=None, ax=None, figdim={'figsize': (3.5,2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - if ax == None: - fig = plt.figure(**figdim) - ax = plt.subplot2grid((1, 7), (0, 0), colspan=4) - ax2 = plt.subplot2grid((1, 7), (0, 4), colspan=2) - else: - fig = ax.get_figure() - #fig.set_size_inches(3.5, 2)) - fig.subplots_adjust(left=0.1, bottom=0.35, wspace = 0.1) - N = len(xticks) - M = len(labels) - bot = [0 for x in range(0, M)] - for i in range(0, N): - y = [data[j][i] for j in range(0, M)] - x = [x+1 for x in range(0, M)] - ax.bar(x, y, bottom=bot, color=palette[i], edgecolor='white', width=0.6) - ax2.bar(-1, -1, color=palette[i], label=xticks[i], width=0) - bot = [bot[x]+y[x] for x in range(0, M)] - groupWidth = 1 - ax2 = hide_fake_axis(ax2, prop={'size': fonthi[0]}) - ax.set_ylabel(ylabel, fontsize=fonthi[0]) - for yl in ax.get_yticklabels(): - yl.set_fontsize(fonthi[0]) - if ydim=='%': - ax.set_yticks([x for x in np.arange(0, 1.1, 0.1)]) - ax.set_yticklabels([str(int(100*x))+'%' for x in np.arange(0, 1.1, 0.1)], fontsize=fonthi[0]) - ax.set_xticks([0.5+x+(groupWidth*0.5) for x in range(0, M)]) - ax.set_xticklabels(labels, fontsize=fonthi[0]) - return ax, fig - -def plotGrSegBar(data, labels, xticks=[], ylabel='', ydim=None, ax=None, figdim={'figsize': (3.5,2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - if ax == None: - fig = plt.figure(**figdim) - ax = plt.subplot2grid((1, 7), (0, 0), colspan=4) - ax2 = plt.subplot2grid((1, 7), (0, 4), colspan=2) - else: - fig = ax.get_figure() - #fig.set_size_inches(3.5, 2)) - fig.subplots_adjust(left=0.1, bottom=0.35, wspace = 0.1) - N = len(xticks) - M = len(labels) - bot = [0 for x in range(0, M)] - for i in range(0, N): - y = [data[j][i] for j in range(0, M)] - x = [x+1 for x in range(0, M)] - ax.bar(x, y, bottom=bot, color=palette[i], edgecolor='white', width=0.6) - ax2.bar(-1, -1, color=palette[i], label=xticks[i], width=0) - bot = [bot[x]+y[x] for x in range(0, M)] - groupWidth = 1 - ax2 = hide_fake_axis(ax2, prop={'size': fonthi[0]}) - ax.set_ylabel(ylabel, fontsize=fonthi[0]) - for yl in ax.get_yticklabels(): - yl.set_fontsize(fonthi[0]) - if ydim=='%': - ax.set_yticks([x for x in np.arange(0, 1.1, 0.1)]) - ax.set_yticklabels([str(int(100*x))+'%' for x in np.arange(0, 1.1, 0.1)], fontsize=fonthi[0]) - ax.set_xticks([0.5+x+(groupWidth*0.5) for x in range(0, M)]) - ax.set_xticklabels(labels, fontsize=fonthi[0]) - return ax, fig - -def plotGrBar(data, labels, barWidth=None, xticks=[], ylabel='', ax=None, figdim={'figsize': (3.5,2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - if ax == None: - fig = plt.figure(**figdim) - ax = plt.subplot2grid((1, 7), (0, 0), colspan=6) - ax2 = plt.subplot2grid((1, 7), (0, 6), colspan=1) - else: - fig = ax.get_figure() - if barWidth == None: - barWidth = 0.8/float(len(data)) - #fig.set_size_inches(3.5, 2) - fig.subplots_adjust(left=0.1, bottom=0.2, wspace = 0.1) - for i in range(0, len(data)): - y = data[i] - x = [x+(barWidth*i) for x in range(1, len(y)+1)] - ax.bar(x, y, color=palette[i], width=barWidth, edgecolor='white') - ax2.bar(-1, -1, color=palette[i], width=0, label=labels[i]) - groupWidth = barWidth*i - ax2 = hide_fake_axis(ax2, prop={'size': fonthi[0]}) - ax.set_ylabel(ylabel, fontsize=fonthi[0]) - for yl in ax.get_yticklabels(): - yl.set_fontsize(fonthi[0]) - ax.set_xticks([x+(groupWidth*0.5) for x in range(1, len(xticks)+1)]) - ax.set_xticklabels(xticks, rotation=90, fontsize=fonthi[0]) - return ax, fig - -def plotGrDivBar(data, labels, barWidth=None, xticks=[], ylabel='', ax=None, figdim={'figsize': (3.5,2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - if ax == None: - fig = plt.figure(**figdim) - ax = plt.subplot2grid((1, 7), (0, 0), colspan=5) - ax2 = plt.subplot2grid((1, 7), (0, 5), colspan=2) - else: - fig = ax.get_figure() - if barWidth == None: - barWidth = 0.8/float(len(data)) - #fig.set_size_inches(3.5, 2) - fig.subplots_adjust(left=0.15, bottom=0.41, wspace = 0.1) - for i in range(0, len(data)): - y = data[i] - x = [x+(barWidth*i) for x in range(1, len(y)+1)] - ax.bar(x, y, color=palette[i], width=barWidth, edgecolor='white') - ax2.bar(-1, -1, color=palette[i], width=0, label=labels[i]) - groupWidth = barWidth*i - ax2 = hide_fake_axis(ax2, prop={'size': fonthi[0]}, msize=8) - ax.set_ylabel(ylabel, fontsize=fonthi[0]) - for yl in ax.get_yticklabels(): - yl.set_fontsize(fonthi[0]) - ax.set_xticks([x+(groupWidth*0.5) for x in range(1, len(xticks)+1)]) - ax.set_xticklabels(xticks, rotation=90, fontsize=fonthi[0]) - return ax, fig - -def plotPairBubble(s1, s2, x, y, z, ax=None, figdim={'figsize': (3.5,2.6), 'dpi': 600}, fonthi=[8, 10, 14]): - if ax == None: - fig = plt.figure(**figdim) - ax = fig.add_subplot(111) - fig.subplots_adjust(left=0.2, bottom=0.2) - else: - fig = ax.get_figure() - ax.set_aspect(aspect=1, adjustable='box', anchor='SW') - ax.scatter(x, y, s=z) - ax.set_xlabel('Clonotype size in '+s1, fontsize=fonthi[0]) - ax.set_ylabel('Clonotype size in '+s2, fontsize=fonthi[0]) - lr = range(0, int(np.amax([x, y]))+6, 5) - ax.set_xticks(lr) - ax.set_xticklabels(lr, fontsize=fonthi[0]) - ax.set_xlim(left=0) - ax.set_yticks(lr) - ax.set_yticklabels(lr, fontsize=fonthi[0]) - ax.set_ylim(bottom=0) - ax.spines['top'].set_visible(False) - ax.spines['right'].set_visible(False) - fig.suptitle('Overlap between clonotypes in a pair of samples', fontsize=fonthi[1]) - return ax, fig - -def plotGridHist(data, xlab, ylab, zlab, collabels, order=None, palette=None, tickstep=2): - cx, cy, cz = [], [], [] - N = 0 - for x, yl in data.iteritems(): - N += 1 - for y, zl in yl.iteritems(): - for z in zl: - cx.append(x) - cy.append(y) - cz.append(z) - data = {xlab: cx, ylab: cy, zlab: cz} - df = pd.DataFrame(data) - if palette == None: - palette = sns.cubehelix_palette(N, rot=-.2, light=.5) - midPal = int(len(palette)/2) - g = sns.FacetGrid(df, row=xlab, hue=ylab, aspect=8, height=2, palette=palette, row_order=order) - g.map(sns.kdeplot, zlab, clip_on=False, shade=True, alpha=.9, lw=1.5, bw=.2) - g.map(sns.kdeplot, zlab, clip_on=False, lw=2, bw=.2, color='w') - g.map(plt.axhline, y=0, lw=2, clip_on=False, color=palette[1]) - def label(x, color, label): - if label == collabels[midPal]: - for e in x: - ax = plt.gca() - ax.text(0, .2, e, fontweight='bold', color=color, alpha=.8, ha='left', va='center', transform=ax.transAxes) - return - - g.map(label, xlab) - g.fig.subplots_adjust(hspace=.25) - g.set_titles('') - g.set(yticks=[]) - g.despine(bottom=True, left=True) - plt.xlabel(ylab, color=palette[midPal], fontweight='bold') - plt.xticks(range(tickstep, tickstep*len(collabels)+tickstep, tickstep), collabels, color=palette[0]) - tl = plt.gca().get_xticklabels() - for i in range(0, N): - tl[i].set_color(palette[i]) - fig = plt.gcf() - return fig - -def plotGexDistribution(data, xlabel, ylabel, title='', axisrotation=None, huerder=None, order=None, ylim=None, ax=None, hue=None, condensed=True, show_violin=True, show_swarm=True, show_box=True, show_strip=True, figdim={'figsize': (7.2,2.2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - if ax == None: - fig = plt.figure(**figdim) - ax = plt.subplot2grid((1, 7), (0, 0), colspan=6) - ax2 = plt.subplot2grid((1, 7), (0, 6), colspan=1) - else: - fig = ax.get_figure() - if condensed: - cx, cy, hueli = [], [], [] - for x, yl in data.iteritems(): - if x != hue: - for i in range(0, len(yl)): - cx.append(x) - cy.append(yl[i]) - if hue != None: - hueli.append(data[hue][i]) - if hue == None: - data = {xlabel: cx, ylabel: cy} - else: - data = {xlabel: cx, ylabel: cy, hue: hueli} - if huerder != None: - if hue != None: - new = zip(data[xlabel], data[ylabel], data[hue]) - new.sort(key=lambda x: huerder.index(x[2])) - new=zip(*new) - data = {xlabel: new[0][:], ylabel: new[1][:], hue: new[2][:]} - new = '' - for i in range(0, len(huerder)): - ax2.scatter(-1, -1, color=palette[i], label=huerder[i], alpha=0.8) - ax2 = hide_fake_axis(ax2, prop={'size': fonthi[0]}) - df = pd.DataFrame(data) - if show_violin: - ax = sns.violinplot(x=xlabel, y=ylabel, data=df, linewidth=0.1, inner=None, order=order, palette=palette, hue=hue, ax=ax) - if show_swarm: - ax = sns.swarmplot(x=xlabel, y=ylabel, data=df, color='white', edgecolor='gray', size=1, order=order, hue=hue, ax=ax) - if show_box: - ax = sns.boxplot(x=xlabel, y=ylabel, data=df, linewidth=0.5, saturation=0.6, boxprops={'alpha': 0.6}, showfliers=False, order=order, hue=hue, ax=ax) - #ax = sns.boxplot(x=xlabel, y=ylabel, data=df, linewidth=1, boxprops={'facecolor': 'None'}, showfliers=False, order=order, hue=hue, ax=ax) - if show_strip: - ax = sns.stripplot(x=xlabel, y=ylabel, data=df, order=order, jitter=True, dodge=True, size=1, color='black', hue=hue, ax=ax) - #ax = sns.stripplot(x=xlabel, y=ylabel, data=df, order=order, jitter=True, dodge=True, size=1, palette=palette, hue=hue, ax=ax) - if ylim != None: - ax.set_ylim(ylim[0], ylim[1]) - else: - ax.set_ylim(bottom=0) - if axisrotation != None: - ax.set_xticklabels(ax.get_xticklabels(), rotation=axisrotation, va='top', ha='right') - fig.subplots_adjust(bottom=0.35) - for yl in ax.get_yticklabels(): - yl.set_fontsize(fonthi[0]) - for xl in ax.get_xticklabels(): - xl.set_fontsize(fonthi[0]) - ax.set_ylabel(ax.get_ylabel(), fontsize=fonthi[0]) - ax.set_xlabel(ax.get_xlabel(), fontsize=fonthi[0]) - ax.get_legend().remove() - fig.suptitle(title, fontsize=fonthi[1]) - return ax, fig - -def plotDistribution(data, xlabel, ylabel, title='', axisrotation=None, huerder=None, order=None, ylim=None, ax=None, hue=None, condensed=True, show_violin=True, show_swarm=False, show_box=False, show_strip=False, figdim={'figsize': (3.5,2), 'dpi': 600}, fonthi=[8, 10, 14], palette=discretePal): - if ax == None: - fig = plt.figure(**figdim) - ax = plt.subplot2grid((1, 7), (0, 0), colspan=5) - ax2 = plt.subplot2grid((1, 7), (0, 5), colspan=2) - else: - fig = ax.get_figure() - if condensed: - cx, cy, hueli = [], [], [] - for x, yl in data.iteritems(): - if x != hue: - for i in range(0, len(yl)): - cx.append(x) - cy.append(yl[i]) - if hue != None: - hueli.append(data[hue][i]) - if hue == None: - data = {xlabel: cx, ylabel: cy} - else: - data = {xlabel: cx, ylabel: cy, hue: hueli} - if huerder != None: - if hue != None: - new = zip(data[xlabel], data[ylabel], data[hue]) - new.sort(key=lambda x: huerder.index(x[2])) - new=zip(*new) - data = {xlabel: new[0][:], ylabel: new[1][:], hue: new[2][:]} - new = '' - for i in range(0, len(huerder)): - ax2.scatter(-1, -1, color=palette[i], label=huerder[i], alpha=0.8) - ax2 = hide_fake_axis(ax2, prop={'size': fonthi[0]}) - fig.subplots_adjust(left=0.18, bottom=0.3, wspace = 0.1) - df = pd.DataFrame(data) - if show_violin: - ax = sns.violinplot(x=xlabel, y=ylabel, data=df, linewidth=0.1, inner=None, order=order, palette=palette, hue=hue, ax=ax) - if show_swarm: - ax = sns.swarmplot(x=xlabel, y=ylabel, data=df, color='white', edgecolor='gray', size=1, order=order, hue=hue, ax=ax) - if show_box: - ax = sns.boxplot(x=xlabel, y=ylabel, data=df, linewidth=0.5, saturation=0.6, boxprops={'alpha': 0.6}, showfliers=False, order=order, hue=hue, ax=ax) - #ax = sns.boxplot(x=xlabel, y=ylabel, data=df, linewidth=1, boxprops={'facecolor': 'None'}, showfliers=False, order=order, hue=hue, ax=ax) - if show_strip: - ax = sns.stripplot(x=xlabel, y=ylabel, data=df, order=order, jitter=True, dodge=True, size=1, color='black', hue=hue, ax=ax) - #ax = sns.stripplot(x=xlabel, y=ylabel, data=df, order=order, jitter=True, dodge=True, size=1, palette=palette, hue=hue, ax=ax) - if ylim != None: - ax.set_ylim(ylim[0], ylim[1]) - if axisrotation != None: - ax.set_xticklabels(ax.get_xticklabels(), rotation=axisrotation, va='top', ha='right') - fig.subplots_adjust(bottom=0.2) - nxl = [] - oxl = ax.get_xticklabels() - for i in range(0, len(oxl)): - tx = oxl[i].get_text() - if i % 2 == 0: - nxl.append(tx) - else: - nxl.append('\n'+tx) - ax.set_xticklabels(nxl, fontsize=fonthi[0]) - for yl in ax.get_yticklabels(): - yl.set_fontsize(fonthi[0]) - ax.set_ylabel(ax.get_ylabel(), fontsize=fonthi[0]) - ax.set_xlabel(ax.get_xlabel(), fontsize=fonthi[0]) - ax.get_legend().remove() - fig.suptitle(title, fontsize=fonthi[1]) - return ax, fig - -def plotFlowChain(regions, segFreqs, lineWidths, palette, freqTh=5, showlines=True, ylabel='', regionlabels=None, title='', ax=None): - if ax == None: - fig = plt.figure() - ax = fig.add_subplot(111) - else: - fig = ax.get_figure() - if regionlabels == None: - regionlabels = regions[:] - orderlists, bottoms = {}, {} - M = sum(lineWidths.values()) - ic = 0 - for region in regions: - N = len(segFreqs[region]) - ic += 1 - yl, ol = [], [] - baseline, cc = 0, 0 - for m, n in segFreqs[region].most_common()[::-1]: - if not showlines: - put_text = False - color = palette[freqTh] - if N-cc < freqTh: - color = palette[N-cc-1] - put_text = True - ax.bar(ic, n, width=.2, bottom=baseline, color=color) - if put_text: - ax.text(ic-0.1, baseline+n, m, color='black') - ol.append(m) - bottoms[m] = baseline - cc += 1 - baseline += n - orderlists[region] = ol - if showlines: - shadows = {} - for k, v in bottoms.iteritems(): - shadows[k] = 0 - segiter = [orderlists[x] for x in regions[1:]] - lN = len(orderlists[regions[0]]) - for i in range(0, lN): - if lN-i < freqTh: - cc = lN-i-1 - else: - cc = 5 - seg_start = orderlists[regions[0]][i] - for seg_comb in itertools.product(*segiter): - seg_comb = [seg_start]+list(seg_comb) - seg = '|'.join(seg_comb) - if seg in lineWidths: - for j in range(0, lineWidths[seg]): - x, y, colc = [], [], 0.0 - for seg_part in seg_comb: - colc += 1 - x.append(colc-0.1) - x.append(colc+0.1) - y.append(bottoms[seg_part]+shadows[seg_part]+0.5) - y.append(bottoms[seg_part]+shadows[seg_part]+0.5) - shadows[seg_part]+=1 - ax.plot(x, y, color=palette[cc], alpha=.7, lw=3, zorder=-1) - ic = 0 - for region in regions: - ic += 1 - baseline, cc = 0, 0 - for m, n in segFreqs[region].most_common()[::-1]: - if n > freqTh: - ax.text(ic-0.1, baseline+n, m, color='black') - ax.bar(ic, n, width=.2, bottom=baseline, color='w', edgecolor='grey', alpha=.4) - baseline += n - cc += 1 - ax.set_ylabel(ylabel) - ax.set_xticklabels(['']+regionlabels, rotation='vertical') - ax.set_title(title) - return ax, fig - -def plotSeqLogo(sequences, palette='aa_chemistry', title='', xlabel='Amino acid position', ax=None, y_ln=30): - colors = {'aa_chemistry': {'G': 'green', 'S': 'green', 'T': 'green', 'Y': 'green', 'C': 'green', 'K': 'blue', 'R': 'blue', 'H': 'blue', 'Q': 'magenta', 'N': 'magenta', 'D': 'red', 'E': 'red', 'A': 'black', 'V': 'black', 'L': 'black', 'I': 'black', 'P': 'black', 'W': 'black', 'F': 'black', 'M': 'black'}} - colors = colors[palette] - #hydrophobic 'black'; acidic 'red'; basic 'blue'; neutral 'magenta'; polar 'green' - if ax == None: - fig = plt.figure() - ax = fig.add_subplot(111) - else: - fig = ax.get_figure() - letter_freq, axis_length = [], 0 - for seq in sequences: - if len(seq) > axis_length: - axis_length = len(seq) - for i in range(0, axis_length): - letter_freq.append(collections.Counter()) - for seq in sequences: - for i in range(0, len(seq)): - letter_freq[i][seq[i]] += 1 - ax.set_ylim(0, y_ln) - ax.set_xlim(0, 2*(axis_length)) - ax.set_xticks(range(1, 2*(axis_length), 2)) - ax.set_xticklabels(range(1, axis_length+1)) - ax.set_xlabel(xlabel) - for i in range(0, axis_length): - prevy = 0 - for m, n in letter_freq[i].most_common(): - try: - color = colors[m] - except: - color = yellow - ax.text((2*i)+1, prevy+1, m, color=color, fontsize=8*np.sqrt(n), horizontalalignment='center', verticalalignment='baseline') - prevy += np.sqrt(n) - ax.get_yaxis().set_visible(False) - plt.title(title) - return ax, fig - -def plotPojection(data, catorder=None, xlabel='', ylabel='', title='', alpha=0.6, marker='.', size=25, edgecolors='none', background=[[], []], backgroundcolor='grey', backgroundalpha=0.6, backgroundmarker='.', backgroundsize=15, backgroundedgecolors='none', figdim={'figsize': (3.5,2.6), 'dpi': 600}, fonthi=[8, 10, 14], ax=None, palette=discretePal): - valid_input = False - pgroup = 0 - if type(data) == dict: - valid_input = True - pgroup = -1 - if catorder == None: - catorder = list(data.keys()) - if type(data) == list: - if len(data) == 3: - valid_input = True - pgroup = 1 - if ax == None: - fig = plt.figure(**figdim) - if pgroup < 1: - ax = plt.subplot2grid((1, 4), (0, 0), colspan=3) - ax2 = plt.subplot2grid((1, 4), (0, 3), colspan=1) - else: - ax = fig.add_subplot(111) - else: - fig = ax.get_figure() - if palette == None: - palette = colget - if valid_input: - ax.scatter(background[0], background[1], color=backgroundcolor, alpha=backgroundalpha, marker=backgroundmarker, s=backgroundsize, edgecolors=backgroundedgecolors) - if pgroup < 1: - cc = 0 - for k in catorder: - v = data[k] - ax.scatter(v[0], v[1], color=palette[cc], alpha=alpha, marker=marker, s=size, edgecolors=edgecolors) - ax2.scatter(0, 0, color=palette[cc], alpha=alpha, marker=marker, s=size, edgecolors=edgecolors, label=k) - cc += 1 - ax2.set_xlim(1,2) - ax2.set_ylim(1,2) - ax2.axis('off') - ax2.legend(frameon=False, loc=2, prop={'size': fonthi[0]}) - fig.subplots_adjust(left=0.06, bottom=0.06, wspace = -0.16) - else: - x, y, z = data - ax.scatter(x, y, c=palette[z], alpha=alpha, marker=marker, s=size, edgecolors=edgecolors) - #ax.get_xaxis().set_visible(False) - #ax.get_yaxis().set_visible(False) - ax.tick_params(axis='both', which='both', left=False, right=False, bottom=False, top=False, labelbottom=False, labeltop=False, labelleft=False, labelright=False) - ax.set_xlabel('Dimension 1', fontsize=fonthi[0]) - ax.set_ylabel('Dimension 2', fontsize=fonthi[0]) - fig.suptitle(title, fontsize=fonthi[1]) - return ax, fig - -if __name__ == '__main__': - __main__() \ No newline at end of file diff --git a/scTCRpy/plotGraphs.pyc b/scTCRpy/plotGraphs.pyc deleted file mode 100644 index f3cc7db81b36ff20e66792e48923deef966c74d5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 46162 zcmeFa1zc6l`aik~6a`dJ5fibn!2nDQ#K7FBCIh|NPE5_ukLFpWj}4FXlVnS+izMJkK+=P~SRanQ}KrhX~2~IflQr@H6+! zFG+;2EX7JvNV;2=LS*5uNFj=3RHPVHB1IY?OS1I>O^UIRLTp92Oj1Z@;kJ`PvIuup zDa2kPRT_vJI7p;Pk+_Wwg99X~ZHS{}bd(6OWReKi+ew5gvr196>{cP!IE(9XC4yy> zD7!>CByy6-S)!a0<&r43L@pBLk;qk|yb|S;D8EDnBq}ITA&K0is9YJMDl8Gc?(7_? zNmN9lq7oGou((7eB=Qijq(r48DlK3ciONb;PQdaKc}nCZUoX}$`avnZvlNI z@|CEHfK?@`CQ)?(Ye-a6B0m9ZNmN^+Is(>}$X}v*0tQHgTh|w`fkX`@Y9wHwM2#hC 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Currently suports import to Python only.' - return - -class htmlElement: - def __init__(self, uid, args): - self.uid = uid - if 'eleName' in args: - self.eleName = args['eleName'] - else: - self.eleName = 'div' - if 'class' in args: - self.eclass = args['class'] - else: - self.eclass = None - if 'stored' in args: - self.stored = True - self.stemp = args['stored'] - else: - self.stored = False - if 'innerHTML' in args: - self.innerHTML = args['innerHTML'] - else: - self.innerHTML = '' - if 'id' in args: - self.imid = args['id'] - else: - self.imid = uid - if 'figure' in args: - self.fig = args['figure'] - else: - self.fig = None - if 'figargs' in args: - self.figargs = args['figargs'] - else: - self.figargs = None - if 'children' in args: - self.children = htmlFrame(prepop=args['children']) - else: - self.children = htmlFrame() - self.labels = {} - for k, v in args.iteritems(): - if k not in ['eleName', 'stored', 'innerHTML', 'children', 'figure', 'figargs', 'sidelabel']: - self.labels[k] = v - return - - def append(self, e): - self.children.append(e) - return - - def html(self, inner_only=False, figargs=[True, None, 72]): - labels = '' - if self.figargs != None: - for i in range(0, len(self.figargs)): - if self.figargs[i] != None: - figargs[i] = self.figargs[i] - for k, v in self.labels.iteritems(): - labels += ' ' + k + '="' + v + '"' - h = '<'+self.eleName+labels+'>\n' - if inner_only: - h = '' - if self.stored: - f = open(self.stemp, 'r') - t = f.read() - f.close() - os.remove(self.stemp) - else: - t = self.innerHTML - if self.fig != None: - if figargs[1] == None: - d = os.getcwd()+'/' - else: - d = figargs[1] - self.fig.savefig(d+self.imid+'.pdf') - if figargs[0]: - t += fig2hex(self.fig, dpi=figargs[2]) + '\n' - else: - t += '\n' - #self.fig.close() !close them somehow... - for child in self.children: - if isinstance(child, dict): - child = htmlElement(**child) - t += child.html(figargs=figargs) - h += t - if inner_only: - return h - else: - return h + '\n\n' - -class htmlFrame: - def __init__(self, prepop=None): - self.children = {} - self.order = [] - if prepop != None: - for a in prepop: - i = a.pop('uid', None) - self.append(htmlElement(i, a)) - return - - def __len__(self): - return len(self.order) - - def __contains__(self, key): - if key in self.order: - return True - else: - for k, v in self.children.iteritems(): - if key in v.children: - return True - else: - return False - - def __getitem__(self, key): - if key in self.children: - return self.children[key] - else: - for k, v in self.children.iteritems(): - if key in v.children: - return self.children[k].children[key] - - def __iter__(self): - for e in self.order: - yield self.children[e] - - def append(self, e): - self.order.append(e.uid) - self.children[e.uid] = e - return - -class htmlReporter: - def __init__(self, fn, template=None, embedpics=True, savepics=True, saveres=200): - self.fn = fn - self.embedpics = embedpics - self.saveres = saveres - if savepics: - self.imgDir = os.path.dirname(fn)+'/img/' - if not os.path.exists(self.imgDir): - os.makedirs(self.imgDir) - if template == None: - self.head, self.feet = '\n\n\n', '\n' - else: - f = open(template, 'r') - t = f.read() - self.head, self.feet = t.split('***CONTENT***') - f.close() - self.members = htmlFrame() - self.N = 0 - return - - def append(self, name, div={}, tempstore=False, parent=None, sidelabel=None): - self.N += 1 - element = htmlElement(name, div) - if tempstore: - divname = self.fn+'_tmp_'+str(self.N)+'_div' - f = open(divname, 'w') - f.write(element.html(inner_only=True, figargs=[self.embedpics, self.imgDir, self.saveres])) - f.close() - element.stored = True - element.stemp = divname - element.innerHTML = '' - if parent == None: - parent = self.members - else: - if parent in self.members: - parent = self.members[parent] - else: - parent = self.members - parent.append(element) - if sidelabel != None: - sidebar, itemname, baritem = sidelabel - if sidebar in self.members: - self.members[sidebar].append(htmlElement(itemname, baritem)) - return - - def add(self, name, div={}, **kwargs): - if 'div' not in kwargs: - kwargs['div'] = div - if isinstance(name, list): - for en, ek in name: - self.append(en, **ek) - else: - self.append(name, **kwargs) - return - - def report(self): - o = self.head + '\n' - for e in self.members: - o += e.html(figargs=[self.embedpics, self.imgDir, self.saveres]) + '\n' - o += self.feet - f = open(self.fn, 'w') - f.write(o) - f.close() - return - -if __name__ == '__main__': - __main__() \ No newline at end of file diff --git a/scTCRpy/reportHTML.pyc b/scTCRpy/reportHTML.pyc deleted file mode 100644 index a5e068c2f4f39d6a9a2b2d7917dc3b7c8f238436..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7129 zcmcIp&vP6{74F_$t+cD3vEnEj?0CybU}K0BLWnD2tvHsPxB^+i$i&2DGpu(;(n$MD z(<57|l8S>|1geq?XZQzlhBH)7{0ZDBPTaWg2f+7zJ^EpXlMwRGo9>=BZ(hHC?|ZM? 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Currently suports import to Python only.' - return - -class clonoTable(): - def __init__(self, experiment): - samples, clonogroups, tcrdata, order = experiment.cellGroups['samples'], experiment.cellGroups['major clonotypes'], experiment.cellTCR, experiment.sampleOrder - table, bar, xticks = [], [], [] - if order == None: - order = list(samples.keys()) - condlist = [] - for c in order: - condlist.append('Cells in '+c) - bar.append([]) - clonodat = [] - for k, v in clonogroups.iteritems(): - clonodat.append([k, len(v), v]) - clonodat.sort(key=lambda x: int(x[0].split('_')[1])) - clonodat = clonodat[:20] - mCl = 0 - for ct, size, cells in clonodat: - xticks.append(ct) - cells = set(cells) - tabrow = [ct] - cdr = tcrdata.clonotypeTable[tcrdata.clonaliasTable[1][ct]]['cdrSym'] - cdr = cdr.split('|') - tabrow += cdr - tabrow.append(str(size)) - sasi = [] - for i in range(0, len(order)): - s = order[i] - scells = set(samples[s])&cells - numcell = len(scells) - if numcell > mCl: - mCl = numcell - sasi.append(numcell) - bar[i].append(numcell) - tabrow += [str(x) for x in sasi] - tabrow.append(plotGraphs.divBar(sasi, mCl)) - table.append(tabrow) - self.table = table - self.bar = bar - self.order = order - self.xticks = xticks - self.headers = ['Clonotype', 'Dominant TRA CDR3', 'Dominant TRB CDR3', 'Secondary TRA CDR3', 'Secondary TRB CDR3', 'Number of cells'] + condlist + ['
'] - self.ax, self.fig = plotGraphs.plotGrBar(self.bar, self.order, xticks=self.xticks, ylabel='Number of cells', figdim={'figsize': (7.2,4.5), 'dpi': 600}) - return - - def html(self, divname='clonotype_table'): - div = { - 'class': 'toggle_tab', - 'id': divname, - 'children': [ - { - 'class': 'widefig', - 'uid': divname, - 'figure': self.fig, - 'figargs': [None, None, 300], - 'innerHTML': '' - }, - { - 'eleName': 'table', - 'id': 'global_clonotype_table', - 'uid': divname+'_1', - 'innerHTML': '' + ''.join(self.headers) + '\n' + '\n'.join(['' + ''.join(row) + '' for row in self.table]) + '\n' - } - ] - } - argdic = { - 'name': divname, - 'div': div, - 'parent': 'content', - 'tempstore': True, - 'sidelabel': [ - 'side_bar', 'button_'+divname, - { - 'innerHTML': 'Clonotype frequency', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, '"+divname+"')" - } - ] - } - return argdic - -class clonOverlap: - def __init__(self, experiment): - samples, tcrdata, order = experiment.cellGroups['samples'], experiment.cellTCR, experiment.sampleOrder - table = [] - figures = {} - if order == None: - order = list(samples.keys()) - selections = {'x_value': order[:], 'y_value': order[:]} - self.selorder = ['x_value', 'y_value'] - for s1 in order: - for s2 in order: - if s1 != s2: - overlaps = {} - c1 = set(samples[s1]) - c2 = set(samples[s2]) - for k, v in tcrdata.clonotypeTable.iteritems(): - if v['cdrSym'] != '|||': - cells = set(v['cells']) - l1 = len(cells&c1) - l2 = len(cells&c2) - lapkey = str(l1)+'_'+str(l2) - if lapkey not in ['1_0', '0_1']: - if lapkey not in overlaps: - overlaps[lapkey] = 0 - overlaps[lapkey] += 1 - x, y, z = [], [], [] - for k, v in overlaps.iteritems(): - l1, l2 = k.split('_') - x.append(int(l1)) - y.append(int(l2)) - z.append(v) - table.append([s1, s2, x, y, z]) - ax, fig = plotGraphs.plotPairBubble(s1, s2, x, y, z) - figures[s1+'_'+s2] = fig - self.table = table - self.figures = figures - self.selections = selections - return - - def html(self, divname='clone_overlap'): - selectors = '' - for ki in range(0, len(self.selorder)): - k = self.selorder[ki] - v = self.selections[k] - selectors += '
'+k+'
' - div = { - 'id': divname, - 'class': 'toggle_tab', - - 'children': [ - { - 'class': 'chooser', - 'id': divname+'_chooser', - 'uid': divname+'_chooser', - 'innerHTML': selectors - } - ] - } - for k, v in self.figures.iteritems(): - fd = { - 'class': 'medfig', - 'id': divname+'_'+k, - 'uid': divname+k, - 'figure': v, - 'innerHTML': '' - } - div['children'].append (fd) - argdic = { - 'name': divname, - 'div': div, - 'parent': 'content', - 'tempstore': True, - 'sidelabel': [ - 'side_bar', 'button_'+divname, - { - 'innerHTML': 'Clonotype overlaps\nbetween samples', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, '"+divname+"')" - } - ] - } - return argdic - -class exprTVio: - def __init__(self, experiment): - samples, cells, order = experiment.cellGroups['samples'], experiment.cellTCR.cellTable, experiment.sampleOrder - table = {'Sample': []} - if order == None: - order = list(samples.keys()) - collabels = ['Dominant alpha', 'Secondary alpha', 'Dominant beta', 'Secondary beta'] - for l in collabels: - table[l] = [] - sd = {} - for k, v in samples.iteritems(): - for c in v: - sd[c] = k - for c, d in cells.iteritems(): - chains = d['chain_epression'] - table['Sample'].append(sd[c]) - table['Dominant alpha'].append(chains['TRA']) - table['Dominant beta'].append(chains['TRB']) - table['Secondary alpha'].append(chains['_TRA']) - table['Secondary beta'].append(chains['_TRB']) - self.table=table - self.collabels = collabels - self.order = order - self.ax, self.fig = plotGraphs.plotDistribution(self.table, 'Expression of TCR chains', 'No of reads', hue='Sample', order=self.collabels, huerder=self.order, show_strip=True) - return - - def html(self, divname='expr_tcr'): - div = { - 'class': 'toggle_sub', - 'children': [ - { - 'class': 'smallfig', - 'uid': divname, - 'figure': self.fig, - 'innerHTML': '' - } - ] - } - argdic = { - 'name': divname, - 'div': div, - 'parent': 'tcr_stats', - } - return argdic - -class lenCdrIns: - def __init__(self, experiment): - samples, cells, clonotypes, consenses, order = experiment.cellGroups['samples'], experiment.cellTCR.cellTable, experiment.cellTCR.clonotypeTable, experiment.cellTCR.consensusTable, experiment.sampleOrder - table = {'Sample': []} - if order == None: - order = list(samples.keys()) - collabels = ['Dominant alpha', 'Secondary alpha', 'Dominant beta', 'Secondary beta'] - for l in collabels: - table[l] = [] - sd = {} - for k, v in samples.iteritems(): - for c in v: - sd[c] = k - for c, d in cells.iteritems(): - chains = clonotypes[d['clonotype']]['chains'] - table['Sample'].append(sd[c]) - da, db, sa, sb = 0, 0, 0, 0 - try: - da = consenses[chains['TRA']['primary_consensus']]['addednuc'] - except: - pass - try: - db = consenses[chains['TRB']['primary_consensus']]['addednuc'] - except: - pass - try: - sa = consenses[chains['TRA']['secondary_consensus']]['addednuc'] - except: - pass - try: - sb = consenses[chains['TRB']['secondary_consensus']]['addednuc'] - except: - pass - table['Dominant alpha'].append(da) - table['Dominant beta'].append(db) - table['Secondary alpha'].append(sa) - table['Secondary beta'].append(sb) - self.table = table - self.collabels = collabels - self.order = order - self.ax, self.fig = plotGraphs.plotDistribution(self.table, 'Length of CDR3 region', 'Inserted nt', hue='Sample', order=self.collabels, huerder=self.order, show_strip=True) - return - - def html(self, divname='adn_tcr'): - div = { - 'class': 'toggle_sub', - 'children': [ - { - 'class': 'smallfig', - 'uid': divname, - 'figure': self.fig, - 'innerHTML': '' - } - ] - } - argdic = { - 'name': divname, - 'div': div, - 'parent': 'tcr_stats', - } - return argdic - -class lenCdrAa: - def __init__(self, experiment): - samples, cells, clonotypes, order = experiment.cellGroups['samples'], experiment.cellTCR.cellTable, experiment.cellTCR.clonotypeTable, experiment.sampleOrder - table = {'Sample': []} - if order == None: - order = list(samples.keys()) - collabels = ['Dominant alpha', 'Secondary alpha', 'Dominant beta', 'Secondary beta'] - for l in collabels: - table[l] = [] - sd = {} - for k, v in samples.iteritems(): - for c in v: - sd[c] = k - for c, d in cells.iteritems(): - chains = clonotypes[d['clonotype']]['chains'] - table['Sample'].append(sd[c]) - table['Dominant alpha'].append(len(chains['TRA']['primary_cdr3_aa'])) - table['Dominant beta'].append(len(chains['TRB']['primary_cdr3_aa'])) - table['Secondary alpha'].append(len(chains['TRA']['secondary_cdr3_aa'])) - table['Secondary beta'].append(len(chains['TRB']['secondary_cdr3_aa'])) - self.table = table - self.collabels = collabels - self.order = order - self.ax, self.fig = plotGraphs.plotDistribution(self.table, 'Length of CDR3 region', 'Length (aa)', hue='Sample', order=self.collabels, huerder=self.order, show_strip=True) - return - - def html(self, divname='len_tcr'): - div = { - 'class': 'toggle_sub', - 'children': [ - { - 'class': 'smallfig', - 'uid': divname, - 'figure': self.fig, - 'innerHTML': '' - } - ] - } - argdic = { - 'name': divname, - 'div': div, - 'parent': 'tcr_stats', - } - return argdic - -class lenCdrNt: - def __init__(self, experiment): - samples, cells, clonotypes, order = experiment.cellGroups['samples'], experiment.cellTCR.cellTable, experiment.cellTCR.clonotypeTable, experiment.sampleOrder - table = {'Sample': []} - if order == None: - order = list(samples.keys()) - collabels = ['Dominant alpha', 'Secondary alpha', 'Dominant beta', 'Secondary beta'] - for l in collabels: - table[l] = [] - sd = {} - for k, v in samples.iteritems(): - for c in v: - sd[c] = k - for c, d in cells.iteritems(): - chains = clonotypes[d['clonotype']]['chains'] - table['Sample'].append(sd[c]) - table['Dominant alpha'].append(len(chains['TRA']['primary_cdr3_nt'])) - table['Dominant beta'].append(len(chains['TRB']['primary_cdr3_nt'])) - table['Secondary alpha'].append(len(chains['TRA']['secondary_cdr3_nt'])) - table['Secondary beta'].append(len(chains['TRB']['secondary_cdr3_nt'])) - self.table = table - self.collabels = collabels - self.order = order - self.ax, self.fig = plotGraphs.plotDistribution(self.table, 'Length of CDR3 region', 'Length (nt)', hue='Sample', order=self.collabels, huerder=self.order, show_strip=True) - return - - def html(self, divname='lent_tcr'): - div = { - 'class': 'toggle_sub', - 'children': [ - { - 'class': 'smallfig', - 'uid': divname, - 'figure': self.fig, - 'innerHTML': '' - } - ] - } - argdic = { - 'name': divname, - 'div': div, - 'parent': 'tcr_stats', - } - return argdic - -class orphanBar: - def __init__(self, experiment): - samples, chains, order = experiment.cellGroups['samples'], experiment.cellGroups['chain pairing'], experiment.sampleOrder - xticks = ['Single pair', 'Double alpha', 'Double beta', 'Full doublet', 'Orphan alpha', 'Orphan beta', 'Double orphan alpha', 'Double orphan beta'] - table, normtab, rowsums= [], [], [] - if order == None: - order = list(samples.keys()) - if xticks == None: - xticks = list(set(chains.keys())) - xticks.sort() - for sample in order: - row, rowsum = [], 0 - cells = set(samples[sample]) - for k in xticks: - v = chains[k] - val = len(set(v)&cells) - row.append(val) - rowsum += val - table.append(row) - rowsums.append(rowsum) - for i in range(0, len(table)): - r, s = [], rowsums[i] - for a in table[i]: - r.append(float(a)/s) - normtab.append(r) - self.order = order - self.table = table - self.normtab = normtab - xticks[6] = '2xO alpha' - xticks[7] = '2xO beta' - self.xticks = xticks - self.barax1, self.barfig1 = plotGraphs.plotHorGrStBar(self.table, self.order, xticks=self.xticks, ylabel='Number of T cells') - self.barax2, self.barfig2 = plotGraphs.plotHorGrStBar(self.normtab, self.order, xticks=self.xticks, ylabel='Percent of T cells', ydim='%') - #self.barax1, self.barfig1 = plotGraphs.plotHorGrStBar(self.table, self.order, xticks=self.xticks, ylabel='Number of T cells', hubysec=True) - #self.barax2, self.barfig2 = plotGraphs.plotHorGrStBar(self.normtab, self.order, xticks=self.xticks, ylabel='Percent of T cells', ydim='%', hubysec=True) - return - - def html(self, divname='orphan_tcr'): - div = { - 'class': 'toggle_sub', - 'children': [ - { - 'class': 'smallfig', - 'uid': divname+'_1', - 'figure': self.barfig1, - 'innerHTML': '' - }, - { - 'class': 'smallfig', - 'uid': divname+'_2', - 'figure': self.barfig2, - 'innerHTML': '' - } - ] - } - argdic = { - 'name': divname, - 'div': div, - 'parent': 'tcr_stats' - } - return argdic - -class diverBar: - def __init__(self, experiment): - samples, groups, subgroup, celltypes, order = experiment.cellGroups['samples'], experiment.divGroup['SAMPLESPLIT'], 'clusters in '+experiment.maniFolds[0].label, experiment.maniFolds[0].annotations, experiment.sampleOrder - majorclone = [] - for k, v in experiment.cellGroups['major clonotypes'].iteritems(): - majorclone += v - majorclone = set(majorclone) - ttable, table, xticks = [], [], [] - if order == None: - order = list(groups.keys()) - for sample in order: - dat = groups[sample][subgroup] - xticks += dat.keys() - table.append([]) - ttable.append([]) - xticks = list(set(xticks)) - xticks.sort() - for i in range(0, len(order)): - sample = groups[order[i]][subgroup] - for j in range(0, len(xticks)): - if xticks[j] in sample: - val = sample[xticks[j]] - else: - val = 0 - try: - val = float(val) - except: - val = 0 - table[i].append(val) - cells = set(samples[order[i]]) - for tick in xticks: - val = 0 - for cell in majorclone: - if cell in cells: - if cell in celltypes: - if celltypes[cell] == tick: - val += 1 - ttable[i].append(val) - self.order = order - self.table = table - self.ttable = ttable - self.xticks = xticks - self.ax1, self.fig1 = plotGraphs.plotGrDivBar(self.ttable, self.order, xticks=self.xticks, ylabel='Number of clonal cells') - self.ax2, self.fig2 = plotGraphs.plotGrDivBar(self.table, self.order, xticks=self.xticks, ylabel='Shannon diversity score') - return - - def html(self, divname='diversity_bar'): - div = { - 'id': divname, - 'class': 'toggle_tab', - - 'children': [ - { - 'class': 'widefig', - 'uid': divname+'_1', - 'figure': self.fig2, - 'figargs': [None, None, 300], - 'innerHTML': '' - }, - { - 'class': 'widefig', - 'uid': divname+'_2', - 'figure': self.fig1, - 'figargs': [None, None, 300], - 'innerHTML': '' - } - ] - } - argdic = { - 'name': divname, - 'div': div, - 'parent': 'content', - 'tempstore': True, - 'sidelabel': [ - 'side_bar', 'button_'+divname, - { - 'innerHTML': 'Diversity of samples', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, '"+divname+"')" - } - ] - } - return argdic - -class segmentBar: - def __init__(self, experiment): - samples, gdir, order = experiment.cellGroups['samples'], experiment.cellGroups, experiment.sampleOrder - labels = ['TRA V segment usage', 'TRB V segment usage', 'TRA D segment usage', 'TRB D segment usage', 'TRA J segment usage', 'TRB J segment usage'] - titles = [x.replace(' segment usage', '').replace(' ', '') for x in labels] - self.figures = {} - for i in range(0, len(labels)): - table = [] - label, title = titles[i], labels[i] - f = [] - for k, v in gdir[title].iteritems(): - f.append([k, len(v)]) - f.sort(key=lambda x: x[1], reverse=True) - for sample in order: - cells = set(samples[sample]) - row, xticks = [], [] - for a, b in f[:8]: - v = gdir[title][a] - val = len(set(v)&cells) - xticks.append(a+' ('+str(b)+')') - row.append(val) - val = 0 - nval = 0 - for a, b in f[8:]: - v = gdir[title][a] - val += len(set(v)&cells) - nval += b - if nval > 0: - xticks.append('Other ('+str(nval)+')') - row.append(val) - table.append(row) - ax, fig = plotGraphs.plotHorGrStBar(table, order, xticks=xticks, ylabel='Frequency of segment', title=title) - self.figures[label] = fig - self.order = order - self.titles = titles - return - - def html(self, divname='segment_bar'): - div = { - 'id': divname, - 'class': 'toggle_tab', - 'children': [] - } - for k in self.titles: - v = self.figures[k] - fd = { - 'class': 'smallfig', - 'uid': k, - 'figure': v, - 'innerHTML': '' - } - div['children'].append (fd) - argdic = { - 'name': divname, - 'div': div, - 'parent': 'content', - 'tempstore': True, - 'sidelabel': [ - 'side_bar', 'button_'+divname, - { - 'innerHTML': 'VDJ segment usage', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, '"+divname+"')" - } - ] - } - return argdic - - -class clonTopology: - def __init__(self, experiment, ctypeorder=None): - samples, clonogroup, tcrdat, manifolds, order = experiment.cellGroups['samples'], experiment.cellGroups['major clonotypes'], experiment.cellTCR, experiment.maniFolds, experiment.sampleOrder - table, figures = {}, {} - selections = {'manifold': [], 'sample': ['all']} - self.selorder = ['manifold', 'sample'] - self.fprefix = 'clone_map_' - if order == None: - order = list(samples.keys()) - clonok = list(clonogroup.keys()) - clonok.sort(key=lambda x: int(x.split('_')[1])) - clonok = clonok[:12] - for i, n, manifold in manifolds: - selections['manifold'].append(n) - coords = manifold.coordinates - annot = manifold.annotations - background = manifold.background - celltypes = [] - bigcl, bigct, bigtc, bigmc = {}, {}, {}, {} - for sample in order: - sampletab = [{}, {}, {sample: [[], []]}] - sabi = [[], []] - if sample not in selections['sample']: - selections['sample'].append(sample) - cells = set(samples[sample]) - for k in clonok: - if k not in bigcl: - bigcl[k] = [[], []] - bcd = bigcl[k] - c_cells = set(clonogroup[k])&cells - xl, yl = [], [] - for c in c_cells: - if c in coords: - x, y = coords[c] - xl.append(x) - yl.append(y) - sabi[0].append(x) - sabi[1].append(y) - bcd[0].append(x) - bcd[1].append(y) - sampletab[0][k] = [xl, yl] - for k, v in annot.iteritems(): - v = str(v) - if v not in sampletab[1]: - sampletab[1][v] = [[], []] - if v not in bigct: - bigct[v] = [[], []] - celltypes.append(v) - if k in cells: - cttab = sampletab[1][v] - bctab = bigct[v] - x, y = coords[k] - cttab[0].append(x) - cttab[1].append(y) - bctab[0].append(x) - bctab[1].append(y) - for c in cells: - if c in tcrdat.cellTable: - ct = tcrdat.cellTable[c]['clonotype'] - if tcrdat.clonotypeTable[ct]['cdrSym'] != '|||': - if c in coords: - x, y = coords[c] - sampletab[2][sample][0].append(x) - sampletab[2][sample][1].append(y) - bigtc.update(sampletab[2]) - sabi = {sample: sabi} - bigmc.update(sabi) - table[self.fprefix+n+'_'+sample] = sampletab - table[self.fprefix+n+'_all'] = [bigcl, bigct, bigtc] - celltypes = list(set(celltypes)) - celltypes.sort() - if ctypeorder == None: - finalCTorer = celltypes - else: - finalCTorer = ctypeorder - ax4, fig4 = plotGraphs.plotPojection(bigmc, catorder=order, title='Projection of clonal T cells on '+n, background=background) - for sample in order: - clontab, atab, ttab = table[self.fprefix+n+'_'+sample] - ax1, fig1 = plotGraphs.plotPojection(clontab, catorder=clonok, title='Projection of clonotypes in '+sample+' on '+n, background=background) - ax2, fig2 = plotGraphs.plotPojection(ttab, title='Projection of all T cells in '+sample+' on '+n, background=background) - ax3, fig3 = plotGraphs.plotPojection(atab, catorder=finalCTorer, title='Projection of cell types in '+sample+' on '+n, background=background) - figures[self.fprefix+n+'_'+sample] = [fig1, fig2, fig3, fig4] - clontab, atab, ttab = table[self.fprefix+n+'_all'] - ax1, fig1 = plotGraphs.plotPojection(clontab, catorder=clonok, title='Projection of all cells in major clonotypes on '+n, background=background) - ax2, fig2 = plotGraphs.plotPojection(ttab, catorder=order, title='Projection of all T cells in of all samples on '+n, background=background) - ax3, fig3 = plotGraphs.plotPojection(atab, catorder=finalCTorer, title='Projection of cell types in all samples on '+n, background=background) - figures[self.fprefix+n+'_all'] = [fig1, fig2, fig3, fig4] - self.order = order - self.selections = selections - self.figures = figures - return - - def html(self, divname='clone_map'): - selectors = '' - for k in self.selorder: - v = self.selections[k] - selectors += '
'+k+'
' - div = { - 'id': divname, - 'class': 'toggle_tab', - - 'children': [ - { - 'class': 'chooser', - 'id': divname+'_chooser', - 'uid': divname+'_chooser', - 'innerHTML': selectors + '
' - } - ] - } - for k, v in self.figures.iteritems(): - fd = { - 'id': k, - 'uid': k, - 'class': 'medfig', - 'children': [] - } - for i in range(0, len(v)): - cfd = { - 'class': 'smallfig', - 'uid': k+'_'+str(i), - 'figure': v[i], - 'figargs': [None, None, 300], - 'innerHTML': '' - } - fd['children'].append (cfd) - div['children'].append (fd) - argdic = { - 'name': divname, - 'div': div, - 'parent': 'content', - 'tempstore': True, - 'sidelabel': [ - 'side_bar', 'button_'+divname, - { - 'innerHTML': 'Cell projections', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, '"+divname+"')" - } - ] - } - return argdic - -class clonoDist: - def __init__(self, experiment, celltypeorder=None): - samples, celltypes, distances, order = experiment.cellGroups['samples'], experiment.maniFolds[0], experiment.cellDistances, experiment.sampleOrder - self.table, self.selections, self.figures = [], [], {} - cellabels, tlabels = {}, {} - if celltypeorder == None: - xticks = list(celltypes.cellTypes.keys()) - xticks.sort() - else: - xticks = celltypeorder - for i in range(0, len(xticks)): - tlabels[xticks[i]] = i - for i in range(0, len(order)): - sample = order[i] - cells = set(samples[sample]) - for cell in cells: - if cell in celltypes.annotations: - ct = tlabels[celltypes.annotations[cell]] - else: - ct = len(xticks) - cellabels[cell] = [i, ct, 'lightgray'] - collabels = [['Sample', order], ['Cell type', xticks]] - for i, n, distance in distances: - for cell, dat in cellabels.iteritems(): - dat[2] = 'lightgray' - gd = distance.treeGroups - go = list(gd.keys()) - go.sort(key=lambda x: int(x.split('Group ')[1])) - for cell, dat in cellabels.iteritems(): - for j in range(0, len(go)): - if cell in gd[go[j]]: - dat[2] = j - ncollabels = collabels + [['Cluster', go]] - self.selections.append(n) - self.figures['cell_distances_'+n] = [ - plotGraphs.plotCellTree(order, distance, cellabels, ncollabels, title='Clustering of clonotypes based on CDR3 sequence similarity ('+n+')'), - plotGraphs.plotSimpleHeat(distance) - ] - self.table.append([i, n, distance, cellabels]) - return - - def html(self, divname='cell_distances'): - selectors = '
Property
' - div = { - 'id': divname, - 'class': 'toggle_tab', - 'children': [ - { - 'class': 'chooser', - 'id': divname+'_chooser', - 'uid': divname+'_chooser', - 'innerHTML': selectors + '
' - } - ] - } - for k, v in self.figures.iteritems(): - fd = { - 'id': k, - 'uid': k, - 'class': 'medfig', - 'children': [] - } - for i in range(0, len(v)): - cfd = { - 'class': 'widefig', - 'uid': k+'_'+str(i), - 'figure': v[i], - 'figargs': [None, None, 300], - 'innerHTML': '' - } - fd['children'].append (cfd) - div['children'].append (fd) - argdic = { - 'name': divname, - 'div': div, - 'parent': 'content', - 'tempstore': True, - 'sidelabel': [ - 'side_bar', 'button_'+divname, - { - 'innerHTML': 'TCR based cell clusters', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, '"+divname+"')" - } - ] - } - return argdic - -class cellTypeDist: - def __init__(self, experiment): - samples, celltypes, distances, order = experiment.cellGroups['samples'], experiment.maniFolds[0], experiment.cellDistances, experiment.sampleOrder - self.table, self.selections, self.figures = [], [], {} - cellabels, tlabels = {}, {} - groupcolors = {} - for i in range(0, len(order)): - groupcolors[order[i]] = i - for i, n, distance in distances: - annot = celltypes.annotations - groups = {} - for sample in order: - cells = set(samples[sample]) - for k, v in annot.iteritems(): - if k in cells: - v = sample + ', ' + str(v) - if v not in groups: - groups[v] = [] - groups[v].append(k) - self.selections.append(n) - self.figures['type_distances_'+n] = [plotGraphs.plotGrTree(distance, groups, groupcolors, title='Sequence similarity of TCRs in individual cell types')] - return - - def html(self, divname='type_distances'): - selectors = '
Property
' - div = { - 'id': divname, - 'class': 'toggle_tab', - 'children': [ - { - 'class': 'chooser', - 'id': divname+'_chooser', - 'uid': divname+'_chooser', - 'innerHTML': selectors + '
' - } - ] - } - for k, v in self.figures.iteritems(): - fd = { - 'id': k, - 'uid': k, - 'class': 'medfig', - 'children': [] - } - for i in range(0, len(v)): - cfd = { - 'class': 'widefig', - 'uid': k+'_'+str(i), - 'figure': v[i], - 'figargs': [None, None, 300], - 'innerHTML': '' - } - fd['children'].append (cfd) - div['children'].append (fd) - argdic = { - 'name': divname, - 'div': div, - 'parent': 'content', - 'tempstore': True, - 'sidelabel': [ - 'side_bar', 'button_'+divname, - { - 'innerHTML': 'Similarity of TCRs across cell types', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, '"+divname+"')" - } - ] - } - return argdic - -class clonoGex: - def __init__(self, experiment, genes=None, cellTypeOrder=None): - samples, groups, manifolds, gex, order = experiment.cellGroups['samples'], experiment.cellGroups['clusters in seqSim'], experiment.maniFolds, experiment.cellGex.gex, experiment.sampleOrder - self.order = order - self.table = [] - self.figures = {} - self.fprefix = 'gex_levels_' - if order == None: - order = list(samples.keys()) - if genes == None: - self.genes = gex.genes - collabels = [] - celltypes = [] - for gene in self.genes: - table1, table2 = {'Sample': [], 'Group': [], 'No of reads': []}, {'Sample': [], 'Group': [], 'No of reads': []} - for sample in order: - scells = set(samples[sample]) - for group, gcells in groups.iteritems(): - cells = scells&set(gcells) - for cell in cells: - reads = gex[cell][gene] - try: - reads = gex[cell][gene] - table1['Sample'].append(sample) - table1['Group'].append(group) - table1['No of reads'].append(reads) - except: - pass - collabels.append(group) - m = manifolds[0] - for c, d in m.annotations.iteritems(): - if c in scells: - try: - reads = gex[c][gene] - table2['Sample'].append(sample) - table2['Group'].append(d) - table2['No of reads'].append(reads) - except: - pass - celltypes.append(d) - self.table.append([gene, table1, table2]) - celltypes = list(set(celltypes)) - celltypes.sort() - collabels = list(set(collabels)) - collabels.sort(key=lambda x: int(x.split('Group ')[1])) - if cellTypeOrder == None: - self.celltypeorder = celltypes - else: - self.celltypeorder = cellTypeOrder - self.collabels = collabels - for gene, table1, table2 in self.table: - ax2, fig2 = plotGraphs.plotGexDistribution(table2, 'Group', 'No of reads', hue='Sample', title='Expression of ' + gene, order=self.celltypeorder, huerder=order, condensed=False, show_swarm=False, show_box=False, axisrotation=45) - try: - ax1, fig1 = plotGraphs.plotGexDistribution(table1, 'Group', 'No of reads', hue='Sample', title='Expression of ' + gene, order=self.collabels, huerder=order, condensed=False, show_swarm=False, show_box=False, axisrotation=45) - except: - ax1, fig1 = plotGraphs.emptyFig() - try: - ax2, fig2 = plotGraphs.plotGexDistribution(table2, 'Group', 'No of reads', hue='Sample', title='Expression of ' + gene, order=self.celltypeorder, huerder=order, condensed=False, show_swarm=False, show_box=False, axisrotation=45) - except: - ax2, fig2 = plotGraphs.emptyFig() - #ax.set_ylim(bottom=0) - self.figures[self.fprefix+gene] = [fig1, fig2] - return - - def html(self, divname='gex_levels'): - selectors = '
gene
' - div = { - 'id': divname, - 'class': 'toggle_tab', - - 'children': [ - { - 'class': 'chooser', - 'id': divname+'_chooser', - 'uid': divname+'_chooser', - 'innerHTML': selectors + '
' - } - ] - } - for k, v in self.figures.iteritems(): - fd = { - 'id': k, - 'uid': k, - 'class': 'medfig', - 'children': [] - } - for i in range(0, len(v)): - cfd = { - 'class': 'widefig', - 'uid': k+'_'+str(i), - 'figure': v[i], - 'figargs': [None, None, 300], - 'innerHTML': '' - } - fd['children'].append (cfd) - div['children'].append (fd) - argdic = { - 'name': divname, - 'div': div, - 'parent': 'content', - 'tempstore': True, - 'sidelabel': [ - 'side_bar', 'button_'+divname, - { - 'innerHTML': 'Expression of top genes', - 'class': 'menu_item', - 'onclick': "toggle_visibility(this, '"+divname+"')" - } - ] - } - return argdic - -if __name__ == '__main__': - __main__() \ No newline at end of file diff --git a/scTCRpy/resultData.pyc b/scTCRpy/resultData.pyc deleted file mode 100644 index 6178c15ac94c88540af8c3022c36dace30131bb2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 32413 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a/scTCRpy/rpackages/DiversitySeq/CITATION +++ /dev/null @@ -1,11 +0,0 @@ -citEntry( - entry="article", - title = "Measuring the diversity of the human microbiota with targeted next-generation sequencing", - author = "Francesca Finotello and Eleonora Mastrorilli and Barbara Di Camillo", - journal="Briefings in Bioinformatics", - volume="19", - number="4", - pages="679-692", - year="2018", - textVersion = "Measuring the diversity of the human microbiota with targeted next-generation sequencing. Briefings in Bioinformatics 19 (4), 679-692, 2018." -) \ No newline at end of file diff --git a/scTCRpy/rpackages/DiversitySeq/DESCRIPTION b/scTCRpy/rpackages/DiversitySeq/DESCRIPTION deleted file mode 100644 index f4e05a381..000000000 --- a/scTCRpy/rpackages/DiversitySeq/DESCRIPTION +++ /dev/null @@ -1,13 +0,0 @@ -Package: DiversitySeq -Type: Package -Title: DiversitySeq: measuring diversity from count data sets -Version: 1.0 -Date: 2016-10-18 -Author: Francesca Finotello, Eleonora Mastrorilli, Barbara Di Camillo -Maintainer: Francesca Finotello -Description: DiversitySeq is a package for the analysis of diversity from count data and for the simulation of 16S ribosomal RNA (16S rRNA) gene sequencing data sets. -License: GPL-3 -Depends: R (>= 2.10), vegan -NeedsCompilation: no -Packaged: 2018-09-02 08:14:34 UTC; francescafinotello -Built: R 3.6.0; ; 2019-06-05 12:40:13 UTC; unix diff --git a/scTCRpy/rpackages/DiversitySeq/INDEX b/scTCRpy/rpackages/DiversitySeq/INDEX deleted file mode 100644 index 51b07fd1a..000000000 --- a/scTCRpy/rpackages/DiversitySeq/INDEX +++ /dev/null @@ -1,12 +0,0 @@ -DivesristySeq DiversitySeq: measuring diversity from count - data sets -aindex Compute alpha diversity from count data -bindex Compute beta diversity from count data -divplot Plot diversity computed with the DiversitySeq - package -mergedatasets Merge count data sets -salivaSimData Simulated 16S rRNA gene sequencing data from - saliva samples -simulatecounts Simulate 16S rRNA gene sequencing data -stoolSimData Simulated 16S rRNA gene sequencing data from - stool samples diff --git a/scTCRpy/rpackages/DiversitySeq/Meta/Rd.rds b/scTCRpy/rpackages/DiversitySeq/Meta/Rd.rds deleted file mode 100644 index 4905b9b9d56cd986b74d9aaefede96ad4e54e7d1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 583 zcmV-N0=WGjiwFP!000001Eo~WZ__XoH*3-_Z7_roCnTQ+99D?~(=<-g#yB7?zJ7=L6NR7WSI4JXdZLA) zeh%w1KqGxVyQE7HlB)F#ZjTERew}Gjo6&a|MCvq1tNLnIys{B8nxD7qS42UcR%@;VaIKB~_;W`yu z1;9@FFD^JMN(Go?MN01XG3H0sJOcH1>o=ct@g1vp>zv>fRVh{}V3#}gOZfC-)vbGI zznz%|CHVEp42$dOIbJ{x+UB-|Jg)Pvd}?Cb7VCo*8Q2gJe$Ox!`#WMO&f|uwHSYuQH#^NbuFf5<1Y&-go`lY&Ji-tmBNx?huWu Vxt!dHiZ4Pxe*x(7kcbNh004f&8GQf% diff --git a/scTCRpy/rpackages/DiversitySeq/Meta/data.rds b/scTCRpy/rpackages/DiversitySeq/Meta/data.rds deleted file mode 100644 index 2c9f3f80f1f68fa6138104f4ccedeadca45a47a7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 173 zcmV;e08;-SiwFP!000001B>8dU|?WoU}gi7tUx9MYiNj@t_1@FlK_w-!ob4738ZBc z%hDZ_O7l_@^O94G6*P(yb27^kgEMno5=#;_p^9YC6_w=Y=b)((F38Blpa_$Mg`56Mwy|SMGv>O1{<1i-x diff --git a/scTCRpy/rpackages/DiversitySeq/Meta/hsearch.rds b/scTCRpy/rpackages/DiversitySeq/Meta/hsearch.rds deleted file mode 100644 index 74bdc4c6366f3dac06c7b3a8733e7669034f669a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 573 zcmV-D0>b?tiwFP!000001I<)RZ`3dlb~pQKwV+A}sTU+4P=N!70$r+ds=9y>uv(EK zadnbm*CLM7*x9y!o?<7SY0v}JfG*w79qqVUe|m(29nO_lQ&Nf47g`j zn~>JF*$)ibH2Yx0=a4C`FMK#{ppN|EzOjek8G`2sULe>73F&_B1{pO{~-S%sx@ zM9;XMQ9T1%ubrkW{Klr>obP|McXdPAuY7ks5#1a5H2X9@qEkq~*qr8&gvI|`pQ5nx zg3Tt<7%;>xhPXX~vp)Q_gtkp9`5bq+Y7lpR|6P%G%RUSzdl4758ndjrZ>wK1=^K-T z33DCg2(oA2&Dt((-5*bu7|{E`g9+0!C$`>=9BPkEAcs3AT*nJRraZ)quai(lrZHuv zV&LsGxEX(INHuA2FY5|MY+SA|#Vhr#%WIh7VPlVC&eRnmRD@Y{_@O)6tj3O0Y%N|Y zvUdE|$3wxxOa@$5-KaDb)4EF?><28=ka4tsP>cm0NF@b}shtmx?UZ;@j0tptIV1_F zDoDn|SxxQ~Qy5upXY3Vj{Cm(0uKXW94%Xyj?>$_c$t+rjk>r{@WZgW+G6^Box90f) LxHB~n>ntu&m diff --git a/scTCRpy/rpackages/DiversitySeq/Meta/links.rds b/scTCRpy/rpackages/DiversitySeq/Meta/links.rds deleted file mode 100644 index f11dee73c02cce210cb79105377c4c40485380fb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 235 zcmVEvq;*X! 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DiversitySeq: measuring diversity from count data sets - -

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Documentation for package ‘DiversitySeq’ version 1.0

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Help Pages

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aindexCompute alpha diversity from count data
bindexCompute beta diversity from count data
DivesristySeqDiversitySeq: measuring diversity from count data sets
divplotPlot diversity computed with the DiversitySeq package
mergedatasetsMerge count data sets
salivaSimDataSimulated 16S rRNA gene sequencing data from saliva samples
simulatecountsSimulate 16S rRNA gene sequencing data
stoolSimDataSimulated 16S rRNA gene sequencing data from stool samples
- diff --git a/scTCRpy/rpackages/DiversitySeq/html/R.css b/scTCRpy/rpackages/DiversitySeq/html/R.css deleted file mode 100644 index f10f5ea66..000000000 --- a/scTCRpy/rpackages/DiversitySeq/html/R.css +++ /dev/null @@ -1,97 +0,0 @@ -body { - background: white; - color: black; -} - -a:link { - background: white; - color: blue; -} - -a:visited { - background: white; - color: rgb(50%, 0%, 50%); -} - -h1 { - background: white; - color: rgb(55%, 55%, 55%); - font-family: monospace; - font-size: x-large; - text-align: center; -} - -h2 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-size: large; - text-align: center; -} - -h3 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-size: large; -} - -h4 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-style: italic; - font-size: large; -} - -h5 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; -} - -h6 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-style: italic; -} - -img.toplogo { - width: 4em; - vertical-align: middle; -} - -img.arrow { - width: 30px; - height: 30px; - border: 0; -} - -span.acronym { - font-size: small; -} - -span.env { - font-family: monospace; -} - -span.file { - font-family: monospace; -} - -span.option{ - font-family: monospace; -} - -span.pkg { - font-weight: bold; -} - -span.samp{ - font-family: monospace; -} - -div.vignettes a:hover { - background: rgb(85%, 85%, 85%); -} diff --git a/scTCRpy/rpackages/Peptides/CITATION b/scTCRpy/rpackages/Peptides/CITATION deleted file mode 100644 index 140959d2d..000000000 --- a/scTCRpy/rpackages/Peptides/CITATION +++ /dev/null @@ -1,12 +0,0 @@ -citHeader("To cite Peptides in publications, please use:") -citEntry(entry = "article", - author = personList(c(person(given = "Daniel", family = "Osorio"),person(given = "Paola", family = "Rondon-Villarreal"),person(given = "Rodrigo", family = "Torres"))), - title = "Peptides: A Package for Data Mining of Antimicrobial Peptides", - journal = "The R Journal", - year = "2015", - issn = "2073-4859", - volume = "7", - number = "1", - pages = "4-14", - textVersion = "Osorio, D., Rondon-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. The R Journal. 7(1), 4-14 (2015)." -) \ No newline at end of file diff --git a/scTCRpy/rpackages/Peptides/DESCRIPTION b/scTCRpy/rpackages/Peptides/DESCRIPTION deleted file mode 100644 index 904aea824..000000000 --- a/scTCRpy/rpackages/Peptides/DESCRIPTION +++ /dev/null @@ -1,27 +0,0 @@ -Package: Peptides -Version: 2.4 -Date: 2018-06-10 -Title: Calculate Indices and Theoretical Physicochemical Properties of - Protein Sequences -Authors@R: c(person("Daniel","Osorio",email="dcosorioh@unal.edu.co",role=c("aut","cre")), - person("Paola","Rondon-Villarreal",role=c("aut","ths")), - person("Rodrigo","Torres",role=c("aut","ths")), - person("J. Sebastian","Paez",email="jpaezpae@purdue.edu",role=c("ctb")) - ) -URL: https://github.com/dosorio/Peptides/ -Suggests: testthat -Description: Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot 'XVG' output files from the 'GROMACS' molecular dynamics package. -License: GPL-2 -LinkingTo: Rcpp -Imports: Rcpp -RoxygenNote: 6.0.1 -NeedsCompilation: yes -Packaged: 2018-06-08 19:24:09 UTC; dcosorioh -Author: Daniel Osorio [aut, cre], - Paola Rondon-Villarreal [aut, ths], - Rodrigo Torres [aut, ths], - J. Sebastian Paez [ctb] -Maintainer: Daniel Osorio -Repository: CRAN -Date/Publication: 2018-06-08 19:35:33 UTC -Built: R 3.6.0; x86_64-pc-linux-gnu; 2019-06-05 12:41:08 UTC; unix diff --git a/scTCRpy/rpackages/Peptides/INDEX b/scTCRpy/rpackages/Peptides/INDEX deleted file mode 100644 index 757984188..000000000 --- a/scTCRpy/rpackages/Peptides/INDEX +++ /dev/null @@ -1,53 +0,0 @@ -AAdata Properties, scales and indices for the 20 - naturally occurring amino acids from various - sources -aIndex Compute the aliphatic index of a protein - sequence -aaComp Compute the amino acid composition of a protein - sequence -aaDescriptors Compute 66 descriptors for each amino acid of a - protein sequence. -autoCorrelation Compute the auto-correlation index of a protein - sequence -autoCovariance Compute the auto-covariance index of a protein - sequence -blosumIndices Compute the BLOSUM62 derived indices of a - protein sequence -boman Compute the Boman (Potential Protein - Interaction) index -charge Compute the theoretical net charge of a protein - sequence -crossCovariance Compute the cross-covariance index of a protein - sequence -crucianiProperties Compute the Cruciani properties of a protein - sequence -fasgaiVectors Compute the FASGAI vectors of a protein - sequence -hmoment Compute the hydrophobic moment of a protein - sequence -hydrophobicity Compute the hydrophobicity index of a protein - sequence -instaIndex Compute the instability index of a protein - sequence -kideraFactors Compute the Kidera factors of a protein - sequence -lengthpep Compute the amino acid length of a protein - sequence -membpos Compute theoretically the class of a protein - sequence -mswhimScores Compute the MS-WHIM scores of a protein - sequence -mw Compute the molecular weight of a protein - sequence -pI Compute the isoelectic point (pI) of a protein - sequence -pepdata Physicochemical properties and indices from 100 - amino acid sequences -plotXVG Plot time series from GROMACS XVG files -protFP Compute the protFP descriptors of a protein - sequence -readXVG Read output data from a XVG format file. -stScales Compute the ST-scales of a protein sequence -tScales Compute the T-scales of a protein sequence -vhseScales Compute the VHSE-scales of a protein sequence -zScales Compute the Z-scales of a protein sequence diff --git a/scTCRpy/rpackages/Peptides/Meta/Rd.rds b/scTCRpy/rpackages/Peptides/Meta/Rd.rds deleted file mode 100644 index bdbe96fc05d9df38a2d46b7501aa031d3e4c0d99..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1166 zcmV;91abQxiwFP!000001LauXa~m}fKGLK(hCpbCM`rX!3OJLd5IVfMZk>8cs3%TC zhX-b4o$S+acRD0pT=T^L%Kt&w`>{SraSvx024?zjxzkGDY9)QU`kdF@APDXRckaU9 z9rz4(UR?}(kD+xR8Xb7{;rT4s4tC+a+wX_e(8Kc(4Rn--{Ecm(1DRxvVV}~cTr(vy zBb9FK_h@e9Kq|#!YJ^Oio=>WtE2;!d8MjR7gzZs^@8kqJ+Y6>_u;!9gUaf(f&1I`%}WjlW0x zzle}4I;5W84`ZIrO_XtK`??8FrkS)xeVph;B$5e}$_{oXSy+v1WVrx`?x}UNSen|I5g-x&5=V{!R(x8f7Fs*PH;N+zLWheo&+ z9*1i4M=pWTgTo%&sFFH3W5pYE?vbY9lFt~Nz@bZRe81)jhUO9lH>hC=`f@0OhqU4c zImRRR8+vung^I_NCND3o89L4aX@|c-obBM^9TR) z_dko(UH8uoYrZ17+!;dCkcf(vWF{3c5huq-B&8--G>(@n>;*7aSiRQoNydn2v2e_JyFfdCk ziX3aj_Yr?`Lc%6p%VlxOBHQO$KZk1rfpqS%Ce1fk$hT|on8+Kzzq8>_zJED+b#`(L zD^lW$H;1)x08fe{Lk`9;GBtw6WL$$rsevqK#iAZmVXa#~Jdn|Ff`Cl9Ayt{${2NloidPJ-$=eD(vcbdt;}tyWTVSs zJQeX~P-n3(%BoFf^zRq@=q;&M)%cAM2Z;e?qCD!D>RTMn*`)X5^U)bpqT;g970ytqEpt|!ag{Mi<8LQfCEz8 zAk|c~nnbFEnsP3zcwgFcO)h%%<ir_x}7&u;Wb0wR-rFrt%?X_jJHKqK32f(P%w`yE^F_Euo!e@c@T7drU+X9(96e*XXi@cUw! z;rGskFsnhGXDO2*{9Zva^Q?OxB-D${tlBU5av@cyy%z9al8DN$4nLR2Em_y0g&w@jpz~p0JLCA5dZ)H diff --git a/scTCRpy/rpackages/Peptides/Meta/data.rds b/scTCRpy/rpackages/Peptides/Meta/data.rds deleted file mode 100644 index 16c1790832321022bb30bd5cceccdc27e4892bfb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 200 zcmV;(05|_1iwFP!00000167Sp3c^4Pg~$FV3W7(-0Tik$w}QBKq2NjgGouZ(Gd0r@ zdU-L0B9)N5JV?HGTPUScl}=^mva0-gIc@ezB`YzR)Le9WI2dekaI?lXBYQuyZ!EWD z!y1Cd$_7AZfSe&M`@=J|rG;Gq4%^6BRULS(BQv=tU`@^gYBF-lzJ@n4`4}Mh$nrm= z9M5GJNPAt{nj{38U+RBakJuK)hzr5KqIEq>Wr?sR12Z*Li!7y+QGdQ-KL?o=0RRA< C2~wK? 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WW scales can be used as: -1. **interfaceScale_pH2:** White, Stephen (2006-06-29). "Experimentally Determined Hydrophobicity Scales". University of California, Irvine. Retrieved 2017-05-25 -2. **interfaceScale_pH8:** White, Stephen (2006-06-29). "Experimentally Determined Hydrophobicity Scales". University of California, Irvine. Retrieved 2017-05-25 -3. **octanolScale_pH2:** White, Stephen (2006-06-29). "Experimentally Determined Hydrophobicity Scales". University of California, Irvine. Retrieved 2017-05-25 -4. **octanolScale_pH8:** White, Stephen (2006-06-29). "Experimentally Determined Hydrophobicity Scales". University of California, Irvine. Retrieved 2017-05-25 -5. **oiScale_pH2:** White, Stephen (2006-06-29). "Experimentally Determined Hydrophobicity Scales". University of California, Irvine. Retrieved 2017-05-25 -6. **oiScale_pH8:** White, Stephen (2006-06-29). "Experimentally Determined Hydrophobicity Scales". University of California, Irvine. Retrieved 2017-05-25 - - -**Peptides v.2.1** - -* The charge and pI functions were rewritten in C++ and an optimization approach was used thanks to Scott McCain (@jspmccain) and Timothée Poisot (@tpoisot) suggestion. - -* An error in zScales function data was solved. Q and E values were wrongly interchanged in v 2.0. - -**Peptides v.2.0.0** - -* All datasets were unified into AAdata - -* All test were migrated to testthat - -* readXVG and plotXVG functions were improved by J. Sebastian Paez - -* kideraFactors output vector was renamed as KF# - -* Now all sequences are checked before to property calculation - -* aaDescriptos, fasgaiVectors, blosumIndices, mswhimScores, zScales, vhseScales, protFP, tScales and stScales functions were added - -**Peptides v.1.2.2** - -* crucianiProperties function was added. - -**Peptides v.1.2.1** - -* Four new functions were added: autoCorrelation, autoCovariance, crossCovariance and crucianiProperties - -* Functions related with XVG files were updated. - -* Documentation was changed to roxygen2 - -**Peptides v.1.1.2** - -* All functions were re-vectorized to support set of peptides as input - -* Kidera function now returns all factors in a unique output - -**Peptides v.1.1.1** - -* The mw function now computes the molecular weight using monoisotopic values - -* A problem with blank spaces was solved - -**Peptides v.1.1.0** - -* The kidera function and Kfactors dataset was included. - -**Peptides v.1.0.4** - -* A instaindex function bug has been fixed. - -* A problem with line breaks in sequences was solved. - -**Peptides v.1.0.3** -* A membpos function bug has been fixed. - -* The results now are not rounded. - -**Peptides v.1.0.2** - -* Hydrophobicity function now can compute the GRAVY index with one of the 38 scales includes in Peptides (*new): - - 1. **Aboderin:** Aboderin, A. A. (1971). An empirical hydrophobicity scale for α-amino-acids and some of its applications. International Journal of Biochemistry, 2(11), 537-544. - 2. **AbrahamLeo:** Abraham D.J., Leo A.J. Hydrophobicity (delta G1/2 cal). Proteins: Structure, Function and Genetics 2:130-152(1987). - 3. ***Argos:** Argos, P., Rao, J. K., & Hargrave, P. A. (1982). Structural Prediction of Membrane‐Bound Proteins. European Journal of Biochemistry, 128(2‐3), 565-575. - 4. **BlackMould:** Black S.D., Mould D.R. Hydrophobicity of physiological L-alpha amino acids. Anal. Biochem. 193:72-82(1991). - 5. **BullBreese:** Bull H.B., Breese K. Hydrophobicity (free energy of transfer to surface in kcal/mole). Arch. Biochem. Biophys. 161:665-670(1974). - 6. ***Casari:** Casari, G., & Sippl, M. J. (1992). Structure-derived hydrophobic potential: hydrophobic potential derived from X-ray structures of globular proteins is able to identify native folds. Journal of molecular biology, 224(3), 725-732. - 7. **Chothia:** Chothia, C. (1976). The nature of the accessible and buried surfaces in proteins. Journal of molecular biology, 105(1), 1-12. - 8. ***Cid:** Cid, H., Bunster, M., Canales, M., & Gazitúa, F. (1992). Hydrophobicity and structural classes in proteins. Protein engineering, 5(5), 373-375. - 9. **Cowan3.4:** Cowan R., Whittaker R.G. Hydrophobicity indices at pH 3.4 determined by HPLC. Peptide Research 3:75-80(1990). - 10. **Cowan7.5:** Cowan R., Whittaker R.G. Hydrophobicity indices at pH 7.5 determined by HPLC. Peptide Research 3:75-80(1990). - 11. **Eisenberg:** Eisenberg D., Schwarz E., Komarony M., Wall R. Normalized consensus hydrophobicity scale. J. Mol. Biol. 179:125-142(1984). - 12. ***Engelman:** Engelman, D. M., Steitz, T. A., & Goldman, A. (1986). Identifying nonpolar transbilayer helices in amino acid sequences of membrane proteins. Annual review of biophysics and biophysical chemistry, 15(1), 321-353. - 13. ***Fasman:** Fasman, G. D. (Ed.). (1989). Prediction of protein structure and the principles of protein conformation. Springer. - 14. **Fauchere:** Fauchere J.-L., Pliska V.E. Hydrophobicity scale (pi-r). Eur. J. Med. Chem. 18:369-375(1983). - 15. ***Goldsack:** Goldsack, D. E., & Chalifoux, R. C. (1973). Contribution of the free energy of mixing of hydrophobic side chains to the stability of the tertiary structure of proteins. Journal of theoretical biology, 39(3), 645-651. - 16. **Guy:** Guy H.R. Hydrophobicity scale based on free energy of transfer (kcal/mole). Biophys J. 47:61-70(1985). - 17. **HoppWoods:** Hopp T.P., Woods K.R. Hydrophilicity. Proc. Natl. Acad. Sci. U.S.A. 78:3824-3828(1981). - 18. **Janin:** Janin J. Free energy of transfer from inside to outside of a globular protein. Nature 277:491-492(1979). - 19. ***Jones:** Jones, D. D. (1975). Amino acid properties and side-chain orientation in proteins: a cross correlation approach. Journal of theoretical biology, 50(1), 167-183. - 20. ***Juretic:** Juretic, D., Lucic, B., Zucic, D., & Trinajstic, N. (1998). Protein transmembrane structure: recognition and prediction by using hydrophobicity scales through preference functions. Theoretical and computational chemistry, 5, 405-445. - 21. ***Kidera:** Kidera, A., Konishi, Y., Oka, M., Ooi, T., & Scheraga, H. A. (1985). Statistical analysis of the physical properties of the 20 naturally occurring amino acids. Journal of Protein Chemistry, 4(1), 23-55. - 22. ***Kuhn:** Kuhn, L. A., Swanson, C. A., Pique, M. E., Tainer, J. A., & Getzoff, E. D. (1995). Atomic and residue hydrophilicity in the context of folded protein structures. Proteins: Structure, Function, and Bioinformatics, 23(4), 536-547. - 23. **KyteDoolittle:** Kyte J., Doolittle R.F. Hydropathicity. J. Mol. Biol. 157:105-132(1982). - 24. ***Levitt:** Levitt, M. (1976). A simplified representation of protein conformations for rapid simulation of protein folding. Journal of molecular biology, 104(1), 59-107. - 25. **Manavalan:** Manavalan P., Ponnuswamy Average surrounding hydrophobicity. P.K. Nature 275:673-674(1978). - 26. **Miyazawa:** Miyazawa S., Jernigen R.L. Hydrophobicity scale (contact energy derived from 3D data). Macromolecules 18:534-552(1985). - 27. **Parker:** Parker J.M.R., Guo D., Hodges R.S. Hydrophilicity scale derived from HPLC peptide retention times. Biochemistry 25:5425-5431(1986). - 28. ***Ponnuswamy:** Ponnuswamy, P. K. (1993). Hydrophobic charactesristics of folded proteins. Progress in biophysics and molecular biology, 59(1), 57-103. - 29. ***Prabhakaran:** Prabhakaran, M. (1990). The distribution of physical, chemical and conformational properties in signal and nascent peptides. Biochem. J, 269, 691-696. - 30. **Rao:** Rao M.J.K., Argos P. Membrane buried helix parameter. Biochim. Biophys. Acta 869:197-214(1986). - 31. **Rose:** Rose G.D., Geselowitz A.R., Lesser G.J., Lee R.H., Zehfus M.H. Mean fractional area loss (f) [average area buried/standard state area]. Science 229:834-838(1985) - 32. **Roseman:** Roseman M.A. Hydrophobicity scale (pi-r). J. Mol. Biol. 200:513-522(1988). - 33. **Sweet:** Sweet R.M., Eisenberg D. Optimized matching hydrophobicity (OMH). J. Mol. Biol. 171:479-488(1983). - 34. **Tanford:** Tanford C. Hydrophobicity scale (Contribution of hydrophobic interactions to the stability of the globular conformation of proteins). J. Am. Chem. Soc. 84:4240-4274(1962). - 35. **Welling:** Welling G.W., Weijer W.J., Van der Zee R., Welling-Wester S. Antigenicity value X 10. FEBS Lett. 188:215-218(1985). - 36. **Wilson:** Wilson K.J., Honegger A., Stotzel R.P., Hughes G.J. Hydrophobic constants derived from HPLC peptide retention times. Biochem. J. 199:31-41(1981). - 37. **Wolfenden:** Wolfenden R.V., Andersson L., Cullis P.M., Southgate C.C.F. Hydration potential (kcal/mole) at 25C. Biochemistry 20:849-855(1981). - 38. ***Zimmerman:** Zimmerman, J. M., Eliezer, N., & Simha, R. (1968). The characterization of amino acid sequences in proteins by statistical methods. Journal of theoretical biology, 21(2), 170-201. - - -* The mw function has been fixed to give the same result as ExPASy pI/mw tool. -* The hmoment function is now vectorized and allow adjust the windows size. (thanks to an anonymous reviewer of RJournal). -* The pepdata dataset and the variable name are now unified to lowercases. -* The seqinr package dependency was removed. diff --git a/scTCRpy/rpackages/Peptides/R/Peptides b/scTCRpy/rpackages/Peptides/R/Peptides deleted file mode 100644 index 3b65e3cbb..000000000 --- a/scTCRpy/rpackages/Peptides/R/Peptides +++ /dev/null @@ -1,27 +0,0 @@ -# File share/R/nspackloader.R -# Part of the R package, http://www.R-project.org -# -# Copyright (C) 1995-2012 The R Core Team -# -# This program is free software; you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation; either version 2 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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-1,29 +0,0 @@ -aaComp aaComp -AAdata AAdata -aaDescriptors aaDescriptors -aIndex aIndex -autoCorrelation autoCorrelation -autoCovariance autoCovariance -blosumIndices blosumIndices -boman boman -charge charge -crossCovariance crossCovariance -crucianiProperties crucianiProperties -fasgaiVectors fasgaiVectors -hmoment hmoment -hydrophobicity hydrophobicity -instaIndex instaIndex -kideraFactors kideraFactors -lengthpep lengthpep -membpos membpos -mswhimScores mswhimScores -mw mw -pepdata pepdata -pI pI -plotXVG plotXVG -protFP protFP -readXVG readXVG -stScales stScales -tScales tScales -vhseScales vhseScales -zScales zScales diff --git a/scTCRpy/rpackages/Peptides/help/Peptides.rdx b/scTCRpy/rpackages/Peptides/help/Peptides.rdx deleted file mode 100644 index b16caf38bcc5202552946b5b0d93341911c87767..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 801 zcmV++1K#`}iwFP!0000017+3QOB7KU0Pr)r?ryufX$9tsR;Cq|QR8hD48_=x2m~+q 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zcmV-40n7d$iwFP!000001MO1FPQx$|H0g^*lp^kY0(Y)eQBZG{3RSNgXQ?e>ueEkt z%Eu#&h|@-V0$gm*?9OOC+D$Hm=!xDio`d)lgO}&x{y~WBCW1TB7vp#j0p?^}LbF)~ z0U9&`ctkH*yFlz)!NOMf-k>S$0?mmTYY3WbLtp>UdteP#V)83%@;ejtT4M4Wlw=_3 zhEf47iR`O0pLNA#da?q{Ub*!RJQK_X4$A-W*4Id2@{Vc+Zl)DAL})jCN_=Q~TIi3Hf0IlO2_aQd-M3@imZbg~bx%V=D#!NWgw)7^yyK{3cF`pt2g3BBMpa7M8-$4eJV z*wOE=(G=7S{i3EyX#H0_&raBaT80gjkf;nRXXFxh0(u5)jC6ZRT{}=I7aVirx0KRN zY-g5=DWlA63hU0nV!1ZETh}F{cUvjw!cIoF4pfHu%%l>fU(_7=REknB_b!NDrHqfL zP~}Awx=1U(?`6*~4x8upA@77_!c4|be&>Z$!gL3IjVOe8tC1TAM4J(X3_Z8>FT`aV oE>n&F(acAIkXpqedp}q`VM-fY9DTne - -R: Calculate Indices and Theoretical Physicochemical Properties of -Protein Sequences - - - -

Calculate Indices and Theoretical Physicochemical Properties of -Protein Sequences - -

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Documentation for package ‘Peptides’ version 2.4

- - - -

Help Pages

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
aaCompCompute the amino acid composition of a protein sequence
AAdataProperties, scales and indices for the 20 naturally occurring amino acids from various sources
aaDescriptorsCompute 66 descriptors for each amino acid of a protein sequence.
aIndexCompute the aliphatic index of a protein sequence
autoCorrelationCompute the auto-correlation index of a protein sequence
autoCovarianceCompute the auto-covariance index of a protein sequence
blosumIndicesCompute the BLOSUM62 derived indices of a protein sequence
bomanCompute the Boman (Potential Protein Interaction) index
chargeCompute the theoretical net charge of a protein sequence
crossCovarianceCompute the cross-covariance index of a protein sequence
crucianiPropertiesCompute the Cruciani properties of a protein sequence
fasgaiVectorsCompute the FASGAI vectors of a protein sequence
hmomentCompute the hydrophobic moment of a protein sequence
hydrophobicityCompute the hydrophobicity index of a protein sequence
instaIndexCompute the instability index of a protein sequence
kideraFactorsCompute the Kidera factors of a protein sequence
lengthpepCompute the amino acid length of a protein sequence
membposCompute theoretically the class of a protein sequence
mswhimScoresCompute the MS-WHIM scores of a protein sequence
mwCompute the molecular weight of a protein sequence
pepdataPhysicochemical properties and indices from 100 amino acid sequences
pICompute the isoelectic point (pI) of a protein sequence
plotXVGPlot time series from GROMACS XVG files
protFPCompute the protFP descriptors of a protein sequence
readXVGRead output data from a XVG format file.
stScalesCompute the ST-scales of a protein sequence
tScalesCompute the T-scales of a protein sequence
vhseScalesCompute the VHSE-scales of a protein sequence
zScalesCompute the Z-scales of a protein sequence
- diff --git a/scTCRpy/rpackages/Peptides/html/R.css b/scTCRpy/rpackages/Peptides/html/R.css deleted file mode 100644 index f10f5ea66..000000000 --- a/scTCRpy/rpackages/Peptides/html/R.css +++ /dev/null @@ -1,97 +0,0 @@ -body { - background: white; - color: black; -} - -a:link { - background: white; - color: blue; -} - -a:visited { - background: white; - color: rgb(50%, 0%, 50%); -} - -h1 { - background: white; - color: rgb(55%, 55%, 55%); - font-family: monospace; - font-size: x-large; - text-align: center; -} - -h2 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-size: large; - text-align: center; -} - -h3 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-size: large; -} - -h4 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-style: italic; - font-size: large; -} - -h5 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; -} - -h6 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-style: italic; -} - -img.toplogo { - width: 4em; - vertical-align: middle; -} - -img.arrow { - width: 30px; - height: 30px; - border: 0; -} - -span.acronym { - font-size: small; -} - -span.env { - font-family: monospace; -} - -span.file { - font-family: monospace; -} - -span.option{ - font-family: monospace; -} - -span.pkg { - font-weight: bold; -} - -span.samp{ - font-family: monospace; -} - -div.vignettes a:hover { - background: rgb(85%, 85%, 85%); -} diff --git a/scTCRpy/rpackages/Peptides/include/Peptides.h b/scTCRpy/rpackages/Peptides/include/Peptides.h deleted file mode 100644 index 454910d71..000000000 --- a/scTCRpy/rpackages/Peptides/include/Peptides.h +++ /dev/null @@ -1,9 +0,0 @@ -// Generated by using Rcpp::compileAttributes() -> do not edit by hand -// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 - -#ifndef RCPP_Peptides_H_GEN_ -#define RCPP_Peptides_H_GEN_ - -#include "Peptides_RcppExports.h" - -#endif // RCPP_Peptides_H_GEN_ diff --git a/scTCRpy/rpackages/Peptides/include/Peptides_RcppExports.h b/scTCRpy/rpackages/Peptides/include/Peptides_RcppExports.h deleted file mode 100644 index 1a5160a05..000000000 --- a/scTCRpy/rpackages/Peptides/include/Peptides_RcppExports.h +++ /dev/null @@ -1,67 +0,0 @@ -// Generated by using Rcpp::compileAttributes() -> do not edit by hand -// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 - -#ifndef RCPP_Peptides_RCPPEXPORTS_H_GEN_ -#define RCPP_Peptides_RCPPEXPORTS_H_GEN_ - -#include - -namespace Peptides { - - using namespace Rcpp; - - namespace { - void validateSignature(const char* sig) { - Rcpp::Function require = Rcpp::Environment::base_env()["require"]; - require("Peptides", Rcpp::Named("quietly") = true); - typedef int(*Ptr_validate)(const char*); - static Ptr_validate p_validate = (Ptr_validate) - R_GetCCallable("Peptides", "Peptides_RcppExport_validate"); - if (!p_validate(sig)) { - throw Rcpp::function_not_exported( - "C++ function with signature '" + std::string(sig) + "' not found in Peptides"); - } - } - } - - inline Rcpp::List chargeList(std::vector< std::string > seq, NumericVector pH, std::string pKscale = "Lehninger") { - typedef SEXP(*Ptr_chargeList)(SEXP,SEXP,SEXP); - static Ptr_chargeList p_chargeList = NULL; - if (p_chargeList == NULL) { - validateSignature("Rcpp::List(*chargeList)(std::vector< std::string >,NumericVector,std::string)"); - p_chargeList = (Ptr_chargeList)R_GetCCallable("Peptides", "Peptides_chargeList"); - } - RObject rcpp_result_gen; - { - RNGScope RCPP_rngScope_gen; - rcpp_result_gen = p_chargeList(Rcpp::wrap(seq), Rcpp::wrap(pH), Rcpp::wrap(pKscale)); - } - if (rcpp_result_gen.inherits("interrupted-error")) - throw Rcpp::internal::InterruptedException(); - if (rcpp_result_gen.inherits("try-error")) - throw Rcpp::exception(as(rcpp_result_gen).c_str()); - return Rcpp::as(rcpp_result_gen); - } - - inline double absoluteCharge(std::string seq, double pH = 7, std::string pKscale = "Lehninger") { - typedef SEXP(*Ptr_absoluteCharge)(SEXP,SEXP,SEXP); - static Ptr_absoluteCharge p_absoluteCharge = NULL; - if (p_absoluteCharge == NULL) { - validateSignature("double(*absoluteCharge)(std::string,double,std::string)"); - p_absoluteCharge = (Ptr_absoluteCharge)R_GetCCallable("Peptides", "Peptides_absoluteCharge"); - } - RObject rcpp_result_gen; - { - RNGScope RCPP_rngScope_gen; - rcpp_result_gen = p_absoluteCharge(Rcpp::wrap(seq), Rcpp::wrap(pH), Rcpp::wrap(pKscale)); - } - if (rcpp_result_gen.inherits("interrupted-error")) - throw Rcpp::internal::InterruptedException(); - if (rcpp_result_gen.inherits("try-error")) - throw Rcpp::exception(as(rcpp_result_gen).c_str()); - return Rcpp::as(rcpp_result_gen); - } - -} - -#endif // RCPP_Peptides_RCPPEXPORTS_H_GEN_ diff --git a/scTCRpy/rpackages/vegan/DESCRIPTION b/scTCRpy/rpackages/vegan/DESCRIPTION deleted file mode 100644 index 08ae346f3..000000000 --- a/scTCRpy/rpackages/vegan/DESCRIPTION +++ /dev/null @@ -1,23 +0,0 @@ -Package: vegan -Title: Community Ecology Package -Version: 2.5-5 -Date: 2019-05-08 -Author: Jari Oksanen, F. Guillaume Blanchet, Michael Friendly, Roeland Kindt, - Pierre Legendre, Dan McGlinn, Peter R. Minchin, R. B. O'Hara, - Gavin L. Simpson, Peter Solymos, M. Henry H. Stevens, Eduard Szoecs, - Helene Wagner -Maintainer: Jari Oksanen -Depends: permute (>= 0.9-0), lattice, R (>= 3.4.0) -Suggests: parallel, tcltk, knitr -Imports: MASS, cluster, mgcv -VignetteBuilder: utils, knitr -Description: Ordination methods, diversity analysis and other - functions for community and vegetation ecologists. -License: GPL-2 -BugReports: https://github.com/vegandevs/vegan/issues -URL: https://cran.r-project.org, https://github.com/vegandevs/vegan -NeedsCompilation: yes -Packaged: 2019-05-11 14:15:58 UTC; jarioksa -Repository: CRAN -Date/Publication: 2019-05-12 14:50:03 UTC -Built: R 3.6.0; x86_64-pc-linux-gnu; 2019-06-05 12:39:39 UTC; unix diff --git a/scTCRpy/rpackages/vegan/INDEX b/scTCRpy/rpackages/vegan/INDEX deleted file mode 100644 index dd36230a5..000000000 --- a/scTCRpy/rpackages/vegan/INDEX +++ /dev/null @@ -1,180 +0,0 @@ -BCI Barro Colorado Island Tree Counts -CCorA Canonical Correlation Analysis -MDSrotate Rotate First MDS Dimension Parallel to an - External Variable -MOStest Mitchell-Olds & Shaw Test for the Location of - Quadratic Extreme -RsquareAdj Adjusted R-square -SSarrhenius Self-Starting nls Species-Area Models -add1.cca Add or Drop Single Terms to a Constrained - Ordination Model -adipart Additive Diversity Partitioning and - Hierarchical Null Model Testing -adonis Permutational Multivariate Analysis of Variance - Using Distance Matrices -anosim Analysis of Similarities -anova.cca Permutation Test for Constrained Correspondence - Analysis, Redundancy Analysis and Constrained - Analysis of Principal Coordinates -avgdist Averaged Subsampled Dissimilarity Matrices -beals Beals Smoothing and Degree of Absence -betadisper Multivariate homogeneity of groups dispersions - (variances) -betadiver Indices of beta Diversity -bgdispersal Coefficients of Biogeographical Dispersal - Direction -bioenv Best Subset of Environmental Variables with - Maximum (Rank) Correlation with Community - Dissimilarities -biplot.rda PCA biplot -capscale [Partial] Distance-based Redundancy Analysis -cascadeKM K-means partitioning using a range of values of - K -cca [Partial] [Constrained] Correspondence Analysis - and Redundancy Analysis -cca.object Result Object from Constrained Ordination -clamtest Multinomial Species Classification Method - (CLAM) -commsim Create an Object for Null Model Algorithms -contribdiv Contribution Diversity Approach -decorana Detrended Correspondence Analysis and Basic - Reciprocal Averaging -decostand Standardization Methods for Community Ecology -designdist Design your own Dissimilarities -deviance.cca Statistics Resembling Deviance and AIC for - Constrained Ordination -dispindmorisita Morisita index of intraspecific aggregation -dispweight Dispersion-based weighting of species counts -distconnected Connectedness of Dissimilarities -diversity Ecological Diversity Indices -dune Vegetation and Environment in Dutch Dune - Meadows. -dune.taxon Taxonomic Classification and Phylogeny of Dune - Meadow Species -eigenvals Extract Eigenvalues from an Ordination Object -envfit Fits an Environmental Vector or Factor onto an - Ordination -eventstar Scale Parameter at the Minimum of the Tsallis - Evenness Profile -fisherfit Fit Fisher's Logseries and Preston's Lognormal - Model to Abundance Data -goodness.cca Diagnostic Tools for [Constrained] Ordination - (CCA, RDA, DCA, CA, PCA) -goodness.metaMDS Goodness of Fit and Shepard Plot for Nonmetric - Multidimensional Scaling -hatvalues.cca Linear Model Diagnostics for Constrained - Ordination -humpfit-deprecated No-interaction Model for Hump-backed Species - Richness vs. Biomass -indpower Indicator Power of Species -isomap Isometric Feature Mapping Ordination -kendall.global Kendall coefficient of concordance -linestack Plots One-dimensional Diagrams without - Overwriting Labels -make.cepnames Abbreviates a Botanical or Zoological Latin - Name into an Eight-character Name -mantel Mantel and Partial Mantel Tests for - Dissimilarity Matrices -mantel.correlog Mantel Correlogram -metaMDS Nonmetric Multidimensional Scaling with Stable - Solution from Random Starts, Axis Scaling and - Species Scores -mite Oribatid Mite Data with Explanatory Variables -monoMDS Global and Local Non-metric Multidimensional - Scaling and Linear and Hybrid Scaling -mrpp Multi Response Permutation Procedure and Mean - Dissimilarity Matrix -mso Functions for performing and displaying a - spatial partitioning of cca or rda results -multipart Multiplicative Diversity Partitioning -nestedtemp Nestedness Indices for Communities of Islands - or Patches -nobs.adonis Extract the Number of Observations from a vegan - Fit. -nullmodel Null Model and Simulation -oecosimu Evaluate Statistics with Null Models of - Biological Communities -ordiArrowTextXY Support Functions for Drawing Vectors -ordiarrows Add Arrows and Line Segments to Ordination - Diagrams -ordihull Display Groups or Factor Levels in Ordination - Diagrams -ordilabel Add Text on Non-transparent Label to an - Ordination Plot. -ordiplot Alternative plot and identify Functions for - Ordination -ordipointlabel Ordination Plots with Points and Optimized - Locations for Text -ordiresids Plots of Residuals and Fitted Values for - Constrained Ordination -ordistep Choose a Model by Permutation Tests in - Constrained Ordination -ordisurf Fit and Plot Smooth Surfaces of Variables on - Ordination. -orditkplot Ordination Plot with Movable Labels -orditorp Add Text or Points to Ordination Plots -ordixyplot Trellis (Lattice) Plots for Ordination -pcnm Principal Coordinates of Neighbourhood Matrix -permatfull Matrix Permutation Algorithms for - Presence-Absence and Count Data -permustats Extract, Analyse and Display Permutation - Results -permutations Permutation tests in Vegan -permutest.betadisper Permutation test of multivariate homogeneity of - groups dispersions (variances) -plot.cca Plot or Extract Results of Constrained - Correspondence Analysis or Redundancy Analysis -prc Principal Response Curves for Treatments with - Repeated Observations -predict.cca Prediction Tools for [Constrained] Ordination - (CCA, RDA, DCA, CA, PCA) -procrustes Procrustes Rotation of Two Configurations and - PROTEST -pyrifos Response of Aquatic Invertebrates to - Insecticide Treatment -radfit Rank - Abundance or Dominance / Diversity - Models -rankindex Compares Dissimilarity Indices for Gradient - Detection -rarefy Rarefaction Species Richness -raupcrick Raup-Crick Dissimilarity with Unequal Sampling - Densities of Species -read.cep Reads a CEP (Canoco) data file -renyi Renyi and Hill Diversities and Corresponding - Accumulation Curves -reorder.hclust Reorder a Hierarchical Clustering Tree -scores Get Species or Site Scores from an Ordination -screeplot.cca Screeplots for Ordination Results and Broken - Stick Distributions -simper Similarity Percentages -simulate.rda Simulate Responses with Gaussian Error or - Permuted Residuals for Constrained Ordination -sipoo Birds in the Archipelago of Sipoo (Sibbo and - Borgå) -spantree Minimum Spanning Tree -specaccum Species Accumulation Curves -specpool Extrapolated Species Richness in a Species Pool -sppscores Add or Replace Species Scores in Distance-Based - Ordination -stepacross Stepacross as Flexible Shortest Paths or - Extended Dissimilarities -stressplot.wcmdscale Display Ordination Distances Against Observed - Distances in Eigenvector Ordinations -taxondive Indices of Taxonomic Diversity and Distinctness -tolerance Species tolerances and sample heterogeneities -treedive Functional Diversity and Community Distances - from Species Trees -tsallis Tsallis Diversity and Corresponding - Accumulation Curves -varespec Vegetation and environment in lichen pastures -varpart Partition the Variation of Community Matrix by - 2, 3, or 4 Explanatory Matrices -vegan-deprecated Deprecated Functions in vegan package -vegan-package Community Ecology Package: Ordination, - Diversity and Dissimilarities -vegandocs Display vegan 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names (no dots) - -export(CCorA, MOStest, RsquareAdj, SSarrhenius, SSgitay, SSgleason, -SSlomolino, adipart, adonis, anosim, beals, betadisper, betadiver, -bgdispersal, bioenv, bioenvdist, bstick, cIndexKM, calibrate, capscale, -cascadeKM, cca, chaodist, contribdiv, clamtest, commsim, cutreeord, -dbrda, decorana, decostand, designdist, -coverscale, dispweight, dispindmorisita, distconnected, -diversity, downweight, drarefy, eigengrad, eigenvals, envfit, -estaccumR, estimateR, eventstar, factorfit, fisherfit, fitspecaccum, -gdispweight,goodness, hiersimu, humpfit, indpower, inertcomp, initMDS, -intersetcor, isomapdist, isomap, linestack, mantel, meandist, -metaMDSdist, metaMDSiter, metaMDSredist, MDSrotate, metaMDS, monoMDS, -mrpp, msoplot, mso, multipart, make.commsim, nestedbetajac, nestedbetasor, nestedchecker, -nesteddisc, nestedn0, nestednodf, nestedtemp, nullmodel, oecosimu, smbind, -ordiareatest, ordiR2step, -ordiarrows, ordiArrowMul, ordiArrowTextXY, ordibar, ordicloud, -ordicluster, ordiellipse, ordigrid, -ordihull, ordilabel, ordiplot, ordipointlabel, ordiresids, -ordisegments, ordispider, ordisplom, ordistep, ordisurf, -orditkplot, orditorp, ordixyplot, ordiYbar, -pcnm, permatfull, permatswap, permustats, permutest, -poolaccum, postMDS, prc, prestondistr, prestonfit, procrustes, -protest, radfit, radlattice, rankindex, rarefy, rarecurve, rareslope, -raupcrick, rda, renyiaccum, renyi, rrarefy, scores, -showvarparts, simper, spandepth, spantree, specaccum, specslope, -specnumber, specpool2vect, specpool, spenvcor, "sppscores<-", sppscores, -stepacross, stressplot, swan, tabasco, taxa2dist, taxondive, tolerance, -treedist, treedive, treeheight, tsallisaccum, tsallis, varpart, -vectorfit, vegandocs, vegdist, avgdist, vegemite, veiledspec, wascores, -wcmdscale, wisconsin) -## export pasteCall for 'permute' -export(pasteCall) -## export anova.cca for 'BiodiversityR': this should be fixed there -export(anova.cca) -export(as.mcmc.oecosimu, as.mcmc.permat) -## alternative implementation of adonis: may be eliminated later -export(adonis2) - -## export regular functions with dot names - -export(as.fisher, as.preston, as.rad, fieller.MOStest, -fisher.alpha, kendall.global, kendall.post, make.cepnames, -mantel.correlog, mantel.partial, no.shared, rad.lognormal, rad.null, -rad.preempt, rad.zipf, rad.zipfbrot, read.cep, -vif.cca) - -## Export panel functions -export(panel.ordi, panel.ordiarrows, panel.ordi3d, prepanel.ordi3d) - -## Export .Defunct functions (to be removed later) -export(commsimulator) - -## Deprecated functions & methods -export(as.mlm) -S3method(as.mlm, cca) -S3method(as.mlm, rda) - -## do NOT export the following internal functions - -## export(orderingKM, ordiArgAbsorber, ordiArrowMul, -## ordiGetData, ordimedian, ordiNAexclude, ordiNApredict, -## ordiParseFormula, ordiTerminfo, pregraphKM, simpleRDA2, varpart2, -## varpart3, varpart4, veganCovEllipse, veganMahatrans) - -## Imports -import(stats) -import(graphics) -import(permute) -importFrom(utils, news, vignette, combn, flush.console, head, object.size, - read.fortran, read.fwf, tail, str) -importFrom(tools, Rd2txt, startDynamicHelp) -import(grDevices) ## too many functions to be listed separately -import(lattice) -importFrom(parallel, mclapply, makeCluster, stopCluster, clusterEvalQ, - parApply, parLapply, parSapply, parRapply, parCapply) -importFrom(MASS, isoMDS, sammon, Shepard, mvrnorm, lda) -importFrom(cluster, daisy, ellipsoidhull) -## 's' must be imported in mgcv < 1.8-0 (not needed later) -importFrom(mgcv, gam, s, te, predict.gam, summary.gam) - -## Registration of S3 methods defined in vegan -# adipart: vegan -S3method(adipart, default) -S3method(adipart, formula) -# AIC: stats -S3method(AIC, fitspecaccum) -S3method(AIC, radfit) -S3method(AIC, radfit.frame) -# RsquareAdj: vegan -S3method(RsquareAdj, cca) -S3method(RsquareAdj, default) -S3method(RsquareAdj, glm) -S3method(RsquareAdj, lm) -S3method(RsquareAdj, rda) -# TukeyHSD: stats -S3method(TukeyHSD, betadisper) -# add1: stats -S3method(add1, cca) -# alias: stats -S3method(alias, cca) -# anova: stats -S3method(anova, betadisper) -S3method(anova, cca) -S3method(anova, prc) -# as.hclust: stats -S3method(as.hclust, spantree) -## Do not export as.mcmc now: would need import(coda) -# as.mcmc: coda <======= rare -#S3method(as.mcmc, oecosimu) -#S3method(as.mcmc, permat) - -# as.ts: stats -S3method(as.ts, oecosimu) -S3method(as.ts, permat) -# bioenv: vegan -S3method(bioenv, default) -S3method(bioenv, formula) -# biplot: stats -S3method(biplot, CCorA) -S3method(biplot, cca) -S3method(biplot, rda) -# boxplot: graphics -S3method(boxplot, betadisper) -S3method(boxplot, permustats) -S3method(boxplot, specaccum) -# bstick: vegan -S3method(bstick, cca) -S3method(bstick, decorana) -S3method(bstick, default) -S3method(bstick, prcomp) -S3method(bstick, princomp) -## c: base -S3method(c, permustats) -# calibrate: vegan -S3method(calibrate, cca) -S3method(calibrate, ordisurf) -# cca: vegan -S3method(cca, default) -S3method(cca, formula) -# coef: stats -S3method(coef, cca) -S3method(coef, radfit) -S3method(coef, radfit.frame) -S3method(coef, rda) -# confint: stats -- also uses MASS:::confint.glm & MASS:::profile.glm -# does this work with namespaces?? -S3method(confint, MOStest) -# cooks.distance: stats -S3method(cooks.distance, cca) -# cophenetic: stats -S3method(cophenetic, spantree) -# density: stats -S3method(density, permustats) -# densityplot: lattice -S3method(densityplot, permustats) -# deviance: stats -S3method(deviance, cca) -S3method(deviance, rda) -S3method(deviance, radfit) -S3method(deviance, radfit.frame) -S3method(deviance, fitspecaccum) -# df.residual: stats -S3method(df.residual, cca) -# drop1: stats -S3method(drop1, cca) -# eigenvals: vegan -S3method(eigenvals, betadisper) -S3method(eigenvals, cca) -S3method(eigenvals, default) -S3method(eigenvals, dudi) -S3method(eigenvals, pca) -S3method(eigenvals, pcnm) -S3method(eigenvals, pco) -S3method(eigenvals, prcomp) -S3method(eigenvals, princomp) -S3method(eigenvals, wcmdscale) -# envfit: vegan -S3method(envfit, default) -S3method(envfit, formula) -# estimateR: vegan -S3method(estimateR, data.frame) -S3method(estimateR, default) -S3method(estimateR, matrix) -# extractAIC: stats -S3method(extractAIC, cca) -# fitted: stats -S3method(fitted, capscale) -S3method(fitted, cca) -S3method(fitted, dbrda) -S3method(fitted, procrustes) -S3method(fitted, radfit) -S3method(fitted, radfit.frame) -S3method(fitted, rda) -# goodness: vegan -S3method(goodness, cca) -S3method(goodness, metaMDS) -S3method(goodness, monoMDS) -# hatvalues: stats -S3method(hatvalues, cca) -S3method(hatvalues, rda) -# head: utils -S3method(head, summary.cca) -# hiersimu: vegan -S3method(hiersimu, default) -S3method(hiersimu, formula) -# methods for hclust object in base R: these would be better in R -S3method(reorder, hclust) -S3method(rev, hclust) -# identify: graphics -S3method(identify, ordiplot) -# labels: base -S3method(labels, envfit) -# lines: graphics -S3method(lines, fitspecaccum) -S3method(lines, humpfit) -S3method(lines, permat) -S3method(lines, preston) -S3method(lines, prestonfit) -S3method(lines, procrustes) -S3method(lines, radline) -S3method(lines, radfit) -S3method(lines, spantree) -S3method(lines, specaccum) -## logLik: stats -S3method(logLik, fitspecaccum) -S3method(logLik, radfit) -S3method(logLik, radfit.frame) -# model.frame, model.matrix: stats -S3method(model.frame, cca) -S3method(model.matrix, cca) -S3method(model.matrix, rda) -# multipart: vegan -S3method(multipart, default) -S3method(multipart, formula) -# nobs: stats -S3method(nobs, CCorA) -S3method(nobs, adonis) -S3method(nobs, anova.cca) -S3method(nobs, betadisper) -S3method(nobs, cca) -S3method(nobs, decorana) -S3method(nobs, fitspecaccum) -S3method(nobs, isomap) -S3method(nobs, metaMDS) -S3method(nobs, pcnm) -S3method(nobs, procrustes) -S3method(nobs, rad) -S3method(nobs, varpart) -S3method(nobs, wcmdscale) -# ordisurf: vegan -S3method(ordisurf, default) -S3method(ordisurf, formula) - -## permustats methods -S3method(permustats, adonis) -S3method(permustats, anosim) -S3method(permustats, mantel) -S3method(permustats, mrpp) -S3method(permustats, oecosimu) -S3method(permustats, ordiareatest) -S3method(permustats, permutest.betadisper) -S3method(permustats, permutest.cca) -S3method(permustats, protest) -S3method(permustats, anova.cca) -## these return an error: no permutation data -S3method(permustats, CCorA) -S3method(permustats, envfit) -S3method(permustats, factorfit) -S3method(permustats, vectorfit) -S3method(permustats, mso) - -S3method(print, permustats) -S3method(summary, permustats) -S3method(print, summary.permustats) - -# permutest: vegan -S3method(permutest, betadisper) -S3method(permutest, cca) -S3method(permutest, default) -# persp: graphics -S3method(persp, renyiaccum) -S3method(persp, tsallisaccum) -# plot: graphics -S3method(plot, MOStest) -S3method(plot, anosim) -S3method(plot, betadisper) -S3method(plot, betadiver) -S3method(plot, cascadeKM) -S3method(plot, cca) -S3method(plot, contribdiv) -S3method(plot, clamtest) -S3method(plot, decorana) -S3method(plot, envfit) -S3method(plot, fisher) -S3method(plot, fisherfit) -S3method(plot, fitspecaccum) -S3method(plot, humpfit) -S3method(plot, isomap) -S3method(plot, mantel.correlog) -S3method(plot, meandist) -S3method(plot, metaMDS) -S3method(plot, monoMDS) -S3method(plot, nestednodf) -S3method(plot, nestedtemp) -S3method(plot, ordisurf) -S3method(plot, ordipointlabel) -S3method(plot, orditkplot) -S3method(plot, permat) -S3method(plot, poolaccum) -S3method(plot, prc) -S3method(plot, preston) -S3method(plot, prestonfit) -S3method(plot, procrustes) -S3method(plot, rad) -S3method(plot, radfit) -S3method(plot, radfit.frame) -S3method(plot, radline) -S3method(plot, rda) -S3method(plot, renyi) -S3method(plot, renyiaccum) -S3method(plot, spantree) -S3method(plot, specaccum) -S3method(plot, taxondive) -S3method(plot, varpart) -S3method(plot, varpart234) -S3method(plot, wcmdscale) -# points: graphics -S3method(points, cca) -S3method(points, decorana) -S3method(points, humpfit) -S3method(points, metaMDS) -S3method(points, monoMDS) -S3method(points, ordiplot) -S3method(points, orditkplot) -S3method(points, procrustes) -S3method(points, radline) -S3method(points, radfit) -# predict: stats -S3method(predict, cca) -S3method(predict, decorana) -S3method(predict, fitspecaccum) -S3method(predict, humpfit) -S3method(predict, procrustes) -S3method(predict, radline) -S3method(predict, radfit) -S3method(predict, radfit.frame) -S3method(predict, rda) -S3method(predict, specaccum) -# print: base -S3method(print, CCorA) -S3method(print, MOStest) -S3method(print, adonis) -S3method(print, anosim) -S3method(print, betadisper) -S3method(print, bioenv) -S3method(print, cca) -S3method(print, commsim) -S3method(print, decorana) -S3method(print, eigenvals) -S3method(print, envfit) -S3method(print, factorfit) -S3method(print, fisherfit) -S3method(print, humpfit) -S3method(print, isomap) -S3method(print, mantel) -S3method(print, mantel.correlog) -S3method(print, metaMDS) -S3method(print, monoMDS) -S3method(print, mrpp) -S3method(print, mso) -S3method(print, nestedchecker) -S3method(print, nesteddisc) -S3method(print, nestedn0) -S3method(print, nestednodf) -S3method(print, nestedtemp) -S3method(print, nullmodel) -S3method(print, oecosimu) -S3method(print, ordiareatest) -S3method(print, permat) -S3method(print, permutest.betadisper) -S3method(print, permutest.cca) -S3method(print, poolaccum) -S3method(print, prestonfit) -S3method(print, procrustes) -S3method(print, protest) -S3method(print, radfit) -S3method(print, radfit.frame) -S3method(print, radline) -S3method(print, rda) -S3method(print, specaccum) -S3method(print, simmat) -S3method(print, simper) -S3method(print, summary.bioenv) -S3method(print, summary.cca) -S3method(print, summary.clamtest) -S3method(print, summary.decorana) -S3method(print, summary.dispweight) -S3method(print, summary.eigenvals) -S3method(print, summary.humpfit) -S3method(print, summary.isomap) -S3method(print, summary.meandist) -S3method(print, summary.permat) -S3method(print, summary.prc) -S3method(print, summary.procrustes) -S3method(print, summary.simper) -S3method(print, summary.taxondive) -S3method(print, taxondive) -S3method(print, tolerance.cca) -S3method(print, varpart) -S3method(print, varpart234) -S3method(print, vectorfit) -S3method(print, wcmdscale) -# profile: stats -# see note on 'confint' -S3method(profile, MOStest) -S3method(profile, humpfit) -## qqmath: lattice -S3method(qqmath, permustats) -## qqnorm: stats -S3method(qqnorm, permustats) -## qr: base -S3method(qr, cca) -# radfit: vegan -S3method(radfit, data.frame) -S3method(radfit, default) -S3method(radfit, matrix) -# rda: vegan -S3method(rda, default) -S3method(rda, formula) -# residuals: stats -S3method(residuals, cca) -S3method(residuals, procrustes) -# rstandard, rstudent: stats -S3method(rstandard, cca) -S3method(rstudent, cca) -# scores: vegan -S3method(scores, betadisper) -S3method(scores, betadiver) -S3method(scores, cca) -S3method(scores, decorana) -S3method(scores, default) -S3method(scores, envfit) -S3method(scores, hclust) -S3method(scores, lda) -S3method(scores, metaMDS) -S3method(scores, monoMDS) -S3method(scores, ordihull) -S3method(scores, ordiplot) -S3method(scores, orditkplot) -S3method(scores, pcnm) -S3method(scores, rda) -S3method(scores, wcmdscale) -# screeplot: stats -S3method(screeplot, cca) -S3method(screeplot, decorana) -S3method(screeplot, prcomp) -S3method(screeplot, princomp) -# sigma: stats -S3method(sigma, cca) -# simulate: stats -S3method(simulate, capscale) -S3method(simulate, cca) -S3method(simulate, dbrda) -S3method(simulate, rda) -S3method(simulate, nullmodel) -# specslope: vegan -S3method(specslope, specaccum) -S3method(specslope, fitspecaccum) -## sppscores<-: vegan -S3method("sppscores<-", capscale) -S3method("sppscores<-", dbrda) -S3method("sppscores<-", metaMDS) -# SSD: stats -S3method(SSD, cca) -# str: utils -S3method(str, nullmodel) -# stressplot: vegan -S3method(stressplot, default) -S3method(stressplot, monoMDS) -S3method(stressplot, wcmdscale) -S3method(stressplot, capscale) -S3method(stressplot, dbrda) -S3method(stressplot, cca) -S3method(stressplot, rda) -S3method(stressplot, prcomp) -S3method(stressplot, princomp) -# summary: base -S3method(summary, anosim) -S3method(summary, bioenv) -S3method(summary, cca) -S3method(summary, clamtest) -S3method(summary, decorana) -S3method(summary, dispweight) -S3method(summary, eigenvals) -S3method(summary, humpfit) -S3method(summary, isomap) -S3method(summary, meandist) -S3method(summary, ordiellipse) -S3method(summary, ordihull) -S3method(summary, permat) -S3method(summary, poolaccum) -S3method(summary, prc) -S3method(summary, procrustes) -S3method(summary, radfit.frame) -S3method(summary, simper) -S3method(summary, specaccum) -S3method(summary, taxondive) -# tail: utils -S3method(tail, summary.cca) -# text: graphics -S3method(text, cca) -S3method(text, decorana) -S3method(text, metaMDS) -S3method(text, monoMDS) -S3method(text, ordiplot) -S3method(text, orditkplot) -S3method(text, procrustes) -# tolerance: vegan -- or analogue?? Gav? -S3method(tolerance, cca) -# update: stats -S3method(update, nullmodel) -# vif: car -- but not used as a S3method within vegan -# because of car definition: could be defined as exported 'vif' generic -# in vegan with namespace -#S3method(vif, cca) -# vcov: stats -S3method(vcov, cca) -# weights: stats -S3method(weights, cca) -S3method(weights, decorana) -S3method(weights, rda) diff --git a/scTCRpy/rpackages/vegan/ONEWS b/scTCRpy/rpackages/vegan/ONEWS deleted file mode 100644 index e94a5e637..000000000 --- a/scTCRpy/rpackages/vegan/ONEWS +++ /dev/null @@ -1,2338 +0,0 @@ --*-Text-*- - - VEGAN RELEASE VERSIONS - ====================== - - CHANGES IN VEGAN 1.17-12 - - - This is a maintenance release which improves robustness of - several functions. A major release is expected soon. - - - tolerance.cca: a new function to find the species response - widths (a.k.a. tolerances) and sample heteregeneities from a - cca() result. - - - adonis: much faster. - - - betadiver: argument 'index' was renamed to 'method' so that the - function can be used similarly as dist(), vegdist() and other - distance functions. This allows using betadiver() as a distance - function in metaMDS(). - - - cca/rda/capscale: handling of aliased and other zero-rank - components changed in vegan 1.17-11, but not all support - functions were adapted to these changes in that release. Now the - following functions cope with the changes and are more robust: - capscale, anova.cca, bstick.cca, goodness.cca, predict.cca, - screeplot.cca, calibrate.cca, deviance.cca/rda, ordiplot3d, - ordiresids, ordirgl, ordixytplot. - - - isomap: text or points are plotted more cleanly, and text uses - ordilabel(). - - - make.cepnames: a bit more flexible and robust, with a new - argument that allows selecting first and second name component - instead of first and last. - - - metaMDSrotate: rotation could be slightly off with more than two - dimensions when there are NA scores. - - - ordiellipse, ordihull, ordispider, orditorp: accept NA scores. - - - ordilabel: gained argument 'select'. - - - ordiplot: better handling of graphical arguments, arg 'cex' can - be set by the user. - - - swan: gained new argument 'maxit' that allows setting maximum - number of iterations. Defaults 'maxit = Inf' or the current - behaviour of continuing as long as there are zeros that can be - filled. - - CHANGES IN VEGAN 1.17-11 - - - This is a maintenance release which improves the robustness of - several functions but introduces no new important features. - - - cca/rda/capscale: handling of aliased and other zero-rank - components changed. They are no longer made NULL and left - undisplayed, but now they are shown as zero rank. The general - output also changed so that proportions of inertia are only - shown if there are conditions or constraints, but not for - unconstrained analysis. The capscale() function no longer shows - species scores if these are unavailable. Several support - functions more robust. - - - nobs: R 2.13.0 introduced generic function nobs() to find the - number of observations, and this release provides nobs() for - several vegan results. (This does not make vegan dependent on R - 2.13.0, but vegan works with older R as well.) - - - prc: allows anova(..., by = "axis") and other 'by' cases for - prc() results. Some rda() support functions still fail, but now - they stop informatively. - - - specaccum: gained argument 'groups' which can be used to find - the number of species in subsets of the data. - - CHANGES IN VEGAN 1.17-10 - - - This is minor revision that mainly fixes vegan with respect to - changes in the currently released R 2.13.0. Most importantly, - cmdscale() output changed in R 2.13.0 and because of this - capscale() could fail in some rare situations with argument 'add - = TRUE'. This vegan bug made BiodiversityR package fail its - tests in R 2.13.0. - - - metaMDSrotate: gained argument na.rm = TRUE. - - CHANGES IN VEGAN 1.17-9 - - - anova of cca/rda/capscale results gave wrong results in partial - models. The bug was introduced in vegan 1.17-7. - - - diversity and related functions rarefy, rrarefy and specnumber - now accept vector input. Earlier a single site had to be - analysed either as a single-row matrix or using the non-default - setting MARGIN = 2. - - - drarefy: new function that returns a matrix of probabilities - that a species occurs in a rarefied sample of a given size. - - - metaMDS: it is possible to supply a starting configuration with - argument 'previous.best'. A previous metaMDS or isoMDS result can - also be given as a starting configuration. If the starting - configuration has a higher number of dimensions than requested, - the extra ones are dropped, and if the starting configuration - has fewer dimensions, random scores for extra dimensions will be - added. This may help in running metaMDS over a range of - dimensionalities. - - - metaMDSrotate: can now rotate metaMDS solutions with any number - of dimensions so that the first axis is parallel to a fitted - environmental vector. Previously, only two dimensional solutions - worked. - - - ordilabel: gained argument 'xpd' that allows labels outside the - plot area. This allows labels above axes, for instance. - - - ordisurf: gained several new arguments to control the mgcv::gam - fitting. Also gained an argument to suppress plotting, and a new - plot method. The fitted model can be specified with a formula. - - - prestonfit and friends: default is now 'tiesplit = TRUE' (which - was a new feature introduced in vegan 1.17-8). - - CHANGES IN VEGAN 1.17-8 - - - prestonfit: fixed a bug in as.preston(): the largest octave - could be missing with 'tiesplit = TRUE'. - - - decorana: spurious axes scores and eigenvalues could be reported - when the eigenvalues actually were zero. This was rarely a - problem with real data, but occurred only in arbitrary examples. - - - procrustes: checks input. - - CHANGES IN VEGAN 1.17-7 - - - anova.cca: more robust when models were fitted without 'data' - argument, or when 'na.action' or 'subset' was used. - - - capscale: implemented 'subset' in model definition. Additive - constant with 'add = TRUE' is taken into account in predict() - and fitted(). Implemented simulate() which returns a - dissimilarity matrix with random error about predicted values. - - - eigenvals: can now extract eigenvalues of some ade4 and labdsv - result objects. - - - nestednodf: did not use binary data when weighted = FALSE was - used together with order = FALSE. Reported by Daniel Spitale. - - - prestonfit: gained option to split tied frequencies (1, 2, 4, 8, - etc.) between adjacent octaves. - - - specaccum: implemented choice of using either "individuals" or - "sites" as x-axis in plot(). Corrected a typo in the result - object: now returns indeed "individuals". - - - CHANGES IN VEGAN 1.17-6 - - - capscale: vegan 1.17-5 defined total inertia as the sum of - absolute values of eigenvalues which changed the total inertia - from previous versions. This is changed so that the total - inertia is the sum of all eigenvalues, i.e., negative - eigenvalues are subtracted from the total which was the - definition in 1.17-4 and earlier. The proportions of inertia - components are now expressed for non-negative eigenvalues only, - and a new item "Real Total" (sum of positive eigenvalues) is - added to the output if there are negative eigenvalues - ("Imaginary" component). The function no longer returns the - negative eigenvalues. - - - CCorA: scaling of object scores changed: they are no longer - divided with sqrt(n-1). Minor fixes for improved robustness. - - - metaMDS: automatically changes some arguments to non-default - values if input data contains negative entries. Help page gives - advice to do so for non-community data. - - - wascores: checks that input weights are non-negative. - - NEW FEATURES AND BUG FIXES IN VEGAN 1.17-5 - - anova.cca: Empty models with no constrained component have - correct degrees of freedom. Tests 'by = "axis"' are really - marginal for all axes (first axis used to be non-marginal to - latter axes). - - - as.mlm.cca/rda, intersetcor, vif.cca: avoid bug in qr.X in R - 2.12.0 which use wrong variable names in aliased models (fixed - also in R 2.12.1). - - - betadisper: plot did not allow selecting axes. Reported by Sarah - Goslee. - - - capscale: failed with NA action (reported by Nevil Amos). Total - inertia is defined as the sum of absolute values of all - eigenvalues consistently with cmdscale (from R 2.12.1) and other - vegan functions. - - - cca/rda/capscale: print proportions of inertia components. - - - decorana: Fortran code prints warnings of failed convergence. - Default plot() uses points if items have no name labels to be - plotted. - - - deviance.cca/rda: return 0 (instead of NA) for over-paremetrized - models with no unconstrained component. - - - eigenvals: uses sum of absolute values of eigenvalues in models - that may have negative eigenvalues (cmdscale, wcmdscale, - capscale). Can extract eigenvalues of pcnm() and cmdscale() of R - 2.12.1. Uses zapsmall() to print near-zero - eigenvalues. - - - mantel.correlog: P-values could slightly off. The function uses - internally mantel() to evaluate the statistic and its - significance. The mantel() function adds one both to the - denominator and numerator, but mantel.correlog() did not notice - this and made the addition for the second time. When the - mantel.correlog() reported a positive value of the statistic, it - reversed the direction of the one-sided test of mantel(), but - did not handle tied values correctly in this reversal. - - - nestednodf: Gustavo Carvalho's new version that also implements - a new quantitative method of Almeida-Neto & Ulrich (Env Mod - Software, 26, 173-178; 2011). - - - oecosimu: takes care that the statistic is evaluated with binary - data when null models are binary (regression introduced in vegan - 1.17-0). Can handle NA values in permutations (as na.rm = TRUE). - - - ordilabel: gained new argument 'col' to set the text colour of - the labels separately from 'border'. Function ordiellipse() uses - this in filled polygons to set the colour of labels to that of - borders or foreground instead of the colour of the fill. - - - ordiR2step: new function to model selection in rda() and - capscale() based on adjusted R2 following the recommendations of - Blanchet, Legendre & Borcard (Ecology 89, 2623-2632; 2008). - - - ordistep: correct a name clash when the fitted model had term or - item called 'mod'. Reported by Richard Telford (Bergen, Norway). - - - pcnm: returns the truncated distanced matrix with argument - 'dist.ret'. - - - prc: rewritten by Cajo ter Braak with a fix to scaling of - coefficients for full compatibility with Canoco. - - - procrustes: Fixed translation following the report and fix by - Christian Dudel (Bochum, Germany). New predict() method to - rotate 'newdata' points to the target. Graphics with plot() - select the direction of arrows with new argument 'to.target', - and use ordilabel() to display the labels with 'type = "t"'. - - - rankindex: can take a list of dissimilarity functions as the - argument to allow the use of other dissimilarity functions than - vegdist() of vegan. - - - rda/capscale: the scores() can take two scaling constants - ('const'). One is used for species, second for WA and LC scores - of sites. This allows using scalings of prcomp(), princomp() or - Canoco (like documented in the design decisions vignette). - - - swan: disconnected data caused infinite loop. Now zeros are left - to the result with disconnected data. - - - treedist: can handle empty or one-node trees. Gained new - argument 'relative' (defaults TRUE): if FALSE, finds raw - dissimilarities of tree heights. Help page tells that relative - tree distances are in range 0..2 instead of 0..1, since - combining two trees may add a new common root. - - - treedive: handles trivial cases of zero (diversity NA) or one - species (diversity 0). - - - vegdist: help page gives now binary variants of the indices - using designdist() notation. - - - vif.cca: can handle models with aliased terms. - - - wcmdscale: returns goodness of fit statistic (item GOF) and - handles negative eigenvalues consistently with cmdscale() and - capscale(). - - NEW FEATURES AND BUG FIXES IN VEGAN 1.17-4 - - - MOStest: a new set of functions to implement generalized - Mitchell-Olds & Shaw test for the location of the quadratic - extreme in a given interval. The test can also be used for the - location of the optimum of the Gaussian response model. In - addition to the basic test, there are Fieller and deviance - profile methods for the confidence interval of the location of - the quadratic extreme or Gaussian optimum. - - - mrpp & meandist: mrpp() does not evaluate the Classification - Strength (CS) of Van Sickle & Hughes (J. N. Am. Benthol. Soc., - 19: 370-384; 2000) any longer. The old version misinterpreted - the weighting scheme of CS, and with correct scheme there is no - exact relationship between CS and the corresponding MRPP - statistic and therefore permutation tests are not available. CS - is still evaluated in meandist(), but with corrected weighting - scheme, and no tests are performed. Reported by Dr John Van - Sickle (Corvallis OR). - - - msoplot: legend text was in a wrong order. Reported by Daniel - Borcard. - - - vegdist: Marti Anderson variant of the Gower distance (method = - "altGower") should be without range standardization. Reported by - Sergio Garcia. - - - ordiellipse: labels disappeared in their background with 'draw = - "polygon"'. - - - predict.cca and predict.rda: gained argument 'newdata' for 'type - = "response"' and 'type = "working"'. These return estimates - of the response data, or dissimilarities in capscale(). - - - NEW FEATURES AND BUG FIXES IN VEGAN 1.17-3 - - - adonis: handles ties correctly. - - - anova.cca and permutest.cca: permutations of cca result is - faster and now nearly equally fast (or slow) in rda and cca. - - - betadisper: 'type = "median"' (the default) was not computing - the spatial median on the real and imaginary axes separately. - Reported by Marek Omelka. - - - cca, rda and capscale failed when Condition() was a factor, but - the constraints had only continuous variables. - - - envfit: defaults to use 999 permutations instead of skipping - permutations. - - - mantel and mantel.partial: faster permutation. - - - mantel.correlog: upgraded and faster. - - - ordiarrows, ordisegments and ordispider gained argument 'label' - to label the groups corresponding to drawn arrows or lines. - - - ordiresids: de-weights residuals and fitted values in CCA so - that these are identical to the values shuffled in simulate.cca. - - - permutest.cca: adds observed statistic among permutations when - printing the result (does not influence anova.cca or - calculations). - - - RsquareAdj: The 'rda' method used wrong number of degrees of - freedom in rank deficit models (number of dependent variables - was lower than the rank of constraints and conditions). Default - method handles vector input. - - - scores: default method works with one row of scores. The scores - of "cca" and "rda" methods always have names, even if there are - no names in the input data. - - - Permutation tests: The available permutation tests are described - in a new help files accessed via ?permutations. - - NEW FEATURES IN VEGAN 1.17-2 - - - permutest.betadisper: printed P-values in a wrong order in - displaying the the matrix of pairwise tests (the values were - correct, but formatting failed). Reported by Dan O'Shea. - - - nesteddisc: failed if the most species rich sites were tied. The - function is now much faster (though still slow), but the price - is that it does not try as hard to find the optimal ordering. - - - screeplot.cca & friends: new argument 'legend' for all methods - (except 'decorana') to draw a legend if the observed and broken - stick distribution are both plotted. - - - ordistep: adds an 'anova' item to the final model similarly as - the standard step(). You can suppress the tracing ('trace = - FALSE'), and find the model build history save in 'anova' in a - compact form. - - - densityplot.oecosimu: gave warnings when there was only one - statistic and hence one lattice panel. - - - predict functions for cca and rda objects match 'newdata' by - dimension names instead of index. - - - simulate.rda and simulate.cca have new argument 'rank' which - allows using lower rank presentation of fitted values - (including rank = 0). - - - treedist: new function to find dissimilarities of species - property trees of communities. The property trees can be, e.g., - functional diversity trees, taxonomies or phylogenies. Similar - in spirit to UniFrac distance (C. Lozupone & R. Knight, Appl - Environ Microbiol 71:8225-8235; 2005), but completely different - in design and works only with binary data. - - NEW FEATURES AND FIXES IN VEGAN 1.17-1 - - - multipart: new functions for multiplicative partionining of - gamma diversity into alpha and beta diversity components. - - - CCorA: Fixed choice of scores in biplots -- Used PC scores - instead of observed scores in the right-hand-side biplot - panel. The biplot function has several new and enhanched - alternatives of plots. - - - envfit: ignored weights in cca() results for factors or a single - continuous variable. The bug was introduced with NA handling - upgrade in vegan 1.17-0. The problem with fitted vectors was - reported by Richard Telford (Bergen, Norway). - - - CHANGES AND NEW FEATURES IN VEGAN 1.17-0 - - - Guillaume Blanchet joined the vegan team. - - - New function to partition data-set diversity (gamma) into - within-plot (alpha) and between-plot (beta) diversity - components. Function adipart performs additive - partitioning (gamma = alpha + beta). Function hiersimu performs - hierarchical null model testing similar to adipart but by using - custom function to calculate statistics for levels of a hierarchy. - - - Subsets and missing value handling added to constrained - ordination methods cca(), rda() and capscale(). The missing - values (NA) can be handled with setting na.action (defaults - na.fail). With na.action = na.omit, observations with missing - values are removed, and with na.action = na.exclude they are - kept, but scores may be NA. However, the WA scores for sites are - available in non-partial models with na.action = na.exclude. - The 'subset' can be defined using any variable in the - constraining data set or species in the dependent data. - - - Functions for fitting environmental variables onto ordination - (envfit, ordisurf) are aware of missing values in constraints or - NA values in scores. The ordination plot functions also can - handle NA scores. - - - New and upgraded quantitative null models. In particular, - quantitative swap models (function abuswap) allows generetating - null matrices where marginal totals and fill are fixed, or row - and column fills are fixed, or row and column fills and either - row or column totals are fixed. - - - oecosimu: rewritten to handle quantitative null models. Gained - keyword 'alternative' for "two-sided", or "less" and "greater" - one-sided tests. More robust with degenerate solutions. The - 'method' can now be a user-supplied function. New support - functions as.ts() and as.mcmc which transform sequential models - into form that can be analysed using tools for time-series - (as.ts()) or MCMC sequences of the 'coda' package (as.mcmc). - - - calibrate: calibrate is now a generic function with a new method - calibrate.ordisurf() in addition to the old calibrate.cca. These - find the estimates of environmental variables from ordination. - - - ordistep: stepwise selection of terms in constrained ordination - (cca, rda and capscale) using permutation tests instead of - pseudo-AIC that is used by the standard step() function. - - - pcnm: new function to find weighted principal coordinates of - neighbour matrix (PCNM) from distances between points. These are - typically used for spatial filtering in constrained - ordination. The function uses weighted analysis and can - therefore produce PCNM for correspondence analysis in addition - to PCA and RDA. - - - betadisper: can use spatial medians which are now the default - method. Preliminary tests indicate that spatial medians correct - the anti-conservative Type I errors reported by Stewart - Schultz. - - - decostand & vegdist: new transformation 'log' which implements - Marti Anderson's scaling log(x) + 1 for x>0 (which is not at all - the same as log(x+1)), and vegdist has alternative Gower - function that skips double zeros. Together these implement the - "modified Gower" distance of Anderson et al. (Ecology Letters 9, - 683-693; 2006). Feature request #473 by Etienne Laliberte in - vegan.r-forge.r-project.org. - - - model.matrix.cca & model.frame.cca: new functions to reconstruct - the model frame and model matrix (model matrices in partial - models) of constraints used in ordination methods cca(), rda() - and capscale(). - - - simulate.cca & simulate.cca: simulate the community (response) - data from the result of cca() or rda() under alternative - hypothesis. Error is added to the fitted values from - ordination. The function uses either Gaussian error or permutes - residuals and adds these to fitted values. - - - spandepth: a new function to find the depth of each node in the - minimum spanning tree produced by spantree(). Feature request by - W. E. Sharp. - - - alias.cca: gained argument names.only. - - - metaMDSrotate: a new function to rotate metaMDS so that first - axis is parallel to an environmental variable. - - - msoplot: uses standard legend. - - - nesteddisc: new method that tries to find the smallest possible - value of the statistic in tied data. Slow, but fixes the - problems of the published method. The problem with tied values - in nesteddisc was found with Carsten Dormann. - - - contribdiv: gained a plot method. - - - rarefy: failed with one site and many sample sizes like - rarefy(rpois(10, 2), sample=2:3). - - - meandist: plot can draw histograms as an alternative to - dendrograms. - - - plot functions for 'decorana' and 'cca' and friends and - 'ordiplot' use 'linestack' if only one axis was requested. - - - CHANGES IN VEGAN 1.15-4 - - - Changed package dependence: vegan does not depend on 'ellipse'. - - - anosim: user interface identical to 'mrpp'. Accepts now data - matrix and finds dissimilarities internally. - - - betadisper: fix removal of zero eigenvalues with non-Euclidean - distances. This may change the results slightly, but in most - cases the effects are minor or none. - - - capscale: has now 'fitted' and 'residual' methods, and 'predict' - works with 'type = "response"'. These return dissimilarities - that produce the same ordination as the original data. - - - indpower: new function to find the indicator power of species to - predict presence of other species (Halme et al., Conservation - Biology 23, 1008-1016; 2009). Closely related to the the power - to predict probabilities in beals(). - - - mantel.correlog: new functions to produce multivariate Mantel - correlograms (Legendre & Legendre, Numerical Ecology, section - 13.1.5; 1998). - - - metaMDS: accepts user-supplied dissimilarities. Species scores, - data transformation, step-across and half-change scaling are - unavailable with user-supplied dissimilarities, but random - starts, PC rotation and scaling to original range of input - dissimilarities work. - - - nestedtemp: row and column labels can be turned on/off - independently in plots. - - - ordihull & ordiellipse: new argument 'label' to plot group names - for hulls or ellipses. Return (invisibly) the data for plotted - convex hulls or ellipses. In 'ordihull' this is a list of hull - vertices, and in 'ordiellipse' a list of (scaled) covariance and - centre data for ellipses. New 'summary' methods find the centres - and areas of plotted hulls or ellipses. Argument 'draw' has new - option "none" that suppresses all drawing so that only the data - for summary can be extracted without plotting. - - - orditorp: works with reversed axes, like with 'xlim = c(3, -3)'. - - - ordixyplot: has a panel function for arrows: a lattice variant - of ordiarrows. - - - poolaccum, estaccumR: new functions to find the specpool() or - estimateR() estimates of extrapolated species richness in random - accumulations of sites. These have 'plot' and 'summary' methods. - - - scores: functions biplot, points, text, ordilabel, ordiplot3d, - ordixyplot and spantreee did not pass all arguments to scores() - function. In particular, this concerned rda where 'scaling' and - 'const' arguments could not be used within these functions. - - - radfit: new function 'radlattice' for a lattice plots of fitted - models for a single site. - - - rrarefy: a new function to generate random rarefied communities. - - - RsquareAdj: now a generic function to find adjusted R squared - with special cases to 'rda', 'cca', 'lm' and 'glm'. - - - summary.cca: cleaner output. - - - wcmdscale: returns scaled scores for axes with negative - eigenvalues. - - - CHANGES IN VEGAN 1.15-3 - - - anosim, mantel, mrpp, protest, envfit: did not include the - observed statistic among permutations. Functions adonis and - permutest.cca did this correctly, but did not print results - neatly. - - - anova.cca: name clash if data were indexed with 'i'. - - - capscale: fixed handling of negative eigenvalues with - non-Euclidean distances. The total inertia is the sum of all - eigenvalues so that negative eigenvalues are subtracted from the - total. The total inertia of negative components and their rank - (number) is given as 'Imaginary' component, and the negative - eigenvalues are listed after unconstrained positive eigenvalues. - The procedure is based on Gower, Linear Algebra and its - Applications 67, 81-97 (1985). New argument 'sqrt.dist' takes - square root of the internally calculated dissimilarities and - avoids negative eigenvalues with some indices, such as vegan - Jaccard and Bray-Curtis. The adjustment is corrected for indices - with upper limit of one, and the reported eigenvalues and - inertia components are reduced by a factor of sqrt(n-1) and are - similar to those reported by 'cmdscale' or 'wcmdscale'. - - - eigenvals: a new function to extract eigenvalues from rda, cca, - capscale, wcmdscale, prcomp, princomp, svd or eigen. If the - result object contains squareroots of eigenvalues, they are - squared. The summary method also finds proportions and - cumulative proportions explained. Function summary.cca now uses - this to display eigenvalues. - - - kendall.global: could get wrong counts of ties in large data - sets. - - - meandist: new sister function to 'mrpp' that finds mean - within-group and between-group dissimilarities. The summary - function finds overall averages of these, and returns all three - MRPP variants plus classification strength. The plot method - draws a dendrogram based on the mean dissimilarity matrix, with - leaves hanging to within-groups dissimilarity. The functions - follow Sickle, J. Agric. Biol. Envir. Stat. 2, 370-388 (1997). - - - ordisurf: fits now linear or quadratic trend surfaces if - 'knots' argument is set to 0, 1, or 2. - - - orditkplot: copes with NA and NaN scores. - - - ordixyplot: mixed x and y axes for biplot arrows and class - centroids. Function failed in constrained ordination. - - - tsallis: gained argument 'hill' to find results analogous to - Hill numbers in renyi(), or the species number equivalents of - indices. - - - wcmdscale: removes now zero eigenvalues instead of the last - eigenvalue. The bug was copied from cmdscale(), which still has - the bug in R 2.9.0 (plus another that was not copied to - wcmdscale). - - - CHANGES IN VEGAN 1.15-2 - - - adonis: adds one to numerator and denominator of permutation - tests, the default number of iterations was raised from 5 to - 999, and the result object got a 'terms' component. Uses much - less memory allowing analysis of larger problems. - - - anova.cca: anova(..., by = "axis") gained new keyword 'cutoff' - to stop permutation tests after exceeding the given cut off - level of significance. The second term of anova(..., by = - "margin") used different random numbers than other terms. - - - beals: Completely rewritten by Miquel de Caceres. Knows now - also the cross validated version of Beals smoothing and other - choices described by De Caceres & Legendre, Oecologia 156, - 657-669; 2008. - - - betadisper: handles missing values both in dissimilarities and - grouping. - - - cca/rda: cleaner output of summary() in unconstrained models. - - - commsimulator: simulated result retains original row and column - names. - - - contribdiv: new function for contribution diversity (Lu et al., - Basic and Applied Ecology 8, 1-12; 2007). - - - decostand: gained dots in argument list, so that - stressplot(metaMDS(x, dist = "gower", trymax = 40)) works. - - - dispindmorisita: new function for the Morisita index of - dispersion. See Krebs, Ecological Methodology; 1999. - - - kendall.global and kendall.post: new functions for Kendall's - coefficient of concordance. In ecology these can be used to - identify significant species associations (Legendre, J Agric - Biol Environm Stat 10, 226-245; 2005). - - - metaMDS: more robust with distances like Euclidean and Manhattan - which have no upper limit. The stepacross works correctly for - these, but gives a warning that its use may not be sensible. - There is a better heuristics to avoid half-change scaling with - these indices. The 'halfchange' argument is now honoured when - given in metaMDS() call. - - - mrpp: returns within-class dissimilarities. Evaluates - classification strength (Van Sickle, Biological and - Environmental Statistics, 2, 370-388; 1997) if weight.type = 3 - was used. - - - nestednodf: new nestedness function of overlap and decreasing - fill (Almeida-Neto et al., Oikos 117, 1227-1239; 2008). Coding - by Gustava Carvalho. - - - ordirgl: no more superfluous warnings with type = "t". - - - ordisurf: gained an argument 'bubble' to draw bubble plots, or - vary point sizes according the value of the observed variable. - The (invisible) return object now has an item 'grid' for the - fitted value and grid values. - - - orditkplot: can produce TIFF graphics, if installed R has this - capability. - - - procrustes: new text() function. - - - radfit: broken-stick model (function rad.null) failed with - Gaussian and Gamma error families. The plot.radfit command - gains argument log = "y", which allows using arithmetic scales - or log-log scales where Zipf model is a straight line. - - - spantree: retains names and uses labels in plot. - - - datasets: Oribatid mites (data 'mite') got taxon names. - - - help files updated, and do not raise errors or warnings with the - Rd parser version 2 in R 2.9.0. - - CHANGES IN VEGAN 1.15-1 - - - betadisper and related functions now work when the 'group' - argument represents a single group/level. - - - decorana: 'text' function failed. - - - cca/rda/capscale: cleaner handling of results with missing - (NULL) row or column names, also in plots. - - - cca/rda/capscale: new functions 'head' and 'tail' for 'summary' - of cca results to show only some rows of the scores. - - - nestedness: method nesteddisc was found to be strongly dependent - on the ordering of tied column (species) frequencies. A warning - is now issued, and development version of vegan in - http://vegan.r-forge.r-project.org/ has an experimental function - that tries to find the optimal ordering. - - - permatfull: 'row'/'column' arguments were mixed. - - - radfit: all methods work consistently for communities of 0, 1 or - 2 species. - - - wcmdscale: typo in the result object items corrected. - - CHANGES IN VEGAN 1.15-0 - -GENERAL - - - Peter Solymos joined the vegan development team. - -NEW FUNCTIONS - - - add1.cca and drop1.cca: functions to implement permutation tests - with argument test = "permutation". Function drop1.cca uses - anova.cca(..., by = "margin") and add1.cca implements a new type - of analysis for single term additions. - - - ordilabel: new alternative for cluttered ordination plots. Text - is written on opaque labels. The texts still cover each other, - but at least the uppermost are readable, and the ordering can be - controlled by 'priority' argument. Similar to 's.label' in ade4 - package. - - - ordipointlabel: new alternative for cluttered ordination plots. - Points are in fixed positions, but their text labels are - located to avoid overplotting. The optimization uses simulated - annealing of 'optim' function. Returns an "orditkplot" object so - that the results can be edited with 'orditkplot'. Similar to - 'pointLabel' function in maptools package. - - - permatfull, permatswap: functions to permute quantitative count - data. - - - treedive: estimation of functional diversity defined as the - height of the dendrogram of species properties (Petchey & Gaston, - Ecology Letters 9, 741-758; 2006). With a helper function - 'treeheight' to find the height of a 'hclust' dendrogram. - - - tsallis: Tsallis entropy family as an alternative to 'renyi'. - - - wcmdscale: weighted metric scaling a.k.a. weighted principal - coordinates analysis; only uses row weights. - -NEW FEATURES AND FIXES - - - anova.cca: default permutation model changed from "direct" to - "reduced" after Pierre Legendre demonstrated that model - "reduced" was superior in term of Type I error. - - - anova.cca: handles smoothly models where constrained or - unconstrained models are NULL and the tests are impossible (used - to stop with error). - - - anova.cca(..., by = "margin") was handling wrongly 'x' in - '~Condition(x) + x + z', or model formulae where same variables - where used both as conditions and (aliased) constraints. - - - cca/rda/capscale: improved robustness in the formula - interface. Partial handling of "cca" objects produced by - ade4:::cca. - - - commsimulator: swap, trial swap and quasiswap written in C and - *much* faster (100x in some tests). - - - oecosimu: accepts now a vector of statistics, and the user can - give the name of the statistic in the function call. - - - ordiplot: did not use partial matching for "sites" and - "species". - - - orditkplot: Improved user interface. Improved zooming into - graphs. Imitates R plotting characters ('pch'). Label font - family, size and font type can be vectors. - - - permuted.index2 and associated functions allow for restricted - permutations of strata (i.e., restricted shuffling of blocks). - - - specaccum: removes missing (all zero) species which gave sd = NA - with method = "exact". - - CHANGES IN VEGAN 1.13-2 - - - anova.cca (and permutest.cca) now calculate the residual df as - (number of rows) - (rank of constraints and conditions) - 1 - instead of using the rank of the residual ordination. With - single response variable in 'rda' the degrees of freedom and - F-values are now identical to those in 'lm' (linear - model). However, the change does not influence significances - (P-values), because these have been always found by permutation, - and this change does not influence the order statistic used in - permutations. - - - oecosimu: always estimates the original statistic with binary - data even when the user supplies quantitative data. - - - orditkplot: no superfluous pointer lines when moving labels. - - - procrustes plot failed if two configurations were exactly - identical. - - - rda (and capscale) use internal scaling constant so that the - returned site and species scores with scalings 1, 2 or 3 - together provide a biplot approximation of the original - data. This scaling constant is calculated internally, but now - its numerical value is returned as an attribute of 'scores.rda', - and 'summary' displays its value. Vignette on "Design decisions - and implementation" in vegan explains the calculation of the - internal scaling constant. - - - summary of capscale identical to the summary of rda. - - - scores of rda and capscale with scaling = 0 really return the - unmodified scores without the scaling constant. - - CHANGES IN VEGAN 1.13-1 - -GENERAL - - - Helene Wagner joined the vegan team. - -FIXES FOR HANDLING RANKS IN CONSTRAINED ORDINATION - - - cca, rda and capscale had only known one kind of rank: the rank - of the ordination result (= number of axes). In fact there are - two other types of ranks: the rank of constraints after removing - conditions, and the rank of constraints + conditions, where - conditions refer to terms "partialled out". This hit those that - tried to use rda with single response variable instead of - community matrix, and also some partial models were handled - wrongly. The changes mainly concern cases where the rank of - constraints is higher than the rank of the ordination (more - constraints than ordination axes). The changes have visible - effects in following support functions: - - - alias: no superfluous aliasing of terms, partial models aliased - correctly. - - - anova: always uses rank of the constraints which also fixes - anova(..., by = "terms") when single response variable was used. - - - calibrate.cca: identifies cases when the rank of constraints is - higher than the rank of ordination and refuses to analyse these. - - - extractAIC: uses rank of constraints for degrees of freedom. - - - predict: predict(..., type = "lc", newdata = somedata) works in - partial analysis. - -OTHER FIXES - - - adonis: faster and improved documentation. - - - intersetcor and inertcomp now check that input really is from - constrained ordination instead of giving obscure error messages. - - - lines.spantree knows again graphical arguments such as 'col', - 'lty'. - - - mite.xy: new data set on the spatial coordinates of sample sites - of Oribatid mites. - - - mso: plot.mso replaced with msoplot, and plot function now - displays the ordination scatter plot. Function msoplot collapses - distances larger than half of the maximum distance into a single - distance class. Printed result shows the variogram data. - - - ordicluster, ordiellipse, ordispider, orglspider, ordisurf, - factorfit and vectorfit could fail with non-vegan ordination - objects in R 2.7.0 whose weights.default function gave error if - object had no weights (these worked in R 2.6.2 and earlier). - - - taxa2dist: has a "method" name for distances. - - - CHANGES IN VEGAN 1.13-0 - -GENERAL - - - Based on development version 1.12-15 (revision 354 at - http://vegan.r-forge.r-project.org) - -NEW FUNCTIONS - - - betadiver: beta diversity functions as reviewed by Patricia - Koleff et al. (J. Anim. Ecol., 72, 367-382; 2003), with a plot - function to produce triangular plots. - - - mso: Helene Wagner's multiscale ordination or spatial - partitioning of cca and rda. This is taken from the Ecological - Archives with minimal edition with the permission of Helene - Wagner. - - - nestedtemp: matrix temperature method for oecosimu following - Rodriguez-Girones & Santamaria (J. Biogeogr. 33, 924-935; 2006), - but still without iterative optimization of row and column - ordering. - - - TukeyHSD.betadisper: pairwise comparisons for betadisper. - -NEW FEATURES AND FIXES - - - adonis: returns both species and site scores. - - - betadisper: was not calculating distance to centroid correctly - for observations where the imaginary distance to centroid was - greater than the real distance. Now takes the absolute value of - the combined distance before taking the square root. This is - in-line with Marti Anderson's PERMDISP2. - - - BCI: example has spatial coordinates of plots. - - - biplot.rda: argument for arrow col. - - - capscale: accepts other distance functions than vegdist, and can - use metaMDSdist for extended dissimilarites & "metaPCoA". - - - designdist: knows 2x2 contingency table notation with a, b, c, - and d. - - - metaMDS: transforms data like with distances for species WA. - - - orditkplot: allows editing labels and zooming into plot. - - - permDisper: renamed to permutest.betadisper. - - - permutest: now a generic function, currently with methods - 'cca' and 'permDisper'. - - - rarefy: accepts vectors of sample sizes. - - - rgl.isomap: dynamic rgl plots for isomap. - - - screeplot.cca etc: return invisibly xycoords. - - - specaccum: returns numbers of individuals with method = "rarefaction". - - - summary.cca: returns more statistics on "variance explained by axes". - - - zzz: vegan got startup message. - - - CHANGES IN VEGAN VERSION 1.11-4 - - - A critical bug fix in adonis: there was a critical bug in adonis - code, and inconsistent statistics were used in permutations so - that P-values were grossly wrong in multi-variables models - (single variable models were OK). In addition, df were wrong in - deficit rank models, and unused factor levels were not dropped. - All adonis users should upgrade and rerun their analyses. - - CHANGES IN VEGAN VERSION 1.11-3 - -GENERAL - - - Bug fixes from Rev. 305 on http://r-forge.r-project.org/. - -FIXES - - - anova.cca: number of permutations could exceed perm.max. - - - permuted.index2: updated to the version in devel branch. This - means bugfixes in permCheck, numPerms and permuted.index2. It - also adds allPerms to get all possible permutations when - complete enumeration is feasible. In addition, there is now a - function permuplot to graphically show the current permutation - scheme. - - - plot.cca, plot.envfit and associates: automatic scaling of - biplot arrows and fitted vectors was wrong when axes were - reversed (like 'xlim = c(1,-1)') or the origin was shifted in - plot.envfit (like 'at = c(1,1)'). Added internal function - 'ordiArrowMul'. - - - plot.procrustes: failed if two configurations were identical. - - - varpart4: sum of squares was wrong if called directly instead of - being called via varpart(). Reported by Guillaume Blanchet. - - CHANGES IN VEGAN VERSION 1.11-2 - -GENERAL - - - minor bug fixes and documentation updates from the devel trunk. - - - version 1.11-1 was made but never released to CRAN, and this is - the first minor release of the 1.11 series. - -FIXES AND UPDATES - - - bstick.princomp: works now. - - - numPerms: was returning incorrect number of permutations - when type = "strata" selected. - - - permuted.index2: was permuting samples within levels of strata - as well as permuting the levels themselves if type = "strata" - selected. - - - Documentation: diversity-vegan gains discussion on taxonomic - diversity and using designdist for analysing beta diversity. - Proof-reading and updates in diversity-vegan and FAQ-vegan. - - - CHANGES IN VEGAN VERSION 1.11-0 - -GENERAL - - - Based on devel version 1.10-13 (rev. 205 at R-Forge). - - - Gavin Simpson joined the vegan team. - - - Suggests now 'tcltk' (for orditkplot). - -NEW FUNCTIONS - - - anova.cca gained a new support function to analyse marginal - effects of individual terms (which are similar to Type III - effects). Defined with argument 'by = "margin"'. - - - betadisper: new functions for Marti Anderson's analysis of - homogeneity of multivariate dispersions. - - - biplot.rda: biplot function for PCA run with rda. Arrows are - used instead of points. - - - CCorA: Canonical correlation analysis with a robust algorithm, - with permutation test and plot function. - - - oecosimu: functions to analyse nestedness of communities (such - as on islands or patches). Function oecosimu is a general - wrapper, and commsimulator generates null-communities of various - types (r00, r0, r1, r2, c0, swap, trial swap, backtracking, - quasiswap). The nestedness can be analysed with functions like - nestedchecker (number of checkerboard units), nestedn0 (measure - N0), nesteddisc (discrepancy), but users can supply their own - functions or even use some standard R functions such as - chisq.test. - - - ordiresids: similar diagnostic plots as in plot.lm for - constrained ordination: Residuals ~ Fitted, sqrt(abs(Residuals)) - ~ Fitted, and qqmath(~ Residuals) using Lattice graphics. - - - orditkplot: interactive and editable plotting function. Function - displays one set of points (species, sites) using both points - and labels (text). The points are fixed, but labels can be - dragged to better positions with mouse. The edited plots can be - saved as EPS, exported to various graphical formats (EPS, PDF, - PNG, JPEG, BMP, XFIG depending on the system) or dumped back to - the R session for plotting and further processing. - - - ordixyplot: a set of functions for Lattice graphics of - ordination results. Includes ordixyplot for 2D graphics, - ordisplom for pairs plots, and ordicloud for 3D graphics. All - can be subsetted and formatted in the usual Lattice way. - - - permuted.index2: New version of permuted.index() that now allows - restricted permutations. Can produce permutations for - time-series or line transects and for spatial grids. These can - also be nested within 'strata'. permuted.series() and - permuted.grid() are the relevant workhorse - functions. Permutation options are set by new function - permControl(). Currently used only in betadisper, but we plan to - migrate vegan functions to permuted.index2() in the devel - version, and will eventually replace the current - permuted.index(). With support function permCheck for checking - permutation schemes. - -NEW DATA SETS - - - sipoo: birds in the Sipoo archipelago (Finland, too close to - Helsinki). - -NEW FEATURES AND FIXES - - - adonis: accepts any 'dist' object as input. - - - as.mlm.cca, as.mlm.rda use now correct names for variables in - aliased models. The data were pivoted correctly in R, but the - labels were not. - - - anova.cca assesses now P value as (hits+1)/(tries+1). - - - anova.cca: anova(..., by = "axis") failed when fitted model had - terms like poly(x,2) or log(x). - - - bgdispersal uses now a more powerful statistic for the McNemar - test (in terms of Type 1 error rate). - - - calibrate.cca does correct pivoting in aliased models. - - - capscale: negative scaling in plot works similarly as in rda. - - - decorana does not crash R when called with NULL row data, such - as decorana(dune[FALSE,]). Method predict(..., type="sites") - works correctly with downweighted analysis. - - - fitted.cca, fitted.rda gained argument type = "working" to get - the fitted values and residuals used internally in calculation - (in cca() these are weigthed and Chi-square standardized - values). - - - isomap checks that input data are dissimilarities or can be - changed into dissimilarities without warnings. - - - metaMDS gains argument wascores (defaults TRUE) to suppress - calculation of species scores. - - - orditorp now handles "..." more cleanly. - - - scores.cca, scores.rda accept display = c("species", "sites"). - - - summary.prc honours argument 'axis'. - - - taxa2dist issues now a warning if called with 'check = FALSE' - and some distances == 0, typically meaning that basal taxa - (species) were not coded. - - - varpart failed if there were unused levels in factors. - - - wascores returns now NA for missing (all zero) species instead - of failing. - -DOCUMENTS - - - new documents: FAQ, a simple introduction to ordination in - vegan, a detailed explanation of diversity methods. New - formatting. - - - Added these NEWS. - - OLDER VEGAN VERSIONS - -Version 1.8-8 (Oct 2, 2007) - - * Minor bugfix release for upcoming R-2.6.0. Based on the - http://r-forge.r-project.org/projects/vegan/ revision 17 (= 1.8-7) - with ported bug fix revisions (see below for revision numbers). - - * anova.cca: by = "term" failed in partial model. This was broken - in 1.8-6 by introducing a test against deficit rank models (r47). - - * cascadeKM: Calinski index works now when the input data is a - data.frame (r57:58). - - * flush.console: metaMDSiter and bioenv use now flush.console() - so that Windows people also see the trace (r56). - - * ordispantree: made defunct, was deprecated in 1.8-1 (r38). - - * scores: handles now numeric data frames (r25). - - * summary.cca: failed if only one type of scores was requested - (r50, 52). - - * taxondive: Fixed dim checking and matching species names in - community data and taxonomic distance data (r21). - - * tweaks to pass --pedantic R CMD check, mainly in formatting - source files, unused variables in source files and superfluous - braces in help files (r39, r46, r62). - - * Updated FAQ-vegan.pdf to the current version at R-Forge. - -Version 1.8-7 (August 24, 2007) - - * Based on devel version 1.9-34. - - * DESCRIPTION: M. Henry H. Stevens (Miami University, Oxford, - Ohio) joined the vegan team. - - * adonis: new function for nonparametric MANOVA that is - appropriate for even extremely wide matrices sometimes associated - with gene data and with diverse ecological communities. Author - Hank Stevens. - - * taxondive: a new function for indices of taxonomic diversity and - distinctness after Clarke & Warwick (Mar Ecol Prog Ser 216, - 265--278, 2001 and other papers). With a helper function - 'taxa2dist' to turn taxonomies into distances with an option for - variable step length (Clarke & Warwick, Mar Ecol Prog Ser 184, - 21--29, 1999), and a toy data set on the taxonomy of dune meadow - species ('dune.taxon'). With a help, testing and pressure from - Mike Cappo, James Cook Uni, Qld. - - * bgdispersal: previous version was partly garbled (by me), and - P. Legendre provided a corrected one. - - * designdist: keeps 'dist' attributes even when the 'method' - function drops them. Swapped the order to (terms, methods) in the - default name. - - * metaMDS: issues a warning if data are disconnected. Passes extra - arguments to other 'distfun' than 'vegdist' so that you can set - 'terms' in 'designdist' etc (metaMDSdist). Can now do trymax=0 or - skip random starts and give you enhanced 'isoMDS' result - (metaMDSiter). - - * ordiplot: failed if number of species was equal to number of - sites (and so did plot.metaMDS and plot.isomap using this). - - * plot.profile.fisherfit: corrected a harmless error detected by - checkUsagePackage(). - - * predict.rda: removed some dead (but heavy) code from type = - "response". - -Version 1.8-6 (May 9, 2007) - - * Based on devel version 1.9-23. - - * as.mlm.cca, as.mlm.rda: new functions to refit constrained - ordination result (cca, rda, capscale) as a multiple response - linear model. You can find influence statistics (Cook's distance, - hat values) from the refitted model. You also can find t-values - etc., but these have the same bias as in other software and should - not be used. - - * bgdispersal: a new function for dispersal direction in - biogeography (Legendre & Legendre 1998, section 13.3.4). Author - Pierre Legendre. - - * designdist: a new function for defining your own dissimilarity - index or for estimating beta diversity (Koleff et al., - J. Ecol. 72, 367-382; 2003). - - * isomap: a new function for isometric feature mapping of - Tenenbaum et al. (Science 290, 2319-2323; 2000). - - * screeplot, bstick: new functions to draw screeplots of vegan - ordination results with brokenstick lines, and alternative - screeplot functions for prcomp and princomp with brokenstick. - Author Gavin L. Simpson. - - * swan: a new function for the degree of absence (Swan 1970, - Ecology 51, 89-102). - - * anova.cca: now refuses to do 'by = "terms"' if the rank of - constraints is higher than the rank of the community matrix. - - * bioenv: gains argument 'partial' to perform partial bioenv. - - * cca, rda, capscale: can now handle longer expression within - 'Condition()' (ordiParseFormula). Used to drop observations with - missing values in unused variables (ordiGetData). - - * goodness.cca, goodness.rda. 'statistic = "distance"' was wrongly - implemented. Now refuses to find "distance" in constrained - analysis: distances of constrained and unconstrained components do - not add up to to distances in unconstrained ordination. - - * metaMDS (metaMDSdist): gains argument 'distfun' to use other - dissimilarity functions than vegdist. - - * renyiaccum: used a variable that was not defined as an argument. - Added support functions persp.renyiaccum and rgl.renyiaccum - (Roeland Kindt). - - * stressplot: R2's renamed to 'non-metric fit' and 'linear fit'. - - * Doc: Corrected reference to Hurlbert in diversity (thanks to - Ralph Grundel). Updated references (varpart, renyiaccum). Removed - discussion on t-values in cca from vignettes, because as.mlm.cca - now implements those. General cleanup and better utf-8 encoding. - -Version 1.8-5 (January 11, 2007) - - * Based on devel version 1.9-12. - - * no.shared (manifest in metaMDS): prints thousands of lines of - debugging info that I forgot to deactive in release. Not fatal, - but extremely annoying. - - * capscale: inertia name as "unknown" if the dissimilarity object - does not have a "method" name. Suggested by Roeland Kindt. - - * DESCRIPTION: license is now explicitly GPL v2 (but not later). - -Version 1.8-4 (January 8, 2007) - - * Based on devel version 1.9-10. - - * cascadeKM: a new function to wrap kmeans and optimality criteria - for classification (Sebastien Durand, Pierre Legendre & Marie - Helene Ouellette). - - * renyiaccum: a new function for Renyi (and Hill) accumulation - curves (Roeland Kindt). - - * bioenv: bioenv.formula uses now "na.action = NULL" in - 'model.frame', and bioenv.default passes arguments to 'cor' which - means that you can set NA treatment in 'cor' using argument "use". - - * cca, rda: added "..." to formula versions to satisfy tests in - R-DEVEL. - - * cca, rda, capscale: used to fail if called within other - functions. Now data always evaluated in the environment of - formula using new internal function ordiGetData. - - * anova.cca: checks that the model has both residual and - constrained components or stops with understandable error message - (used to stop with incomprehensible error message). - - * print.summary.cca, print.summary.decorana: have now arguments - 'head' and 'tail' to print only a part of species and site - scores. Suggested by Gavin Simpson. - - * metaMDS: checks now that the input data ('comm') is not a 'dist' - object (like many users have had). - - * ordisurf: Does not depend on package 'akima' any longer, but - directly finds fitted values in a regular grid using - 'predict.gam'. Added pnpoly.c to find which of these values are - within the convex hull defined by data. Results also look neater - with sparse data now. Added argument 'labcex' passed to 'contour' - for changing size of contour labels. Setting 'labcex = 0' will - suppress drawing labels (by setting drawlabels = FALSE in - 'contour()'). - - * orditorp: handles now vector arguments of 'col', 'pcol', 'cex', - and 'pch'. - - * rad.zipfbrot: less likely to overflow to NA coefficients during - iteration. - - * renyi: added a plot function, and documented together with - renyiaccum() instead of diversity(). - - * scores.default: Knows now about ade4 objects. Primarily looks - for scores scaled by eigenvalues both for sites and species. - - * specaccum: Added new conditioned method of Colwell et - al. with estimated sd based on extrapolated richness (Roeland - Kindt). - - * vegdist.c: More informative warnings with 'method' name (useful - with rankindex). - - * DESCRIPTION: listed 'require()d' packages in the "Suggests:" - field to satisfy more anal tests in R-DEVEL. Mention diversity - analysis in the "Description:". - -Version 1.8-3 (Sept 29, 2006) - - * Based on devel version 1.9-2. - - * varespec.rda, varechem.rda: saved in binary form, because old - ascii form gave warning in R-2.4.0-rc. - - * vegdist: added Chao index (of Jaccard type) that should take - into account missing pairs of species. Checks that Binomial index - is non-negative. Identical sites could have dissimilarity of - magnitude 1e-17 after some standardizations, but now values <1e-15 - are zapped to zero. - - * estimateR: uses now standard unbiased formulation of Chao. - - * renyi: should work now (really!). - - * metaMDS: with zero = "add", zeros now replaced with - min(dis[dis>0])/2 (used to be 1E-4) (metaMDSdist). Sets number of - tries also when this was not set previously (metaMDSiter) - -Version 1.8-2 (June 13, 2006) - - * version 1.8-1 failed test in Windows because of a wrong encoding - name. Explicit \enc added for non-ascii words. Kurt Hornik and - Uwe Ligges diagnosed this and led me to see the light. - - * similar to devel version 1.7-97. - -Version 1.8-1 (June 12, 2006) - - * Based on devel version 1.7-96. - - * Pierre Legendre joined the vegan team. - - * beals: a new function for Beals smoothing. - - * bioenv: added 'trace' argument. - - * cca/rda/capscale: accept several 'Condition' elements in the - formula. - - * capscale: capscale(y ~ ., data=...) or expansion of "." on the - rhs works now. Documentation recognizes now db-RDA as the real - mother method. - - * scores.cca, summary.cca etc: rewrite so that is cleaner and - easier to maintain. User visible changes are scaling by species - standard deviation (negative scaling) for 'rda', scaling=0 - (no scaling) for all methods and slightly changed output and - improved user control in summary. These scalings actually were - documented in 1.6-10, although I dropped them just before the - release. predict.cca, predict.rda: work now with 'newdata' even - when not called with formula. - - * anova.cca: new argument 'by' for tests of single terms or axes: - with by = "terms" performs individual test for constraints, and - with by = "axis" a separate sequential test for each axis. New - argument 'first' to analyse only the first axis instead of all - constrained variation. - - * intersetcor: new function for the interset correlation or the - (weighted) correlation between individual constraints (contrasts) - and invidual axes in cca/rda/capscale. (Not recommended.) - - * decostand: does not automatically convert matrix to a - data.frame. NA handling more consistent now (thanks to Tyler Smith - for diagnosis). Adds attribute 'decostand' giving the "method". - - * linestack: now really uses median as the midpoint with the odd - number of cases, and does not give superfluous warnings with three - or less items. New argument 'labels' to replace the default text - in plot. The old argument 'label' renamed to 'side'. However, the - function still works with the old syntax, but gives a warning if - the old argument 'label' is used for 'side'. Returns invisibly the - shifted positions of labels. - - * metaMDS: 'postMDS' sets now attributes similarly when called - independently or within 'metaMDS'. 'metaMDS' forwards arguments - to 'postMDS' except 'halfchange'. Change of phrasing in - 'print'. Added handling of zero dissimilarities into - 'metaMDSdist': either "fail" or "add" 1E-4 into zeros. - - * mrpp: new function for the multiresponse permutation procedure - (MRPP). Code by Henry Stevens (Miami Univ, Oxford, Ohio). - - * ordiarrows: draws arrowhead only in the last segment. New - argument 'startmark' for marking the starting point of the arrow. - - * ordisurf: new arguments 'main' for the title and 'nlevels' and - 'levels' for the number of contour or their values. - - * orditorp: arguments for text colour and text character expansion - changed to standard 'col' and 'cex' from previous 'tcol' and - 'tcex'. - - * procrustes: 'summary' prints now rotation matrix, translation - and scale, and honours 'digits'. - - * prc: new functions for Principal Response Curves (PRC) of van - den Brink and ter Braak (Envir. Toxicol. Chem, 18, 138-148; 1999). - This is a special rda() model with dedicated summary and plot - functions. New data set 'pyrifos' to demonstrate 'prc'. - - * radfit: added brokenstick as a null model (rad.null), removed - rad.veil (as it was a bad idea originally), corrected minor bugs - in rad.preempt (which did not fail gracefully). Line colours - start from the point colour in plot.radfit.frame. 'print' uses - "g" format for coefficients and adds 'digits' argument. Added - 'summary.radfit.frame' that simply prints each model. - - * rankindex: uses now cluster:::daisy when 'grad' includes - factors. - - * spantree: now a method function with 'plot', 'lines' and - 'cophenetic' methods. 'lines' replaces 'ordispantree'. The plot - has a weird, new way of finding configuration for a spanning tree - from cophenetic distances (unpublished). Documented separately. - - * specaccum: 'plot' honours now 'ylim'. - - * specpool: Chao richness was wrongly defined, but now uses the - biased formula (error introduced in 1.6-5, correct earlier). - Failed with zero species or if there were no species that occurred - exactly one in the 'pool' (thanks to Emmanuel Castella, Geneve CH, - for the bug report). - - * varpart: new functions for unbiased partitioning of variation by - two to four explanatory tables in RDA or linear regression. The - author of these functions is Pierre Legendre & co (Univ - Montreal). New data sets 'mite', 'mite.env' and 'mite.pcnm' to - demonstrate the functions. - - * vegandocs: new function to display *all* pdf documentation and - ChangeLog. This really should be in base R -- this is a kluge to - fill the hole. - - * vegemite: added argument to 'select' a subset of sites. Drops - missing species from the table. Prints number of species and - number of sites and the used cover scale at the end of the table. - Passes arguments (i.e., 'maxabund') to coverscale(). - - * coverscale: added argument 'maxabund' to 'scale = "log"'. - Returns the name of the cover scale as an attribute for vegemite() - to print. - - * vegdist: now first checks input and then transforms (if - needed). Thanks to Tyler Smith, - - * Internal changes: permuted.index acceptes NULL strata as an - alternative to missing strata. ordispantreee deprecated. - spider.cca removed. - - * Documentation: general cleanup in help files. New chapter on - t-values in cca/rda/capscale in vegan-FAQ. New pdf document on - partioning with varpart by Pierre Legendre & co. Non-latin - characters now use UTF-8 in documentation. R manual says that you - should not use non-latin characters in help files, but that was - written by Englishmen. However, this seems to cause distress to - some users of a US West Coast OS (Windows), but OK with mainstream - OS's (Linux, MacOS). . - -Version 1.6-10 (September 26, 2005) - - * Based on devel version 1.7-77. Checked with R 2.1.1 (stable) - and R 2.2.0 (alpha). - - * rda: negative scalings explicitly ignored and treated as - corresponding positive values. Function summary.rda used to fail - with NA centroids. - - * permutest.cca & anova.cca: permutation of 'cca' result now - re-weights environmental data properly with permuted community - weights (this breaks compliance with popular proprietary - software). New default method 'direct' that always permutes the - data instead of residuals. Now clearly faster basic routines, but - re-weighting in 'cca' is costly, and permutations may even be - slower than earlier in small data sets. The permutest.cca returns - more data: constrained and residual total inertia, degrees of - freedom in the input model, and .Random.seed used in iterations. - Thanks to Pierre Legendre for pushing me to make this faster. - - * ordination plot functions: obey now xlim and ylim. - - * ordination text and points functions: a new argument 'select' - that can be used to select a subset of items, and a new argument - 'labels' for text used instead of the default row names. - - * points.cca, text.cca: biplot arrows will be scaled automatically - to fit the current graph if 'arrow.mul' is not given. The new - behaviour is similar to the default in plot.cca and in - plot.envfit. - - * orditorp: a new "ordination text or points" function to add text - or points to an existing plot: adds text if this can be done - without overwriting other text labels, and points otherwise. - - * linestack: a new function to draw labelled one-dimensional - diagrams without overwriting the labels (a primitive one, and - could easily be improved: submissions are welcome). - - * ordirgl, orgltext: adapted to changes in rgl package version - 0.65. Workaround for older rgl packages, too. - - * decostand: Added Hellinger transformation. Empty columns and - rows become now 0 instead of NaN in most methods, except when the - input data contains negative values. Warns on input with negative - entries or on output containing NaN. Has now argument - 'range.global' for method 'range' based on the code supplied by - Tyler Smith. - - * plot.radfit: puts now legend "topright" in R >= 2.1.0 (which has - this keyword). - - * read.cep: issues a warning if vegan was compiled with gfortran, - which has a bug that may corrupt the result. The bug concerns - multiline input with T format modifier, and it was corrected in - http://gcc.gnu.org/ml/gcc-patches/2005-09/msg00126.html, but still - bugs most released versions of gcc. - - * vegdist: new indices 'raup' and 'binomial'. Method 'raup' - implements probabilistic Raup-Crick index and is based on the code - submitted by Michael Bedward. Method 'binomial' implements - Millar's index, officially published as "Binomial deviance as a - dissimilarity measure" (the C code has been in vegan for about two - years, but now I tell about it and add the public interface). - Method 'mountford' will give NA for any comparison involving an - empty site. Warns about empty sites or negative entries with all - methods except 'euclidean' and 'manhattan'. - - * Documentation: added documentation of the cca/rda/capscale - result object. Dontruns used more neatly. - -Version 1.6-9 (April 22, 2005) - - * Maintenance release: 1.6-8 failed in R 2.1.0 patched and R 2.2.0 - devel (works in R 2.1.0 release) due to problems in - as.preston. Based on devel version 1.7-62. - - * as.fisher, as.preston: used table() in a way that failed in R - 2.1.0 patched - - * calibrate.cca: new function to predict or calibrate or - bioindicate the values of environmental constraints from - community composition (ordination). - - * decostand: new argument na.rm (defaults FALSE) for ignoring - missing values in row, column or matrix standardizations. - - * vegdist: new argument na.rm (defaults FALSE) for pairwise - deletion of missing vaues in dissimilarity calculation. - -Version 1.6-8 (April 18, 2005) - - * Based on devel version 1.7-59. Adapted to R 2.1.0 beta. - - * DESCRIPTION: gives due credit to Roeland Kindt and Bob O'Hara as - co-authors. - - * documentation: updates in capscale, vegdist. vegan-FAQ adapted - to changes in Sweave in R 2.1.0. - - * several methods assumed that input is count data, but silently - accepted floating point numbers and gave wrong results. Now they - stop with error with non-integer input: fisherfit, prestonfit, - prestondistr, rarefy, fisher.alpha, estimateR. - - * bioenv: uses now 'cor' instead of 'cor.test', and does not give - so many superfluous warnings and is marginally faster. Changed - printed output, so that gives 'call' instead of names of community - and environmental data, since bioenv.formula could not handle - these cleanly. - - * capscale: has now argument 'add' to use an additive constant to - all dissimilarities so that all computed eigenvalues are - non-negative. This is an argument of underlying 'cmdscale' - function, which implements the "Correction method 2" of Legendre & - Legendre (1998), p. 434. - - * decorana: checks now that there are no negative data entries. - - * dune: cleaner site names. - - * envfit: 'plot' now automatically scales arrows similarly as - 'plot.cca' if 'arrow.mul' is not specified and arrows are added to - an old plot. Has now a 'scores' function. - - * goodness.cca, predict.cca: documented separately. - - * goodness.metaMDS: new function to assess pointwise goodness of - fit in metaMDS or isoMDS. - - * humpfit: user can now give starting values of parameters. - 'summary.humpfit' returns 'cov.unscaled' so that users can apply - 'ellipse' function of 'ellipse' package to display approximate - confidence ellipses for pairs of parameters (with Normal - assumptions). - - * make.cepnames: No longer duplicates one component names, but - 'abbreviate's them to eight characters. - - * metaMDS: Split to independent metaunits 'metaMDSdist' and - 'metaMDSiter'. New function 'metaMDSredist' tries to reconstruct - dissimilarity matrices and transformations. 'postMDS' skips - halfchange scaling with too few points (with a warning) instead of - stopping with an error. Prints now info about 'postMDS' - operations. New 'scores' function. Improved 'plot' function with - 'display' argument and labelling of axes. Argument 'shrink' to - undo expansion of species scores in plot or scores functions. - Workaround for a future bug in 'isoMDS' which drops names of - points in R 2.1.0. Updates number of 'tries' with - 'previous.best'. - - * ordiplot: has now 'display' argument, so that only species or - sites can be plotted. - - * ordiplot3d: a new function for 3D static ordination diagrams. - - * ordirgl: new function for dynamic 3D graphics of ordination - results. With support functions orglpoints, orgltext, - orglsegments and orglspider to add graphical items to dynamic - plots. Needs 'rgl' package. - - * predict.cca, predict.rda: type = "wa" with 'newdata' works now - even when some species were removed from the original fit because - they were all-zero (missing). Default now to 'model = "CA"' in - unconstrained analysis without "CCA" component. - - * predict.decorana: a new function for 'decorana' results. Similar - to 'predict.cca' (but refuses to give impossible results). - - * procrustes: obeys now 'choices' even when input is a matrix. - Plots now projections of rotated axes. New argument 'scores' so - that can handle other than "site" scores. - - * protest: passes now arguments to 'scores' so that now can handle - other than "site" scores on more than two dimensions. - - * rda: handles now species with constant values (typically - missing or all zeros). - - * spenvcor: new function to find the "species -- environment - correlation" in contrained ordination (cca, rda, capscale). - - * stressplot: a new function to plot Shepard diagram for 'metaMDS' - or 'isoMDS'. - - * summary.cca/summary.rda: print now call. - - * vegemite: can now 'use' 'dendrogram' objects. - -Version 1.6-7 (Jan 24, 2005) - - * Based on devel version 1.7-42. - - * plot.envfit did not plot vectors. Thanks to Roeland Kindt and - Ron E. VanNimwegen for bug reports and fixes. - - * ordisurf obeys now keyword 'display', and '...' will transfer - arguments to 'scores'. - - * ordisegments: obeys now keyword 'display'. - - * ordigrid: works now. - - * ordihull, ordiarrows, ordisegments, ordispider, ordiellipse: - have a new keyword 'show.groups' to show only specified groups. - With the help of this argument it is possible to use different - colours and linetypes for each group. Further, it makes possible - to plot results of logical comparisons (such as A1 > 4) by setting - show.groups = T. Two user requests. - - * new diagnostic and helper functions for 'cca', 'rda' and - 'capscale': 'goodness' to estimate the proportion of inertia - accounted for or residuals for sites or species; 'inertcomp' to - decompose species and site inertia for conditioned, contrained and - residual componets; 'vif.cca' to estimate the variance inflation - factors for constraints and conditions; 'fitted' and 'residuals' - to approximate data by ordination scores; 'predict' to approximate - data or find site or species scores, possibly with 'newdata'; - 'coef' to find the regression coefficients. 'alias.cca' is now - similar to 'alias.lm' (simplified version of the latter). - - -Version 1.6-6 (Jan 7, 2005) - - * Based on version 1.7-34, but without still experimental - functions predict, fitted, vif & goodness for cca and rda - objects. - - * mantel: implemented partial mantel test (function - 'mantel.partial'). Both 'mantel' and 'mantel.partial' should be - marginally faster and not give so many superfluous warnings. - - * plot.envfit: should be more reliable now. A new keyword 'at' to - position the bunch of arrows in other places than the origin. - Thanks to Roeland Kindt for fixes. - - * rankindex: Default correlation now "spearman" since the older - default ("kendall") was far too slow in large data sets. Should - not give so many superfluous warnings. - - * summary.rda: Site scores were wrong with 'scaling = 3'. - Influences 'plot' and 'scores' commands for 'rda' and 'capscale' - results with 'scaling = 3'. - - * vegdist: Issues a warning if 'method = "morisita"' is used with - non-integer data. - -Version 1.6-5 (Oct 12, 2004) - - * Based on version 1.7-27. Checked with R-1.9.1 (Linux, MacOS X) - and R-2.0.0 (Linux, patched version in Windows XP). Passed other - tests, but some examples in 'humpfit' failed in Windows XP, and - are not run on that platfrom. General cleanup in documentation. - Does not 'require(mva)' any longer. - - * anosim: corrected the equation in docs (function was - correct). Thanks to Yong Cao for notifying this. - - * bioenv.formula: finds now variables from attach'ed data.frames - and 'data' need not be specified. - - * capscale & deviance.rda: capscale was modified so that its - result could be handled with step (added a terms component). - Function deviance.capscale was deleted so that deviance.rda will - be used. A literature reference to AIC in CCA/RDA was added to the - documentation. - - * cca/rda/capscale: Small eigenvalues are made to zero and rank - reduced accordingly. Most often influences 'capscale'. - - * cca/rda/capscale: Now alias constraints that are collinear with - other constraints or conditions in partial analysis. New function - 'alias.cca' to print aliased contrasts. NB this does not influence - the proper ordination results, but only to the selection of biplot - scores and centroids displayed. 'summary' no longer gives a - redundant warning. - - * decorana: removes empty species with a warning instead of - stopping with error. Still error with empty sites. The behaviour - is similar to 'cca'. - - * envfit (vectorfit, factorfit): Has now a formula interface. - Bottlenecks in permutation changed now into C functions. Much - faster, in particular 'factorfit' which was 25 times faster in my - tests. plot.envfit could drop names (dimensions). Now honours - 'scaling' argument in cca/rda/capscale (used to 'scaling = 2' - always). - - * envfit, ordisurf: have now weights parameter 'w'. Weights are - used for the results of 'cca' and 'decorana' as default, or they - can be supplied by the user. In this way, envfit gives equal - results to biplot scores and centroids in 'cca', and ordisurf is - consistent with envfit. Both can now access 'lc' scores of 'cca' - etc. - - * estimateR: Abundance-based estimators of extrapolated richness, - written by Bob O'Hara . - - * humpfit: Asesses now the dispersion parameter in non-Poisson - models (summary.humpfit). Added a profile method that inherits - from profile.glm, so that you can use plot.profile.glm, - pairs.profile.glm and confint.profile.glm of MASS for displaying - profile and finding the confidence intervals from the profile - likelihood. - - * fisherfit: prints now standard error of the estimate. Added - 'profile' and 'confint' methods. - - * metaMDS: A new function to collect all Peter Minchin's - recommendations to run nonmetric multidimensional scaling: (1) - adequate data transformation with Wisconsin double and sqrt if - needed, (2) use of adequate dissimilarity measure, (3) possible - stepacross if needed (this Minchin didn't have), (4) run NMDS with - several random starts and stop after finding two similar - solutions, (5) scale results with postMDS, and (6) add species - scores with wascores. Function has print and plot methods. - Function scores.default changed to understand metaMDS. - - * specpool: Chao is now based on unbiased equation. Standard - errors now estimated for Chao, 1st-order jackknife and bootstrap - richness, but SE of 2nd-order jackknife still missing. - - * vegdist: an option for binary indices, since some users believed - these are not in vegan, although you can get them with - 'decostand'. - -Version 1.6-4 (June 10, 2004) - - * Based on 1.7-12. - - * anova.cca: Changed defaults to beta=0.01 and perm.max=10000. - - * bioenv: A new function implementing the BIO-ENV procedure of - Clarke & Ainsworth (Mar. Ecol. Prog. Ser. 92, 205-219; 1993). The - function finds the subset of scaled environmental variables with - maximum (rank) correlation with community data. - - * cca: added ter Braak scaling, a.k.a. Hill scaling, with - negative values of `scaling'. This scaling should approximate - Hill's non-linear rescaling of `decorana', but it the scaling is - linear and does not rescale. The scaling is very simple: the - results from corresponding positive scaling are multipiled with - sqrt(1/(1-lambda)). - - * plot.cca: Better heuristics used for the length of biplot - arrows, and they are now longer in general. Axes are drawn for - biplot arrows also with `text.cca' and `points.cca' functions. - - * deviance.cca and extractAIC.cca: auxilliary functions which - allow automatic model building with `step' or `stepAIC' (MASS) - functions in cca and rda. Unfortunately, cca and rda do not have - deviance or AIC, and these functions are certainly wrong and - dangerous. However, with continuous candidate variables they - select the variables in the same order as Canoco. - - * humpfit: A new family of functions to fit the no-interaction - model (Oksanen, J., J. Ecol. 84, 293-295; 1996) to species - richness -- biomass data. - - * vegdist: corrected a bug in Gower index -- range standardization - was made to sites, but it should be done to species. Thanks to - Brett Melbourne for the bug report. - -Version 1.6-3 (Mar 22, 2004) - - * Still based on 1.7-3, but ported some changes from version - 1.7-7 (envfit, procrustes). This version was produced to prepare - for incompatible changes in coming R-1.9.0. - - * vegan-FAQ: Does not show the vegan and R versions the - documentations were built, because of stupid incompatible change - in R-1.9.0 of the future. package.description() function was - unnecessarily replaced with packageDescription, and to accomodate - recent, present and future versions of R seemed to be too much - hassle. - - * plot.envfit: Added option 'p.max' to display only environmental - variables assessed to have P values less or equal to the given - limit. - - * plot.procrustes: added option kind = 0 to draw only axes and - functions points.procrustes and lines.procrustes to add points and - line segments or arrows to the plot. - - -Version 1.6-2: Based on devel version 1.7-3. - - * renyi: should work now (again?). - - * prestondistr: The truncation level was badly chosen. Now default - level is set to -1, or log2(-1) = 0.5 which might be the left - limit of the first octave. This is now a parameter, leaving the - choice as the responsibility of the user. - - * cca.default: Removes missing species and issues a warning - instead of stopping with error. - -Version 1.6-1: Based on devel version 1.7-1. - - * Minor maintanenance release to satisfy tests in R-devel. There - was a buglet in ordisegments that caused a warning. I had - introduced a trick to handle printCoefmat before it was invented - to replace print.coefmat, but this failed in R-devel. - - * Vignettes included in this release. These were only in devel - versions of my web pages previously. - -Version 1.6-0 (Fisher) - - - * Based on devel version 1.5-58. Passes checks with R-1.8.0 and - R-1.7.0 and compiles into a Windows binary. Many functions were - contributed by Roeland Kindt (Nairobi, Kenya). - - * BCI: A new data set on tree counts on 1-ha plots in the Barro - Colorado Island. Thanks to Roeland Kindt and Richard S. Condit. - - * capscale: A new function for [partial] constrained analysis of - principal coordinates (CAP). - - * cca, rda: Can now handle cases where the number of constraints - is higher than the number of species. - - * cca, decorana: Find now if data have empty rows or columns. - - * decostand: Added Chi-square transformation. - - * distconnected: A new function to find if vegetation data are - disconnected below given threshold dissimilarity level. Utility - function no.shared gives a logical dissimilarity matrix with - values TRUE for cases with no shared species. - - * fisher.alpha: Should work with large sample sizes now (bug - report thanks to Ariel Bergamini). - - * fisherfit, prestonfit, prestondistr: New functions to fit - Fisher's logarithmic series and Preston's log-normal model. - - * make.cepnames: New utility function to change Latin names into - 4+4 character CEP names. - - * ordicluster, ordispantree: New functions in the ordihull family - to overlay a cluster dendrogram or a spanning tree onto an - ordination. Weights are now really used with cca and decorana - plots. - - * radfit: New function to fit Ranked Abundance Dominance or - Dominance/ Diversity models with maximum likelihood: Pre-emption, - log-normal, veiled log-normal, Zipf and Zipf--Mandelbrot models. - - * rankindex: Can now use stepacross and new dissimilarity indices - of vegdist. - - * rarefy: Can now optionally find SE of rarefied richness. - - * renyi: A new function to find Rnyi diversities or Hill numbers - with any scale (thanks to Roeland Kindt). - - * scores.ordiplot: should be more robust now. - - * spantree: A new function to find a minimum spanning tree using - only dissimilarities below a threshold or disregarding NA - dissimilarities. - - * specaccum: A new function for species accumulation - curves. Thanks to Roeland Kindt. - - * specnumber: A very simple utility function to find species - richness. - - * specpool: New function for extrapolated species richness in a - species pool, or for estimating the number of unseen species. - - * stepacross: New function for estimation of dissimilarities - between sites that have no shared species. Implements both - flexible shortest paths and their approximation known as extended - dissimilarities. - - * vegdist: New dissimilarity indices Jaccard (finally), Morisita, - Horn--Morisita and Mountford. - -Version 1.4-4 - - * Based on devel version 1.5-35. - - * decorana: Finds now eigenvalues from the final solution as ratio - of biased weighted variances of site and species scores. The - values returned by the legacy Fortran function are called - ``decorana values''. - - * downweight: passes the downweighting fraction as an attribute, - and decorana catches and prints the fraction. - - * wascores: Uses now biased variances for expading WAs and returns - the shrinkage factors as an attribute "shrinkage". Shrinkage - factors are equal to eigenvalues in CCA when only this one - variable is used as constraint. Function `eigengrad' returns only - these eigenvalues. - - * rda summary/plot bugfix: Failed with non-default scaling (1 or - 3) when there were no factor constraints. Corrected a statement - giving a harmless warning in plot.cca. - - * ordiplot knows now option type = "text". New simpleminded - functions points.ordiplot and text.ordiplot for adding items in an - ordiplot graph. - - * ordiarrows, ordisegments could drop dimensions (when will I - learn!) and fail if only one arrow or one segment should be - drawn. One argument was missing in a `gl' command in `ordigrid'. - - -Version 1.4-3 (Luova) - - * Based on devel version 1.5-30 - - * `cca' and `rda' use canonical expansion of the formula and - return the `terms' component. Functions like `terms', `formula' - and `update' magically started to work with cca and rda (and eight - lines of code would allow `step' and `stepAIC' to work magically - in model building... but it's so much magic that I don't trust - it). Some of the allowed things are now `mod <- cca(dune ~ . - - Use, dune.env)' - (all variables but `Use' in dune.env), and `update(mod, . ~ . - - Manure)' (remove `Manure' from the previous). - - * `cca' and `rda' find centroids of factor levels with factor - constraints (only with formula interface). These can be accessed - with `display="cn"' in `scores', `summary' and `plot'. If - `display="cn"' is requested in `plot', biplot arrows are still - drawn unless there is a class centroid with the same name: - continuous variables are still shown as arrows and ordered factors - both as arrows as centroids. - - * Enhanced user control of many ordination plots. Some accept more - graphical parameters (`ordiplot', `plot.procrustes'). New - functions `points.cca' and `text.cca' to add these items into - CCA/RDA plots (documented separately). - - * `identify.ordiplot' knows more alternatives, among them - `plot.procrustes'. This was helped with new, very generic - `scores.ordiplot'. - - * New functions to add graphical items in ordination diagrams: - `ordihull' draws convex hulls for groups, `ordiarrows' draws - arrows, `ordisegments' segments, `ordigrid' combines points in to - a grid, `ordispider' combines points to their (weighted) centroids - or WA scores to the corresponding LC score in cca/rda, - `ordiellipse' draws dispersion or confidence ellipses for - points. All these take a groups argument for selecting subsets of - points. `ordispider' obsoletes `spider.cca' (introduced in the - previous release). - - * Implemented simple ``deshrinking'' of weighted averages in - `wascores'. - - * A new diversity function `fisher.alpha' that estimates the - diversity as the alpha parameter of Fisher's log-series. Thanks to - Bob O'Hara for the main code. - - * `summary.decorana' now in canonical form so that printing is - done by `print.summary.decorana' and user can intercept and catch - the result. Several users had been surprised of the earlier - non-canonical behaviour. `summary' knows again the prior weights - used in downweighting of rare species (these evaporated in 1.4-0). - - * rda summary/scores/plot bugfix: Scaling of site scores was wrong - with scaling alternatives 1 and 3. - - * envfit (vectorfit, factorfit) bug fixes: Can now handle single - vectors (labelling, permutation). Recognize now `choices' so that - the user can select ordination axes. - - * Checked with released R-1.7.0. - -Version 1.4-2 - - -Based on devel version 1.5-18 - - * New functions and data sets: - - * dune: A new data set -- the classic Dune Meadow data with the - environmental data. - - * ordiplot, identify.ordiplot: New functions to plot "any" - ordination result and identify plotted points. Intended to provide - similar functionality as `plot', `plotid' and `specid' functions - in `labdsv' without name clashes. Functions `plot.cca' and - `plot.decorana' return (invisibly) `ordiplot' objects as well, and - these can be used by `identify' to label species and sites. - - * rda: Redundancy Analysis, or optionally, yet another PCA. This - is a spin-off from cca(), and it is documented together with - cca. The only new functions are rda, rda.default, rda.formula and - summary.rda. Otherwise rda uses cca methods which were changed to - be aware of rda. - - * read.cep: A function to read Cornell Ecology Package or CEP - formatted files into R. This has been in devel versions since - 1.1-1, but it never made to the releases, since this worked only - in Linux, but crashed R in Windows. It seemed to work in R-1.6.1 - with gcc 3.2 - (MinGW) in Windows, so it is included. Treat with caution. - - * spider.cca: A tiny function that joins each WA score to the - corresponding LC score in cca/rda plots. - -Minor changes: - - * Checked with R-1.7.0 (unstable devel version, snapshot - 2003-02-05) and corrected as needed. - - * Added symmetric scaling (=3) to alternitves in cca and rda. - - * Minor improvements and bug fixes in vegemite. - -Version 1.4-1 (Logan) - -Based on devel version 1.5-10. - -New features: - - * Permutation tests added to envfit (vectorfit, factorfit) and - procrustes (called protest). - - * All permutation tests take now argument strata: If given, - permutations made only within strats. Concerns anosim, anova.cca - (permutest.cca), mantel, envfit (factorfit, envfit) and protest - (in procrustes). - - * fitted.procrustes(): A new function. - - * rarefy(): A new function for rarefaction species richness. - -Other changes: - - - * cca.default(): Handles now NULL matrices X and Z: skips them. - - * cca.formula(): Knows now cca(X ~ 1) and permforms unconstrained - CA, and cca(X ~ ., data) and perfors CCA using all variables in - `data' as constraints. Has now na.action=na.fail so that cca - crashes more informatively (used to crash mysteriously). A more - graceful na.action may come. Assignment "=" corrected to the true - blue "<-" (used to crash in old R). - - * envfit (factorfit, vectorfit): Use `scores' now. Various - bugfixes and should work now with single factors or vectors. - - * plot.envfit(): Options `choices' works now like documented. - - * ordisurf(): A new name to surf() to avoid name clash with - labdsv. - - * procrustes(): added option `symmetric' for symmetric rotation - and goodness of fit statistic. - - * plot.procrustes(): Keyword `axes' changed to `choices' to be - consistent with other functions. - - * vegemite(): A new name to vegetab. The name was chosen because - the output is so compact (and to avoid confusion with function - vegtab in labdsv). Function checks now that the cover codes fit in - one column and splits the output if it does not fit into used page - width. - -Version 1.4-0 - -Based on devel version 1.3-19. - -New functions: - - * `anosim': Analysis of Similarities. - - * `cca': [Partial] [Constrained] Correspondence Analysis. This is - an alternative to Dray's `CAIV' (package `CoCoAn'). Differences - include formula interface, WA scores in addition to LC scores, - partial CCA, residual CA after constraints, algorithm based on svd - (and much faster), more standard support functions (`plot', - `summary', `print'). - - * `anova.cca' and `permutest.cca': Permutation tests for `cca'. - - * `envfit': A new wrapper function which calls either `vectorfit' - or new `factorfit' function, and `plot.envfit' for easier display - of results in graphs. - - * `mantel': Mantel test for two - dissimilarity matrices. - - * `scores': A generic function to extract scores from `cca', - `decorana' or even from some common ordination methods in R. - - * `surf': Surface fitting for ordination, intended as an - alternative to `vectorfit'. - - * `vegetab': Prints a compact, ordered vegetation table, together - with `coverscale' to transform data into compact format. - -Other changes: - - * `downweight': directly callable instead of being embedded in - `decorana'. Now it can be used with other downweighting thresholds - or with other CA's than `decorana', e.g., `cca', `ca', `CAIV'. - - * `plot.decorana': new keyword `type', removed keyword `tol'. - - * `vectorfit': centres now ordination before fitting, so it will - work with other ordinations than MDS. - - * Mainly user invisible changes in `plot.decorana', - `plot.procrustes', `postMDS', `print.summary.procrustes', - `vectorfit', `procrustes'. One of the main changes was that - `eqscplot' of `MASS' library was replaced with `plot(..., asp=1)' - of standard R (but this is not S-plus compliant). - - * Edited help files. More examples run from help files. - - * Tried to improve LabDSV compliance, see - http://labdsv.nr.usu.edu/. - - * Checked with R-1.5.0. However, `log' in diversity not changed - into `logb' (yet), although base is specified, because `logb' - won't work in R pre-1.5.0. - -Version 1.2-1 - - * Checked with R-1.4-0 (frozen snapshot) tools: Documentation and - method consistency corrected (summary.decorana.R, decorana.Rd), - and file permissions changed. - - * decorana.Rd: removed dependence on package `multiv' in the - example, since it is not a `recommended package' -- replaced with - `decorana(..., ira=1)'. - -Version 1.2-0 - - -Based on experimental version 1.1-6. Doesn't include read.cep() of the -experimental version, because the function is certainly unportable to -all platforms (works on Red Hat Linux 7.1, but perhaps nowhere else). - -Major new feature: - - * decorana(): R port of Mark Hill's DECORANA for Detrended - correspondence analysis, with methods print(), summary() and - plot(). - -Minor changes: - - * postMDS(): finds isoMDS scores and doesn't destroy list - structure. - - * vectorfit: finds isoMDS scores and decorana row scores (which - are uncentred, though). - - * Proof reading in documentation. - -Version 1.0-3 - - *`plot.procrustes': selection of axes plotted, improved scaling in - graphics, control of axis labels. - - * `postMDS': added plot to demonstrate half-change scaling. - - * `procrustes': target can now have lower dimensionality than - rotated matrix. - -Version 1.0-2 - - - * Fixed bugs in documentation. - - * `plot.procrustes': Fixed passing `...' to graphics functions. - -Version 1.0-1 - - * The first public version. diff --git a/scTCRpy/rpackages/vegan/R/vegan b/scTCRpy/rpackages/vegan/R/vegan deleted file mode 100644 index 3b65e3cbb..000000000 --- a/scTCRpy/rpackages/vegan/R/vegan +++ /dev/null @@ -1,27 +0,0 @@ -# File share/R/nspackloader.R -# Part of the R package, http://www.R-project.org -# -# Copyright (C) 1995-2012 The R Core Team -# -# This program is free software; you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation; either version 2 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 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To view a copy of this license, visit -> or send a letter to -> Creative Commons, 543 Howard Street, 5th Floor, San Francisco, -> California, 94105, USA. -> -> Copyright © 2008-2016 vegan development team - ------------------------------------------------------------------------- - -Introduction ------------- - ------------------------------------------------------------------------- - -### What is **vegan**? - -**Vegan** is an R package for community ecologists. It contains the most -popular methods of multivariate analysis needed in analysing ecological -communities, and tools for diversity analysis, and other potentially -useful functions. **Vegan** is not self-contained but it must be run -under R statistical environment, and it also depends on many other R -packages. **Vegan** is [free -software](http://www.gnu.org/philosophy/free-sw.html) and distributed -under [GPL2 license](http://www.gnu.org/licenses/gpl.html). - ------------------------------------------------------------------------- - -### What is R? - -R is a system for statistical computation and graphics. It consists of a -language plus a run-time environment with graphics, a debugger, access -to certain system functions, and the ability to run programs stored in -script files. - -R has a home page at . It is [free -software](http://www.gnu.org/philosophy/free-sw.html) distributed under -a GNU-style [copyleft](http://www.gnu.org/copyleft/copyleft.html), and -an official part of the [GNU](http://www.gnu.org/) project (“GNU S”). - ------------------------------------------------------------------------- - -### How to obtain **vegan** and R? - -Both R and latest release version of **vegan** can be obtained through -[CRAN](https://cran.r-project.org). Unstable development version of -**vegan** can be obtained through -[GitHub](https://github.com/vegandevs/vegan). Formerly **vegan** was -developed in [R-Forge](http://r-forge.r-project.org/projects/vegan/), -but after moving to [GitHub](https://github.com/vegandevs/vegan) the -R-Forge repository may be out of date. - ------------------------------------------------------------------------- - -### What R packages **vegan** depends on? - -**Vegan** depends on the **permute** package which will provide advanced -and flexible permutation routines for **vegan**. The **permute** package -is developed together with **vegan** in -[GitHub](https://github.com/gavinsimpson/permute). - -Some individual **vegan** functions depend on packages **MASS**, -**mgcv**, **parallel**, **cluster**, **lattice** and **tcltk**. These -all are base or recommended R packages that should be available in -every R installation. **Vegan** declares these as suggested or -imported packages, and you can install **vegan** and use most of its -functions without these packages. - -**Vegan** is accompanied with a supporting package **vegan3d** for -three-dimensional and dynamic plotting. The **vegan3d** package needs -non-standard packages **rgl** and **scatterplot3d**. - ------------------------------------------------------------------------- - -### What other packages are available for ecologists? - -CRAN [Task Views](https://cran.r-project.org/web/views/) include -entries like `Environmetrics`, `Multivariate` and `Spatial` that -describe several useful packages and functions. If you install R package -**ctv**, you can inspect Task Views from your R session, and -automatically install sets of most important packages. - ------------------------------------------------------------------------- - -### What other documentation is available for **vegan**? - -**Vegan** is a fully documented R package with standard help pages. -These are the most authoritative sources of documentation (and as a -last resource you can use the force and the read the source, as -**vegan** is open source). **Vegan** package ships with other -documents which can be read with `browseVignettes("vegan")` -command. The documents included in the **vegan** package are - -- **Vegan** `NEWS` -- This document (`FAQ-vegan`). -- Short introduction to basic ordination methods in **vegan** - (`intro-vegan`). -- Introduction to diversity methods in **vegan** - (`diversity-vegan`). -- Discussion on design decisions in **vegan** (`decision-vegan`). -- Description of variance partition procedures in function `varpart` - (`partitioning`). - -Web documents outside the package include: - -- : **vegan** homepage. -- : - **vegan** tutorial. - ------------------------------------------------------------------------- - -### Is there a Graphical User Interface (GUI) for **vegan**? - -Roeland Kindt has made package **BiodiversityR** which provides a GUI -for **vegan**. The package is available at -[CRAN](https://cran.r-project.org/package=BiodiversityR). -It is not a mere GUI for **vegan**, but adds some new functions and -complements **vegan** functions in order to provide a workbench for -biodiversity analysis. You can install **BiodiversityR** using -`install.packages("BiodiversityR")` or graphical package management menu -in R. The GUI works on Windows, MacOS X and Linux. - ------------------------------------------------------------------------- - -### How to cite **vegan**? - -Use command `citation("vegan")` in R to see the recommended citation to -be used in publications. - ------------------------------------------------------------------------- - -### How to build **vegan** from sources? - -In general, you do not need to build **vegan** from sources, but binary -builds of release versions are available through -[CRAN](https://cran.r-project.org/) for Windows and MacOS X. If you use -some other operating systems, you may have to use source packages. -**Vegan** is a standard R package, and can be built like instructed in R -documentation. **Vegan** contains source files in C and FORTRAN, and you -need appropriate compilers (which may need more work in Windows and -MacOS X). - ------------------------------------------------------------------------- - -### Are there binaries for devel versions? - -[R-Forge](http://r-forge.r-project.org/projects/vegan/) runs daily tests -on the devel package, and if passed, it builds source package together -with Windows binaries. However, the R-Forge may be out of date, because -**vegan** is mainly developed in -[GitHub](https://github.com/vegandevs/vegan). You can install R-Forge -packages within R with command -`install.packages("vegan", repos="http://r-forge.r-project.org/")`. If -you use GUI menu entry, you must select or define the R-Forge -repository. - ------------------------------------------------------------------------- - -### How to report a bug in **vegan**? - -If you think you have found a bug in **vegan**, you should report it to -**vegan** maintainers or developers. The preferred forum to report bugs -is [GitHub](https://github.com/vegandevs/vegan/issues). The bug report -should be so detailed that the bug can be replicated and corrected. -Preferably, you should send an example that causes a bug. If it needs a -data set that is not available in R, you should send a minimal data set -as well. You also should paste the output or error message in your -message. You also should specify which version of **vegan** you used. - -Bug reports are welcome: they are the only way to make **vegan** -non-buggy. - -Please note that you shall not send bug reports to R mailing lists, -since **vegan** is not a standard R package. - ------------------------------------------------------------------------- - -### Is it a bug or a feature? - -It is not necessarily a bug if some function gives different results -than you expect: That may be a deliberate design decision. It may be -useful to check the documentation of the function to see what was the -intended behaviour. It may also happen that function has an argument to -switch the behaviour to match your expectation. For instance, function -`vegdist` always calculates quantitative indices (when this is -possible). If you expect it to calculate a binary index, you should use -argument `binary = TRUE`. - ------------------------------------------------------------------------- - -### Can I contribute to **vegan**? - -**Vegan** is dependent on user contribution. All feedback is welcome. If -you have problems with **vegan**, it may be as simple as incomplete -documentation, and we shall do our best to improve the documents. - -Feature requests also are welcome, but they are not necessarily -fulfilled. A new feature will be added if it is easy to do and it looks -useful, or if you submit code. - -If you can write code yourself, the best forum to contribute to vegan is -[GitHub](https://github.com/vegandevs/vegan). - ------------------------------------------------------------------------- - -Ordination ----------- - ------------------------------------------------------------------------- - -### I have only numeric and positive data but **vegan** still complains - -You are wrong! Computers are painfully pedantic, and if they find -non-numeric or negative data entries, you really have them. Check your -data. Most common reasons for non-numeric data are that row names were -read as a non-numeric variable instead of being used as row names (check -argument `row.names` in reading the data), or that the column names were -interpreted as data (check argument `header = TRUE` in reading the -data). Another common reason is that you had empty cells in your input -data, and these were interpreted as missing values. - ------------------------------------------------------------------------- - -### Can I analyse binary or cover class data? - -Yes. Most **vegan** methods can handle binary data or cover abundance -data. Most statistical tests are based on permutation, and do not make -distributional assumptions. There are some methods (mainly in diversity -analysis) that need count data. These methods check that input data are -integers, but they may be fooled by cover class data. - ------------------------------------------------------------------------- - -### Why dissimilarities in **vegan** differ from other sources? - -Most commonly the reason is that other software use presence–absence -data whereas **vegan** used quantitative data. Usually **vegan** indices -are quantitative, but you can use argument `binary = TRUE` to make them -presence–absence. However, the index name is the same in both cases, -although different names usually occur in literature. For instance, -Jaccard index actually refers to the binary index, but **vegan** uses -name `"jaccard"` for the quantitative index, too. - -Another reason may be that indices indeed are defined differently, -because people use same names for different indices. - ------------------------------------------------------------------------- - -### Why NMDS stress is sometimes 0.1 and sometimes 10? - -Stress is a proportional measure of badness of fit. The proportions can -be expressed either as parts of one or as percents. Function `isoMDS` -(**MASS** package) uses percents, and function `monoMDS` (**vegan** -package) uses proportions, and therefore the same stress is 100 times -higher in `isoMDS`. The results of `goodness` function also depend on -the definition of stress, and the same `goodness` is 100 times higher in -`isoMDS` than in `monoMDS`. Both of these conventions are equally -correct. - ------------------------------------------------------------------------- - -### I get zero stress but no convergent solutions in `metaMDS` - -Most common reason is that you have too few observations for your NMDS. -For `n` observations (points) and `k` dimensions you need to estimate -`n*k` parameters (ordination scores) using `n*(n-1)/2` dissimilarities. -For `k` dimensions you must have `n > 2*k + 1`, or for two dimensions at -least six points. In some degenerate situations you may need even a -larger number of points. If you have a lower number of points, you can -find an undefined number of perfect (stress is zero) but different -solutions. Conventional wisdom due to Kruskal is that you should have -`n > 4*k + 1` points for `k` dimensions. A typical symptom of -insufficient data is that you have (nearly) zero stress but no two -convergent solutions. In those cases you should reduce the number of -dimensions (`k`) and with very small data sets you should not use -`NMDS`, but rely on metric methods. - -It seems that local and hybrid scaling with `monoMDS` have similar lower -limits in practice (although theoretically they could differ). However, -higher number of dimensions can be used in metric scaling, both with -`monoMDS` and in principal coordinates analysis (`cmdscale` in -**stats**, `wcmdscale` in **vegan**). - ------------------------------------------------------------------------- - -### Zero dissimilarities in isoMDS - -Function `metaMDS` uses function `monoMDS` as its default method for -NMDS, and this function can handle zero dissimilarities. Alternative -function `isoMDS` cannot handle zero dissimilarities. If you want to use -`isoMDS`, you can use argument `zerodist = "add"` in `metaMDS` to handle -zero dissimilarities. With this argument, zero dissimilarities are -replaced with a small positive value, and they can be handled in -`isoMDS`. This is a kluge, and some people do not like this. A more -principal solution is to remove duplicate sites using R command -`unique`. However, after some standardizations or with some -dissimilarity indices, originally non-unique sites can have zero -dissimilarity, and you have to resort to the kluge (or work harder with -your data). Usually it is better to use `monoMDS`. - ------------------------------------------------------------------------- - -### I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores - -Claims like this have indeed been at large in the Internet, but they are -based on grave misunderstanding and are plainly wrong. NMDS ordination -results are strictly metric, and in **vegan** `metaMDS` and `monoMDS` -they are even strictly Euclidean. The method is called “non-metric” -because the Euclidean distances in ordination space have a non-metric -rank-order relationship to community dissimilarities. You can inspect -this non-linear step curve using function `stressplot` in **vegan**. -Because the ordination scores are strictly Euclidean, it is correct to -use **vegan** functions `envfit` and `ordisurf` with NMDS results. - ------------------------------------------------------------------------- - -### Where can I find numerical scores of ordination axes? - -Normally you can use function `scores` to extract ordination scores for -any ordination method. The `scores` function can also find ordination -scores for many non-**vegan** functions such as for `prcomp` and -`princomp` and for some **ade4** functions. - -In some cases the ordination result object stores raw scores, and the -axes are also scaled appropriate when you access them with `scores`. For -instance, in `cca` and `rda` the ordination object has only so-called -normalized scores, and they are scaled for ordination plots or for other -use when they are accessed with `scores`. - ------------------------------------------------------------------------- - -### How the RDA results are scaled? - -The scaling or RDA results indeed differ from most other software -packages. The scaling of RDA is such a complicated issue that it cannot -be explained in this FAQ, but it is explained in a separate pdf document -on “Design decision and implementation details in vegan” that you can -read with command `browseVignettes("vegan")`. - ------------------------------------------------------------------------- -### I cannot print and plot RDA results properly - -If the RDA ordination results have a weird format or you cannot plot -them properly, you probably have a name clash with **klaR** package -which also has function `rda`, and the **klaR** `print`, `plot` or -`predict` functions are used for **vegan** RDA results. You can choose -between `rda` functions using `vegan::rda()` or `klaR::rda()`: you -will get obscure error messages if you use the wrong function. In -general, **vegan** should be able to work normally if **vegan** was -loaded after **klaR**, but if **klaR** was loaded later, its functions -will take precedence over **vegan**. Sometimes **vegan** namespace is -loaded automatically when restoring a previously stored workspace at -the start-up, and then **klaR** methods will always take precedence -over **vegan**. You should check your loaded packages. **klaR** may be -also loaded indirectly via other packages (in the reported cases it -was most often loaded via **agricolae** package). **Vegan** and -**klaR** both have the same function name (`rda`), and it may not be -possible to use these packages simultaneously, and the safest choice -is to unload one of the packages if only possible. See discussion in -[vegan github issues](https://github.com/vegandevs/vegan/issues/277). - -If you have a very old version of **ade4** (prior to 1.7-8), you may -have similar name clashes with `cca`. The solution is to upgrade -**ade4**. - ------------------------------------------------------------------------- -### Ordination fails with “Error in La.svd” - -Constrained ordination (`cca`, `rda`, `capscale`) will sometimes fail -with error message -`Error in La.svd(x, nu, nv): error code 1 from Lapack routine 'dgesdd'.` - -It seems that the basic problem is in the `svd` function of `LAPACK` -that is used for numerical analysis in R. `LAPACK` is an external -library that is beyond the control of package developers and R core team -so that these problems may be unsolvable. It seems that the problems -with the `LAPACK` code are so common that even the help page of `svd` -warns about them - -Reducing the range of constraints (environmental variables) helps -sometimes. For instance, multiplying constraints by a constant \< 1. -This rescaling does not influence the numerical results of constrained -ordination, but it can complicate further analyses when values of -constraints are needed, because the same scaling must be applied there. -We can only hope that this problem is fixed in the future versions of R -and `LAPACK`. - ------------------------------------------------------------------------- - -### Variance explained by ordination axes. - -In general, **vegan** does not directly give any statistics on the -“variance explained” by ordination axes or by the constrained axes. This -is a design decision: I think this information is normally useless and -often misleading. In community ordination, the goal typically is not to -explain the variance, but to find the “gradients” or main trends in the -data. The “total variation” often is meaningless, and all proportions of -meaningless values also are meaningless. Often a better solution -explains a smaller part of “total variation”. For instance, in -unstandardized principal components analysis most of the variance is -generated by a small number of most abundant species, and they are easy -to “explain” because data really are not very multivariate. If you -standardize your data, all species are equally important. The first axes -explains much less of the “total variation”, but now they explain all -species equally, and results typically are much more useful for the -whole community. Correspondence analysis uses another measure of -variation (which is not variance), and again it typically explains a -“smaller proportion” than principal components but with a better result. -Detrended correspondence analysis and nonmetric multidimensional scaling -even do not try to “explain” the variation, but use other criteria. All -methods are incommensurable, and it is impossible to compare methods -using “explanation of variation”. - -If you still want to get “explanation of variation” (or a deranged -editor requests that from you), it is possible to get this information -for some methods: - -- Eigenvector methods: Functions `rda`, `cca` and `capscale` give the - variation of conditional (partialled), constrained (canonical) and - residual components, but you must calculate the proportions by hand. - Function `eigenvals` extracts the eigenvalues, and - `summary(eigenvals(ord))` reports the proportions explained in the - result object `ord`. Function `RsquareAdj` gives the R-squared and - adjusted R-squared (if available) for constrained components. - Function `goodness` gives the same statistics for individual species - or sites (species are unavailable with `capscale`). In addition, - there is a special function `varpart` for unbiased partitioning of - variance between up to four separate components in redundancy - analysis. -- Detrended correspondence analysis (function `decorana`). The total - amount of variation is undefined in detrended correspondence - analysis, and therefore proportions from total are unknown and - undefined. DCA is not a method for decomposition of variation, and - therefore these proportions would not make sense either. -- Nonmetric multidimensional scaling. NMDS is a method for nonlinear - mapping, and the concept of of variation explained does not make - sense. However, 1 - stress\^2 transforms nonlinear stress into - quantity analogous to squared correlation coefficient. Function - `stressplot` displays the nonlinear fit and gives this statistic. - ------------------------------------------------------------------------- - -### Can I have random effects in constrained ordination or in `adonis`? - -No. Strictly speaking, this is impossible. However, you can define -models that respond to similar goals as random effects models, although -they strictly speaking use only fixed effects. - -Constrained ordination functions `cca`, `rda` and `capscale` can have -`Condition()` terms in their formula. The `Condition()` define partial -terms that are fitted before other constraints and can be used to remove -the effects of background variables, and their contribution to -decomposing inertia (variance) is reported separately. These partial -terms are often regarded as similar to random effects, but they are -still fitted in the same way as other terms and strictly speaking they -are fixed terms. - -Function `adonis` evaluates terms sequentially. In a model with -right-hand-side `~ A + B` the effects of `A` are evaluated first, and -the effects of `B` after removing the effects of `A`. Sequential tests -are also available in `anova` function for constrained ordination -results by setting argument `by = "term"`. In this way, the first terms -can serve in a similar role as random effects, although they are fitted -in the same way as all other terms, and strictly speaking they are fixed -terms. - -All permutation tests in **vegan** are based on the **permute** package -that allows constructing various restricted permutation schemes. For -instance, you can set levels of `plots` or `blocks` for a factor -regarded as a random term. - -A major reason why real random effects models are impossible in most -**vegan** functions is that their tests are based on the permutation of -the data. The data are given, that is fixed, and therefore permutation -tests are basically tests of fixed terms on fixed data. Random effect -terms would require permutations of data with a random component instead -of the given, fixed data, and such tests are not available in **vegan**. - ------------------------------------------------------------------------- - -### Is it possible to have passive points in ordination? - -**Vegan** does not have a concept of passive points, or a point that -should only little influence the ordination results. However, you can -add points to eigenvector methods using `predict` functions with -`newdata`. You can first perform an ordination without some species or -sites, and then you can find scores for all points using your complete -data as `newdata`. The `predict` functions are available for basic -eigenvector methods in **vegan** (`cca`, `rda`, `decorana`, for an -up-to-date list, use command `methods("predict")`). You also can -simulate the passive points in R by using low weights to row and columns -(this is the method used in software with passive points). For instance, -the following command makes row 3 “passive”: -`dune[3,] <- 0.001*dune[3,]`. - ------------------------------------------------------------------------- - -### Class variables and dummies - -You should define a class variable as an R `factor`, and **vegan** will -automatically handle them with formula interface. You also can define -constrained ordination without formula interface, but then you must code -your class variables by hand. - -R (and **vegan**) knows both unordered and ordered factors. Unordered -factors are internally coded as dummy variables, but one redundant level -is removed or aliased. With default contrasts, the removed level is the -first one. Ordered factors are expressed as polynomial contrasts. Both -of these contrasts explained in standard R documentation. - ------------------------------------------------------------------------- - -### How are environmental arrows scaled? - -The printed output of `envfit` gives the direction cosines which are the -coordinates of unit length arrows. For plotting, these are scaled by -their correlation (square roots of column `r2`). You can see the scaled -lengths of `envfit` arrows using command `scores`. - -The scaled environmental vectors from `envfit` and the arrows for -continuous environmental variables in constrained ordination (`cca`, -`rda`, `capscale`) are adjusted to fill the current graph. The lengths -of arrows do not have fixed meaning with respect to the points (species, -sites), but they can only compared against each other, and therefore -only their relative lengths are important. - -If you want change the scaling of the arrows, you can use `text` -(plotting arrows and text) or `points` (plotting only arrows) functions -for constrained ordination. These functions have argument `arrow.mul` -which sets the multiplier. The `plot` function for `envfit` also has the -`arrow.mul` argument to set the arrow multiplier. If you save the -invisible result of the constrained ordination `plot` command, you can -see the value of the currently used `arrow.mul` which is saved as an -attribute of `biplot` scores. - -Function `ordiArrowMul` is used to find the scaling for the current -plot. You can use this function to see how arrows would be scaled: - - -```{r eval=FALSE} -sol <- cca(varespec) -ef <- envfit(sol ~ ., varechem) -plot(sol) -ordiArrowMul(scores(ef, display="vectors")) -``` - ------------------------------------------------------------------------- - -### I want to use Helmert or sum contrasts - -`vegan` uses standard R utilities for defining contrasts. The default in -standard installations is to use treatment contrasts, but you can change -the behaviour globally setting `options` or locally by using keyword -`contrasts`. Please check the R help pages and user manuals for details. - ------------------------------------------------------------------------- - -### What are aliased variables and how to see them? - -Aliased variable has no information because it can be expressed with the -help of other variables. Such variables are automatically removed in -constrained ordination in **vegan**. The aliased variables can be -redundant levels of factors or whole variables. - -**Vegan** function `alias` gives the defining equations for aliased -variables. If you only want to see the names of aliased variables or -levels in solution `sol`, use `alias(sol, names.only=TRUE)`. - ------------------------------------------------------------------------- - -### Plotting aliased variables - -You can fit vectors or class centroids for aliased variables using -`envfit` function. The `envfit` function uses weighted fitting, and the -fitted vectors are identical to the vectors in correspondence analysis. - ------------------------------------------------------------------------- - -### Restricted permutations in **vegan** - -**Vegan** uses **permute** package in all its permutation tests. The -**permute** package will allow restricted permutation designs for time -series, line transects, spatial grids and blocking factors. The -construction of restricted permutation schemes is explained in the -manual page `permutations` in **vegan** and in the documentation of the -**permute** package. - ------------------------------------------------------------------------- - -### How to use different plotting symbols in ordination graphics? - -The default ordination `plot` function is intended for fast plotting and -it is not very configurable. To use different plotting symbols, you -should first create and empty ordination plot with -`plot(..., type="n")`, and then add `points` or `text` to the created -empty frame (here `...` means other arguments you want to give to your -`plot` command). The `points` and `text` commands are fully -configurable, and allow different plotting symbols and characters. - ------------------------------------------------------------------------- - -### How to avoid cluttered ordination graphs? - -If there is a really high number of species or sites, the graphs often -are congested and many labels are overwritten. It may be impossible to -have complete readable graphics with some data sets. Below we give a -brief overview of tricks you can use. Gavin Simpson’s blog [From the -bottom of the heap](http://www.fromthebottomoftheheap.net) has a series -of articles on “decluttering ordination plots” with more detailed -discussion and examples. - -- Use only points, possibly with different types if you do not need to - see the labels. You may need to first create an empty plot using - `plot(..., type="n")`, if you are not satisfied with the default - graph. (Here and below `...` means other arguments you want to give - to your `plot` command.) -- Use points and add labels to desired points using interactive - `identify` command if you do not need to see all labels. -- Add labels using function `ordilabel` which uses non-transparent - background to the text. The labels still shadow each other, but the - uppermost labels are readable. Argument `priority` will help in - displaying the most interesting labels (see [Decluttering blog, part - 1](http://www.fromthebottomoftheheap.net/2013/01/12/decluttering-ordination-plots-in-vegan-part-1-ordilabel/)). -- Use `orditorp` function that uses labels only if these can be added - to a graph without overwriting other labels, and points otherwise, - if you do not need to see all labels. You must first create an empty - plot using `plot(..., type="n")`, and then add labels or points with - `orditorp` (see [Decluttering - blog](http://www.fromthebottomoftheheap.net/2013/01/13/decluttering-ordination-plots-in-vegan-part-2-orditorp/)). -- Use `ordipointlabel` which uses points and text labels to the - points, and tries to optimize the location of the text to minimize - the overlap (see [Decluttering - blog](http://www.fromthebottomoftheheap.net/2013/06/27/decluttering-ordination-plots-in-vegan-part-3-ordipointlabel/)). -- Ordination `text` and `points` functions have argument `select` that - can be used for full control of selecting items plotted as text or - points. -- Use interactive `orditkplot` function that lets you drag labels of - points to better positions if you need to see all labels. Only one - set of points can be used (see [Decluttering - blog](http://www.fromthebottomoftheheap.net/2013/12/31/decluttering-ordination-in-vegan-part-4-orditkplot/)). -- Most `plot` functions allow you to zoom to a part of the graph using - `xlim` and `ylim` arguments to reduce clutter in congested areas. - ------------------------------------------------------------------------- - -### Can I flip an axis in ordination diagram? - -Use `xlim` or `ylim` with flipped limits. If you have model -`mod <- cca(dune)` you can flip the first axis with -`plot(mod, xlim = c(3, -2))`. - ------------------------------------------------------------------------- - -### Can I zoom into an ordination plot? - -You can use `xlim` and `ylim` arguments in `plot` or `ordiplot` to zoom -into ordination diagrams. Normally you must set both `xlim` and `ylim` -because ordination plots will keep the equal aspect ratio of axes, and -they will fill the graph so that the longer axis will fit. - -Dynamic zooming can be done with function `orditkplot`. You can directly -save the edited `orditkplot` graph in various graphic formats, or you -can export the graph object back to R and use `plot` to display the -results. - ------------------------------------------------------------------------- - -Other analysis methods ----------------------- - ------------------------------------------------------------------------- - -### Is there TWINSPAN? - -No. It may be possible to port TWINSPAN to **vegan**, but it is not -among the **vegan** top priorities. If anybody wants to try porting, I -will be happy to help. TWINSPAN has a very permissive license, and it -would be completely legal to port the function into R. - ------------------------------------------------------------------------- - -### Why restricted permutation does not influence adonis results? - -The permutation scheme influences the permutation distribution of the -statistics and probably the significance levels, but does not influence -the calculation of the statistics. - ------------------------------------------------------------------------- - -### How is deviance calculated? - -Some **vegan** functions, such as `radfit` use base R facility of -`family` in maximum likelihood estimation. This allows use of several -alternative error distributions, among them `"poisson"` and -`"gaussian"`. The R `family` also defines the deviance. You can see the -equations for deviance with commands like `poisson()$dev` or -`gaussian()$dev`. - -In general, deviance is 2 times log.likelihood shifted so that models -with exact fit have zero deviance. - ------------------------------------------------------------------------- diff --git a/scTCRpy/rpackages/vegan/doc/FAQ-vegan.html b/scTCRpy/rpackages/vegan/doc/FAQ-vegan.html deleted file mode 100644 index 9bf495605..000000000 --- a/scTCRpy/rpackages/vegan/doc/FAQ-vegan.html +++ /dev/null @@ -1,927 +0,0 @@ - - - - - -<strong>vegan</strong> FAQ - - - - - - - - - - - - - - - - - - - - -

vegan FAQ

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This document contains answers to some of the most frequently asked -questions about R package vegan.

- -
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This work is licensed under the Creative Commons Attribution 3.0 -License. To view a copy of this license, visit -http://creativecommons.org/licenses/by/3.0/ or send a letter to -Creative Commons, 543 Howard Street, 5th Floor, San Francisco, -California, 94105, USA.

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Copyright © 2008-2016 vegan development team

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Introduction

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What is vegan?

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Vegan is an R package for community ecologists. It contains the most -popular methods of multivariate analysis needed in analysing ecological -communities, and tools for diversity analysis, and other potentially -useful functions. Vegan is not self-contained but it must be run -under R statistical environment, and it also depends on many other R -packages. Vegan is free -software and distributed -under GPL2 license.

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What is R?

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R is a system for statistical computation and graphics. It consists of a -language plus a run-time environment with graphics, a debugger, access -to certain system functions, and the ability to run programs stored in -script files.

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R has a home page at https://www.R-project.org/. It is free -software distributed under -a GNU-style copyleft, and -an official part of the GNU project (“GNU S”).

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How to obtain vegan and R?

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Both R and latest release version of vegan can be obtained through -CRAN. Unstable development version of -vegan can be obtained through -GitHub. Formerly vegan was -developed in R-Forge, -but after moving to GitHub the -R-Forge repository may be out of date.

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What R packages vegan depends on?

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Vegan depends on the permute package which will provide advanced -and flexible permutation routines for vegan. The permute package -is developed together with vegan in -GitHub.

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Some individual vegan functions depend on packages MASS, -mgcv, parallel, cluster, lattice and tcltk. These -all are base or recommended R packages that should be available in -every R installation. Vegan declares these as suggested or -imported packages, and you can install vegan and use most of its -functions without these packages.

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Vegan is accompanied with a supporting package vegan3d for -three-dimensional and dynamic plotting. The vegan3d package needs -non-standard packages rgl and scatterplot3d.

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What other packages are available for ecologists?

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CRAN Task Views include -entries like Environmetrics, Multivariate and Spatial that -describe several useful packages and functions. If you install R package -ctv, you can inspect Task Views from your R session, and -automatically install sets of most important packages.

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What other documentation is available for vegan?

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Vegan is a fully documented R package with standard help pages. -These are the most authoritative sources of documentation (and as a -last resource you can use the force and the read the source, as -vegan is open source). Vegan package ships with other -documents which can be read with browseVignettes("vegan") -command. The documents included in the vegan package are

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  • Vegan NEWS
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  • This document (FAQ-vegan).
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  • Short introduction to basic ordination methods in vegan -(intro-vegan).
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  • Introduction to diversity methods in vegan -(diversity-vegan).
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  • Discussion on design decisions in vegan (decision-vegan).
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  • Description of variance partition procedures in function varpart -(partitioning).
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Web documents outside the package include:

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Is there a Graphical User Interface (GUI) for vegan?

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Roeland Kindt has made package BiodiversityR which provides a GUI -for vegan. The package is available at -CRAN. -It is not a mere GUI for vegan, but adds some new functions and -complements vegan functions in order to provide a workbench for -biodiversity analysis. You can install BiodiversityR using -install.packages("BiodiversityR") or graphical package management menu -in R. The GUI works on Windows, MacOS X and Linux.

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How to cite vegan?

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Use command citation("vegan") in R to see the recommended citation to -be used in publications.

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How to build vegan from sources?

- -

In general, you do not need to build vegan from sources, but binary -builds of release versions are available through -CRAN for Windows and MacOS X. If you use -some other operating systems, you may have to use source packages. -Vegan is a standard R package, and can be built like instructed in R -documentation. Vegan contains source files in C and FORTRAN, and you -need appropriate compilers (which may need more work in Windows and -MacOS X).

- -
- -

Are there binaries for devel versions?

- -

R-Forge runs daily tests -on the devel package, and if passed, it builds source package together -with Windows binaries. However, the R-Forge may be out of date, because -vegan is mainly developed in -GitHub. You can install R-Forge -packages within R with command -install.packages("vegan", repos="http://r-forge.r-project.org/"). If -you use GUI menu entry, you must select or define the R-Forge -repository.

- -
- -

How to report a bug in vegan?

- -

If you think you have found a bug in vegan, you should report it to -vegan maintainers or developers. The preferred forum to report bugs -is GitHub. The bug report -should be so detailed that the bug can be replicated and corrected. -Preferably, you should send an example that causes a bug. If it needs a -data set that is not available in R, you should send a minimal data set -as well. You also should paste the output or error message in your -message. You also should specify which version of vegan you used.

- -

Bug reports are welcome: they are the only way to make vegan -non-buggy.

- -

Please note that you shall not send bug reports to R mailing lists, -since vegan is not a standard R package.

- -
- -

Is it a bug or a feature?

- -

It is not necessarily a bug if some function gives different results -than you expect: That may be a deliberate design decision. It may be -useful to check the documentation of the function to see what was the -intended behaviour. It may also happen that function has an argument to -switch the behaviour to match your expectation. For instance, function -vegdist always calculates quantitative indices (when this is -possible). If you expect it to calculate a binary index, you should use -argument binary = TRUE.

- -
- -

Can I contribute to vegan?

- -

Vegan is dependent on user contribution. All feedback is welcome. If -you have problems with vegan, it may be as simple as incomplete -documentation, and we shall do our best to improve the documents.

- -

Feature requests also are welcome, but they are not necessarily -fulfilled. A new feature will be added if it is easy to do and it looks -useful, or if you submit code.

- -

If you can write code yourself, the best forum to contribute to vegan is -GitHub.

- -
- -

Ordination

- -
- -

I have only numeric and positive data but vegan still complains

- -

You are wrong! Computers are painfully pedantic, and if they find -non-numeric or negative data entries, you really have them. Check your -data. Most common reasons for non-numeric data are that row names were -read as a non-numeric variable instead of being used as row names (check -argument row.names in reading the data), or that the column names were -interpreted as data (check argument header = TRUE in reading the -data). Another common reason is that you had empty cells in your input -data, and these were interpreted as missing values.

- -
- -

Can I analyse binary or cover class data?

- -

Yes. Most vegan methods can handle binary data or cover abundance -data. Most statistical tests are based on permutation, and do not make -distributional assumptions. There are some methods (mainly in diversity -analysis) that need count data. These methods check that input data are -integers, but they may be fooled by cover class data.

- -
- -

Why dissimilarities in vegan differ from other sources?

- -

Most commonly the reason is that other software use presence–absence -data whereas vegan used quantitative data. Usually vegan indices -are quantitative, but you can use argument binary = TRUE to make them -presence–absence. However, the index name is the same in both cases, -although different names usually occur in literature. For instance, -Jaccard index actually refers to the binary index, but vegan uses -name "jaccard" for the quantitative index, too.

- -

Another reason may be that indices indeed are defined differently, -because people use same names for different indices.

- -
- -

Why NMDS stress is sometimes 0.1 and sometimes 10?

- -

Stress is a proportional measure of badness of fit. The proportions can -be expressed either as parts of one or as percents. Function isoMDS -(MASS package) uses percents, and function monoMDS (vegan -package) uses proportions, and therefore the same stress is 100 times -higher in isoMDS. The results of goodness function also depend on -the definition of stress, and the same goodness is 100 times higher in -isoMDS than in monoMDS. Both of these conventions are equally -correct.

- -
- -

I get zero stress but no convergent solutions in metaMDS

- -

Most common reason is that you have too few observations for your NMDS. -For n observations (points) and k dimensions you need to estimate -n*k parameters (ordination scores) using n*(n-1)/2 dissimilarities. -For k dimensions you must have n > 2*k + 1, or for two dimensions at -least six points. In some degenerate situations you may need even a -larger number of points. If you have a lower number of points, you can -find an undefined number of perfect (stress is zero) but different -solutions. Conventional wisdom due to Kruskal is that you should have -n > 4*k + 1 points for k dimensions. A typical symptom of -insufficient data is that you have (nearly) zero stress but no two -convergent solutions. In those cases you should reduce the number of -dimensions (k) and with very small data sets you should not use -NMDS, but rely on metric methods.

- -

It seems that local and hybrid scaling with monoMDS have similar lower -limits in practice (although theoretically they could differ). However, -higher number of dimensions can be used in metric scaling, both with -monoMDS and in principal coordinates analysis (cmdscale in -stats, wcmdscale in vegan).

- -
- -

Zero dissimilarities in isoMDS

- -

Function metaMDS uses function monoMDS as its default method for -NMDS, and this function can handle zero dissimilarities. Alternative -function isoMDS cannot handle zero dissimilarities. If you want to use -isoMDS, you can use argument zerodist = "add" in metaMDS to handle -zero dissimilarities. With this argument, zero dissimilarities are -replaced with a small positive value, and they can be handled in -isoMDS. This is a kluge, and some people do not like this. A more -principal solution is to remove duplicate sites using R command -unique. However, after some standardizations or with some -dissimilarity indices, originally non-unique sites can have zero -dissimilarity, and you have to resort to the kluge (or work harder with -your data). Usually it is better to use monoMDS.

- -
- -

I have heard that you cannot fit environmental vectors or surfaces to NMDS results which only have rank-order scores

- -

Claims like this have indeed been at large in the Internet, but they are -based on grave misunderstanding and are plainly wrong. NMDS ordination -results are strictly metric, and in vegan metaMDS and monoMDS -they are even strictly Euclidean. The method is called “non-metric” -because the Euclidean distances in ordination space have a non-metric -rank-order relationship to community dissimilarities. You can inspect -this non-linear step curve using function stressplot in vegan. -Because the ordination scores are strictly Euclidean, it is correct to -use vegan functions envfit and ordisurf with NMDS results.

- -
- -

Where can I find numerical scores of ordination axes?

- -

Normally you can use function scores to extract ordination scores for -any ordination method. The scores function can also find ordination -scores for many non-vegan functions such as for prcomp and -princomp and for some ade4 functions.

- -

In some cases the ordination result object stores raw scores, and the -axes are also scaled appropriate when you access them with scores. For -instance, in cca and rda the ordination object has only so-called -normalized scores, and they are scaled for ordination plots or for other -use when they are accessed with scores.

- -
- -

How the RDA results are scaled?

- -

The scaling or RDA results indeed differ from most other software -packages. The scaling of RDA is such a complicated issue that it cannot -be explained in this FAQ, but it is explained in a separate pdf document -on “Design decision and implementation details in vegan” that you can -read with command browseVignettes("vegan").

- -
- -

I cannot print and plot RDA results properly

- -

If the RDA ordination results have a weird format or you cannot plot -them properly, you probably have a name clash with klaR package -which also has function rda, and the klaR print, plot or -predict functions are used for vegan RDA results. You can choose -between rda functions using vegan::rda() or klaR::rda(): you -will get obscure error messages if you use the wrong function. In -general, vegan should be able to work normally if vegan was -loaded after klaR, but if klaR was loaded later, its functions -will take precedence over vegan. Sometimes vegan namespace is -loaded automatically when restoring a previously stored workspace at -the start-up, and then klaR methods will always take precedence -over vegan. You should check your loaded packages. klaR may be -also loaded indirectly via other packages (in the reported cases it -was most often loaded via agricolae package). Vegan and -klaR both have the same function name (rda), and it may not be -possible to use these packages simultaneously, and the safest choice -is to unload one of the packages if only possible. See discussion in -vegan github issues.

- -

If you have a very old version of ade4 (prior to 1.7-8), you may -have similar name clashes with cca. The solution is to upgrade -ade4.

- -
- -

Ordination fails with “Error in La.svd”

- -

Constrained ordination (cca, rda, capscale) will sometimes fail -with error message -Error in La.svd(x, nu, nv): error code 1 from Lapack routine 'dgesdd'.

- -

It seems that the basic problem is in the svd function of LAPACK -that is used for numerical analysis in R. LAPACK is an external -library that is beyond the control of package developers and R core team -so that these problems may be unsolvable. It seems that the problems -with the LAPACK code are so common that even the help page of svd -warns about them

- -

Reducing the range of constraints (environmental variables) helps -sometimes. For instance, multiplying constraints by a constant < 1. -This rescaling does not influence the numerical results of constrained -ordination, but it can complicate further analyses when values of -constraints are needed, because the same scaling must be applied there. -We can only hope that this problem is fixed in the future versions of R -and LAPACK.

- -
- -

Variance explained by ordination axes.

- -

In general, vegan does not directly give any statistics on the -“variance explained” by ordination axes or by the constrained axes. This -is a design decision: I think this information is normally useless and -often misleading. In community ordination, the goal typically is not to -explain the variance, but to find the “gradients” or main trends in the -data. The “total variation” often is meaningless, and all proportions of -meaningless values also are meaningless. Often a better solution -explains a smaller part of “total variation”. For instance, in -unstandardized principal components analysis most of the variance is -generated by a small number of most abundant species, and they are easy -to “explain” because data really are not very multivariate. If you -standardize your data, all species are equally important. The first axes -explains much less of the “total variation”, but now they explain all -species equally, and results typically are much more useful for the -whole community. Correspondence analysis uses another measure of -variation (which is not variance), and again it typically explains a -“smaller proportion” than principal components but with a better result. -Detrended correspondence analysis and nonmetric multidimensional scaling -even do not try to “explain” the variation, but use other criteria. All -methods are incommensurable, and it is impossible to compare methods -using “explanation of variation”.

- -

If you still want to get “explanation of variation” (or a deranged -editor requests that from you), it is possible to get this information -for some methods:

- -
    -
  • Eigenvector methods: Functions rda, cca and capscale give the -variation of conditional (partialled), constrained (canonical) and -residual components, but you must calculate the proportions by hand. -Function eigenvals extracts the eigenvalues, and -summary(eigenvals(ord)) reports the proportions explained in the -result object ord. Function RsquareAdj gives the R-squared and -adjusted R-squared (if available) for constrained components. -Function goodness gives the same statistics for individual species -or sites (species are unavailable with capscale). In addition, -there is a special function varpart for unbiased partitioning of -variance between up to four separate components in redundancy -analysis.
    • -
  • Detrended correspondence analysis (function decorana). The total -amount of variation is undefined in detrended correspondence -analysis, and therefore proportions from total are unknown and -undefined. DCA is not a method for decomposition of variation, and -therefore these proportions would not make sense either.
    • -
  • Nonmetric multidimensional scaling. NMDS is a method for nonlinear -mapping, and the concept of of variation explained does not make -sense. However, 1 - stress^2 transforms nonlinear stress into -quantity analogous to squared correlation coefficient. Function -stressplot displays the nonlinear fit and gives this statistic.
    • -
- -
- -

Can I have random effects in constrained ordination or in adonis?

- -

No. Strictly speaking, this is impossible. However, you can define -models that respond to similar goals as random effects models, although -they strictly speaking use only fixed effects.

- -

Constrained ordination functions cca, rda and capscale can have -Condition() terms in their formula. The Condition() define partial -terms that are fitted before other constraints and can be used to remove -the effects of background variables, and their contribution to -decomposing inertia (variance) is reported separately. These partial -terms are often regarded as similar to random effects, but they are -still fitted in the same way as other terms and strictly speaking they -are fixed terms.

- -

Function adonis evaluates terms sequentially. In a model with -right-hand-side ~ A + B the effects of A are evaluated first, and -the effects of B after removing the effects of A. Sequential tests -are also available in anova function for constrained ordination -results by setting argument by = "term". In this way, the first terms -can serve in a similar role as random effects, although they are fitted -in the same way as all other terms, and strictly speaking they are fixed -terms.

- -

All permutation tests in vegan are based on the permute package -that allows constructing various restricted permutation schemes. For -instance, you can set levels of plots or blocks for a factor -regarded as a random term.

- -

A major reason why real random effects models are impossible in most -vegan functions is that their tests are based on the permutation of -the data. The data are given, that is fixed, and therefore permutation -tests are basically tests of fixed terms on fixed data. Random effect -terms would require permutations of data with a random component instead -of the given, fixed data, and such tests are not available in vegan.

- -
- -

Is it possible to have passive points in ordination?

- -

Vegan does not have a concept of passive points, or a point that -should only little influence the ordination results. However, you can -add points to eigenvector methods using predict functions with -newdata. You can first perform an ordination without some species or -sites, and then you can find scores for all points using your complete -data as newdata. The predict functions are available for basic -eigenvector methods in vegan (cca, rda, decorana, for an -up-to-date list, use command methods("predict")). You also can -simulate the passive points in R by using low weights to row and columns -(this is the method used in software with passive points). For instance, -the following command makes row 3 “passive”: -dune[3,] <- 0.001*dune[3,].

- -
- -

Class variables and dummies

- -

You should define a class variable as an R factor, and vegan will -automatically handle them with formula interface. You also can define -constrained ordination without formula interface, but then you must code -your class variables by hand.

- -

R (and vegan) knows both unordered and ordered factors. Unordered -factors are internally coded as dummy variables, but one redundant level -is removed or aliased. With default contrasts, the removed level is the -first one. Ordered factors are expressed as polynomial contrasts. Both -of these contrasts explained in standard R documentation.

- -
- -

How are environmental arrows scaled?

- -

The printed output of envfit gives the direction cosines which are the -coordinates of unit length arrows. For plotting, these are scaled by -their correlation (square roots of column r2). You can see the scaled -lengths of envfit arrows using command scores.

- -

The scaled environmental vectors from envfit and the arrows for -continuous environmental variables in constrained ordination (cca, -rda, capscale) are adjusted to fill the current graph. The lengths -of arrows do not have fixed meaning with respect to the points (species, -sites), but they can only compared against each other, and therefore -only their relative lengths are important.

- -

If you want change the scaling of the arrows, you can use text -(plotting arrows and text) or points (plotting only arrows) functions -for constrained ordination. These functions have argument arrow.mul -which sets the multiplier. The plot function for envfit also has the -arrow.mul argument to set the arrow multiplier. If you save the -invisible result of the constrained ordination plot command, you can -see the value of the currently used arrow.mul which is saved as an -attribute of biplot scores.

- -

Function ordiArrowMul is used to find the scaling for the current -plot. You can use this function to see how arrows would be scaled:

- -
sol <- cca(varespec)
-ef <- envfit(sol ~ ., varechem)
-plot(sol)
-ordiArrowMul(scores(ef, display="vectors"))
-
- -
- -

I want to use Helmert or sum contrasts

- -

vegan uses standard R utilities for defining contrasts. The default in -standard installations is to use treatment contrasts, but you can change -the behaviour globally setting options or locally by using keyword -contrasts. Please check the R help pages and user manuals for details.

- -
- -

What are aliased variables and how to see them?

- -

Aliased variable has no information because it can be expressed with the -help of other variables. Such variables are automatically removed in -constrained ordination in vegan. The aliased variables can be -redundant levels of factors or whole variables.

- -

Vegan function alias gives the defining equations for aliased -variables. If you only want to see the names of aliased variables or -levels in solution sol, use alias(sol, names.only=TRUE).

- -
- -

Plotting aliased variables

- -

You can fit vectors or class centroids for aliased variables using -envfit function. The envfit function uses weighted fitting, and the -fitted vectors are identical to the vectors in correspondence analysis.

- -
- -

Restricted permutations in vegan

- -

Vegan uses permute package in all its permutation tests. The -permute package will allow restricted permutation designs for time -series, line transects, spatial grids and blocking factors. The -construction of restricted permutation schemes is explained in the -manual page permutations in vegan and in the documentation of the -permute package.

- -
- -

How to use different plotting symbols in ordination graphics?

- -

The default ordination plot function is intended for fast plotting and -it is not very configurable. To use different plotting symbols, you -should first create and empty ordination plot with -plot(..., type="n"), and then add points or text to the created -empty frame (here ... means other arguments you want to give to your -plot command). The points and text commands are fully -configurable, and allow different plotting symbols and characters.

- -
- -

How to avoid cluttered ordination graphs?

- -

If there is a really high number of species or sites, the graphs often -are congested and many labels are overwritten. It may be impossible to -have complete readable graphics with some data sets. Below we give a -brief overview of tricks you can use. Gavin Simpson’s blog From the -bottom of the heap has a series -of articles on “decluttering ordination plots” with more detailed -discussion and examples.

- -
    -
  • Use only points, possibly with different types if you do not need to -see the labels. You may need to first create an empty plot using -plot(..., type="n"), if you are not satisfied with the default -graph. (Here and below ... means other arguments you want to give -to your plot command.)
    • -
  • Use points and add labels to desired points using interactive -identify command if you do not need to see all labels.
    • -
  • Add labels using function ordilabel which uses non-transparent -background to the text. The labels still shadow each other, but the -uppermost labels are readable. Argument priority will help in -displaying the most interesting labels (see Decluttering blog, part -1).
    • -
  • Use orditorp function that uses labels only if these can be added -to a graph without overwriting other labels, and points otherwise, -if you do not need to see all labels. You must first create an empty -plot using plot(..., type="n"), and then add labels or points with -orditorp (see Decluttering -blog).
    • -
  • Use ordipointlabel which uses points and text labels to the -points, and tries to optimize the location of the text to minimize -the overlap (see Decluttering -blog).
    • -
  • Ordination text and points functions have argument select that -can be used for full control of selecting items plotted as text or -points.
    • -
  • Use interactive orditkplot function that lets you drag labels of -points to better positions if you need to see all labels. Only one -set of points can be used (see Decluttering -blog).
    • -
  • Most plot functions allow you to zoom to a part of the graph using -xlim and ylim arguments to reduce clutter in congested areas.
    • -
- -
- -

Can I flip an axis in ordination diagram?

- -

Use xlim or ylim with flipped limits. If you have model -mod <- cca(dune) you can flip the first axis with -plot(mod, xlim = c(3, -2)).

- -
- -

Can I zoom into an ordination plot?

- -

You can use xlim and ylim arguments in plot or ordiplot to zoom -into ordination diagrams. Normally you must set both xlim and ylim -because ordination plots will keep the equal aspect ratio of axes, and -they will fill the graph so that the longer axis will fit.

- -

Dynamic zooming can be done with function orditkplot. You can directly -save the edited orditkplot graph in various graphic formats, or you -can export the graph object back to R and use plot to display the -results.

- -
- -

Other analysis methods

- -
- -

Is there TWINSPAN?

- -

No. It may be possible to port TWINSPAN to vegan, but it is not -among the vegan top priorities. If anybody wants to try porting, I -will be happy to help. TWINSPAN has a very permissive license, and it -would be completely legal to port the function into R.

- -
- -

Why restricted permutation does not influence adonis results?

- -

The permutation scheme influences the permutation distribution of the -statistics and probably the significance levels, but does not influence -the calculation of the statistics.

- -
- -

How is deviance calculated?

- -

Some vegan functions, such as radfit use base R facility of -family in maximum likelihood estimation. This allows use of several -alternative error distributions, among them "poisson" and -"gaussian". The R family also defines the deviance. You can see the -equations for deviance with commands like poisson()$dev or -gaussian()$dev.

- -

In general, deviance is 2 times log.likelihood shifted so that models -with exact fit have zero deviance.

- -
- - - - diff --git a/scTCRpy/rpackages/vegan/doc/decision-vegan.R b/scTCRpy/rpackages/vegan/doc/decision-vegan.R deleted file mode 100644 index cbf3598ce..000000000 --- a/scTCRpy/rpackages/vegan/doc/decision-vegan.R +++ /dev/null @@ -1,174 +0,0 @@ -### R code from vignette source 'decision-vegan.Rnw' - -################################################### -### code chunk number 1: decision-vegan.Rnw:21-26 -################################################### -figset <- function() par(mar=c(4,4,1,1)+.1) -options(SweaveHooks = list(fig = figset)) -options("prompt" = "> ", "continue" = " ") -options(width = 55) -require(vegan) - - -################################################### -### code chunk number 2: decision-vegan.Rnw:84-85 (eval = FALSE) -################################################### -## options(mc.cores = 2) - - -################################################### -### code chunk number 3: decision-vegan.Rnw:125-136 (eval = FALSE) -################################################### -## ## start up and define meandist() -## library(vegan) -## data(sipoo) -## meandist <- -## function(x) mean(vegdist(x, "bray")) -## library(parallel) -## clus <- makeCluster(4) -## clusterEvalQ(clus, library(vegan)) -## mbc1 <- oecosimu(dune, meandist, "r2dtable", -## parallel = clus) -## stopCluster(clus) - - -################################################### -### code chunk number 4: decision-vegan.Rnw:240-251 -################################################### -getOption("SweaveHooks")[["fig"]]() -data(sipoo) -mod <- nestedtemp(sipoo) -plot(mod, "i") -x <- mod$c["Falcsubb"] -y <- 1 - mod$r["Svartholm"] -points(x,y, pch=16, cex=1.5) -abline(x+y, -1, lty=2) -f <- function(x, p) (1-(1-x)^p)^(1/p) -cross <- function(x, a, p) f(x,p) - a + x -r <- uniroot(cross, c(0,1), a = x+y, p = mod$p)$root -arrows(x,y, r, f(r, mod$p), lwd=4) - - -################################################### -### code chunk number 5: decision-vegan.Rnw:549-553 -################################################### -library(vegan) -data(varespec) -data(varechem) -orig <- cca(varespec ~ Al + K, varechem) - - -################################################### -### code chunk number 6: a -################################################### -plot(orig, dis=c("lc","bp")) - - -################################################### -### code chunk number 7: decision-vegan.Rnw:562-563 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(orig, dis=c("lc","bp")) - - -################################################### -### code chunk number 8: decision-vegan.Rnw:572-574 -################################################### -i <- sample(nrow(varespec)) -shuff <- cca(varespec[i,] ~ Al + K, varechem) - - -################################################### -### code chunk number 9: decision-vegan.Rnw:577-578 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(shuff, dis=c("lc","bp")) - - -################################################### -### code chunk number 10: a -################################################### -plot(procrustes(scores(orig, dis="lc"), - scores(shuff, dis="lc"))) - - -################################################### -### code chunk number 11: decision-vegan.Rnw:591-592 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(procrustes(scores(orig, dis="lc"), - scores(shuff, dis="lc"))) - - -################################################### -### code chunk number 12: decision-vegan.Rnw:600-603 -################################################### -tmp1 <- rda(varespec ~ Al + K, varechem) -i <- sample(nrow(varespec)) # Different shuffling -tmp2 <- rda(varespec[i,] ~ Al + K, varechem) - - -################################################### -### code chunk number 13: decision-vegan.Rnw:606-608 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(procrustes(scores(tmp1, dis="lc"), - scores(tmp2, dis="lc"))) - - -################################################### -### code chunk number 14: decision-vegan.Rnw:625-627 -################################################### -orig -shuff - - -################################################### -### code chunk number 15: decision-vegan.Rnw:632-633 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(procrustes(orig, shuff)) - - -################################################### -### code chunk number 16: decision-vegan.Rnw:646-651 -################################################### -tmp1 <- rda(varespec ~ ., varechem) -tmp2 <- rda(varespec[i,] ~ ., varechem) -proc <- procrustes(scores(tmp1, dis="lc", choi=1:14), - scores(tmp2, dis="lc", choi=1:14)) -max(residuals(proc)) - - -################################################### -### code chunk number 17: decision-vegan.Rnw:663-666 -################################################### -data(dune) -data(dune.env) -orig <- cca(dune ~ Moisture, dune.env) - - -################################################### -### code chunk number 18: decision-vegan.Rnw:671-672 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(orig, dis="lc") - - -################################################### -### code chunk number 19: a -################################################### -plot(orig, display="wa", type="points") -ordispider(orig, col="red") -text(orig, dis="cn", col="blue") - - -################################################### -### code chunk number 20: decision-vegan.Rnw:696-697 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(orig, display="wa", type="points") -ordispider(orig, col="red") -text(orig, dis="cn", col="blue") - - diff --git a/scTCRpy/rpackages/vegan/doc/decision-vegan.Rnw b/scTCRpy/rpackages/vegan/doc/decision-vegan.Rnw deleted file mode 100644 index 09a50322d..000000000 --- a/scTCRpy/rpackages/vegan/doc/decision-vegan.Rnw +++ /dev/null @@ -1,722 +0,0 @@ -% -*- mode: noweb; noweb-default-code-mode: R-mode; -*- -%\VignetteIndexEntry{Design decisions and implementation} - -\documentclass[a4paper,10pt,twocolumn]{article} -\usepackage{vegan} % package options and redefinitions - -\author{Jari Oksanen} -\title{Design decisions and implementation details in vegan} - -\date{\footnotesize{ - processed with vegan -\Sexpr{packageDescription("vegan", field="Version")} -in \Sexpr{R.version.string} on \today}} - -%% need no \usepackage{Sweave} -\begin{document} -\bibliographystyle{jss} - -\SweaveOpts{strip.white=true} - -<>= -figset <- function() par(mar=c(4,4,1,1)+.1) -options(SweaveHooks = list(fig = figset)) -options("prompt" = "> ", "continue" = " ") -options(width = 55) -require(vegan) -@ -\maketitle - -\begin{abstract} - This document describes design decisions, and discusses implementation -and algorithmic details in some vegan functions. The proper FAQ is -another document. -\end{abstract} - -\tableofcontents - -\section{Parallel processing} - -Several \pkg{vegan} functions can perform parallel processing using -the standard \R{} package \pkg{parallel}. -The \pkg{parallel} package in \R{} implements -the functionality of earlier contributed packages \pkg{multicore} and -\pkg{snow}. The \pkg{multicore} functionality forks the analysis to -multiple cores, and \pkg{snow} functionality sets up a socket cluster -of workers. The \pkg{multicore} functionality only works in unix-like -systems (such as MacOS and Linux), but \pkg{snow} functionality works -in all operating systems. \pkg{Vegan} can use either method, but -defaults to \pkg{multicore} functionality when this is available, -because its forked clusters are usually faster. This chapter -describes both the user interface and internal implementation for the -developers. - -\subsection{User interface} -\label{sec:parallel:ui} - -The functions that are capable of parallel processing have argument -\code{parallel}. The normal default is \code{parallel = 1} which -means that no parallel processing is performed. It is possible to set -parallel processing as the default in \pkg{vegan} (see -\S\,\ref{sec:parallel:default}). - -For parallel processing, the \code{parallel} argument can be either - -\begin{enumerate} -\item An integer in which case the given number of parallel processes - will be launched (value $1$ launches non-parallel processing). In - unix-like systems (\emph{e.g.}, MacOS, Linux) these will be forked - \code{multicore} processes. In Windows socket clusters will be set up, - initialized and closed. -\item A previously created socket cluster. This saves time as the - cluster is not set up and closed in the function. If the argument is a - socket cluster, it will also be used in unix-like systems. Setting - up a socket cluster is discussed in \S\,\ref{sec:parallel:socket}. -\end{enumerate} - -\subsubsection{Using parallel processing as default} -\label{sec:parallel:default} - -If the user sets option \code{mc.cores}, its value will be used as the -default value of the \code{parallel} argument in \pkg{vegan} -functions. The following command will set up parallel processing to -all subsequent \pkg{vegan} commands: -<>= -options(mc.cores = 2) -@ - -The \code{mc.cores} option is defined in the \pkg{parallel} package, -but it is usually unset in which case \pkg{vegan} will default to -non-parallel computation. The \code{mc.cores} option can be set by -the environmental variable \code{MC_CORES} when the \pkg{parallel} -package is loaded. - -\R{} allows setting up a default socket cluster -(\code{setDefaultCluster}), but this will not be used in \pkg{vegan}. - -\subsubsection{Setting up socket clusters} -\label{sec:parallel:socket} - -If socket clusters are used (and they are the only alternative in -Windows), it is often wise to set up a cluster before calling -parallelized code and give the pre-defined cluster as the value of -the \code{parallel} argument in \pkg{vegan}. If you want to use -socket clusters in unix-like systems (MacOS, Linux), this can be only -done with pre-defined clusters. - -If socket cluster is not set up in Windows, \pkg{vegan} will create and -close the cluster within the function body. This involves following commands: -\begin{Schunk} -\begin{Soutput} -clus <- makeCluster(4) -## perform parallel processing -stopCluster(clus) -\end{Soutput} -\end{Schunk} -The first command sets up the cluster, in this case with four -cores, and the second command stops the cluster. - -Most parallelized \pkg{vegan} functions work similarly in socket and -fork clusters, but in \code{oecosimu} the parallel processing is used -to evaluate user-defined functions, and their arguments and data must -be made known to the socket cluster. For example, if you want to run -in parallel the \code{meandist} function of the \code{oecosimu} -example with a pre-defined socket cluster, you must use: -<>= -## start up and define meandist() -library(vegan) -data(sipoo) -meandist <- - function(x) mean(vegdist(x, "bray")) -library(parallel) -clus <- makeCluster(4) -clusterEvalQ(clus, library(vegan)) -mbc1 <- oecosimu(dune, meandist, "r2dtable", - parallel = clus) -stopCluster(clus) -@ -Socket clusters are used for parallel processing in Windows, but you -do not need to pre-define the socket cluster in \code{oecosimu} if you -only need \pkg{vegan} commands. However, if you need some other -contributed packages, you must pre-define the socket cluster also in -Windows with appropriate \code{clusterEvalQ} calls. - -If you pre-set the cluster, you can also use \pkg{snow} style socket -clusters in unix-like systems. - -\subsubsection{Random number generation} - -\pkg{Vegan} does not use parallel processing in random number -generation, and you can set the seed for the standard random number -generator. Setting the seed for the parallelized generator (L'Ecuyer) -has no effect in \pkg{vegan}. - -\subsubsection{Does it pay off?} - -Parallelized processing has a considerable overhead, and the analysis -is faster only if the non-parallel code is really slow (takes several -seconds in wall clock time). The overhead is particularly large in -socket clusters (in Windows). Creating a socket cluster and evaluating -\code{library(vegan)} with \code{clusterEvalQ} can take two seconds or -longer, and only pays off if the non-parallel analysis takes ten -seconds or longer. Using pre-defined clusters will reduce the -overhead. Fork clusters (in unix-likes operating systems) have a -smaller overhead and can be faster, but they also have an overhead. - -Each parallel process needs memory, and for a large number of -processes you need much memory. If the memory is exhausted, the -parallel processes can stall and take much longer than -non-parallel processes (minutes instead of seconds). - -If the analysis is fast, and function runs in, say, less than five -seconds, parallel processing is rarely useful. Parallel processing is -useful only in slow analyses: large number of replications or -simulations, slow evaluation of each simulation. The danger of memory -exhaustion must always be remembered. - -The benefits and potential problems of parallel processing depend on -your particular system: it is best to rely on your own experience. - -\subsection{Internals for developers} - -The implementation of the parallel processing should accord with the -description of the user interface above (\S\,\ref{sec:parallel:ui}). -Function \code{oecosimu} can be used as a reference implementation, -and similar interpretation and order of interpretation of arguments -should be followed. All future implementations should be consistent -and all must be changed if the call heuristic changes. - -The value of the \code{parallel} argument can be \code{NULL}, a -positive integer or a socket cluster. Integer $1$ means that no -parallel processing is performed. The ``normal'' default is -\code{NULL} which in the ``normal'' case is interpreted as $1$. Here -``normal'' means that \R{} is run with default settings without -setting \code{mc.cores} or environmental variable \code{MC_CORES}. - -Function \code{oecosimu} interprets the \code{parallel} arguments in -the following way: -\begin{enumerate} -\item \code{NULL}: The function is called with argument \code{parallel - = getOption("mc.cores")}. The option \code{mc.cores} is normally - unset and then the default is \code{parallel = NULL}. -\item Integer: An integer value is taken as the number of created - parallel processes. In unix-like systems this is the number of - forked multicore processes, and in Windows this is the number of - workers in socket clusters. In Windows, the socket cluster is - created, and if needed \code{library(vegan)} is evaluated in the - cluster (this is not necessary if the function only uses internal - functions), and the cluster is stopped after parallel processing. -\item Socket cluster: If a socket cluster is given, it will be used in - all operating systems, and the cluster is not stopped - within the function. -\end{enumerate} - -This gives the following precedence order for parallel processing -(highest to lowest): -\begin{enumerate} - \item Explicitly given argument value of \code{parallel} will always - be used. - \item If \code{mc.cores} is set, it will be used. In Windows this - means creating and stopping socket clusters. Please note - that the \code{mc.cores} is only set from the environmental - variable \code{MC_CORES} when you load the \pkg{parallel} package, - and it is always unset before first - \code{require(parallel)}. - \item The fall back behaviour is no parallel processing. -\end{enumerate} - -\section{Nestedness and Null models} - -Some published indices of nestedness and null models of communities -are only described in general terms, and they could be implemented in -various ways. Here I discuss the implementation in \pkg{vegan}. - -\subsection{Matrix temperature} - -The matrix temperature is intuitively simple -(Fig. \ref{fig:nestedtemp}), but the the exact calculations were not -explained in the original publication \cite{AtmarPat93}. -\begin{figure} -<>= -data(sipoo) -mod <- nestedtemp(sipoo) -plot(mod, "i") -x <- mod$c["Falcsubb"] -y <- 1 - mod$r["Svartholm"] -points(x,y, pch=16, cex=1.5) -abline(x+y, -1, lty=2) -f <- function(x, p) (1-(1-x)^p)^(1/p) -cross <- function(x, a, p) f(x,p) - a + x -r <- uniroot(cross, c(0,1), a = x+y, p = mod$p)$root -arrows(x,y, r, f(r, mod$p), lwd=4) -@ -\caption{Matrix temperature for \emph{Falco subbuteo} on Sibbo - Svartholmen (dot). The curve is the fill line, and in a cold - matrix, all presences (red squares) should be in the upper left - corner behind the fill line. Dashed diagonal line of length $D$ goes - through the point, and an arrow of length $d$ connects the point to - the fill line. The ``surprise'' for this point is $u = (d/D)^2$ and - the matrix temperature is based on the sum of surprises: presences - outside the fill line or absences within the fill line.} -\label{fig:nestedtemp} -\end{figure} -The function can be implemented in many ways following the general -principles. \citet{RodGir06} have seen the original code and reveal -more details of calculations, and their explanation is the basis of -the implementation in \pkg{vegan}. However, there are still some open -issues, and probably \pkg{vegan} function \code{nestedtemp} will never -exactly reproduce results from other programs, although it is based on -the same general principles.\footnote{function \code{nestedness} in - the \pkg{bipartite} package is a direct port of the original - \proglang{BINMATNEST} program of \citet{RodGir06}.} I try to give -main computation details in this document --- all details can be seen -in the source code of \code{nestedtemp}. - -\begin{itemize} -\item Species and sites are put into unit square \citep{RodGir06}. The - row and column coordinates will be $(k-0.5)/n$ for $k=1 \ldots n$, - so that there are no points in the corners or the margins of the - unit square, and a diagonal line can be drawn through any point. I - do not know how the rows and columns are converted to the unit - square in other software, and this may be a considerable source of - differences among implementations. - \item Species and sites are ordered alternately using indices - \citep{RodGir06}: - \begin{equation} - \begin{split} - s_j &= \sum_{i|x_{ij} = 1} i^2 \\ - t_j &= \sum_{i|x_{ij} = 0} (n-i+1)^2 - \end{split} - \end{equation} - Here $x$ is the data matrix, where $1$ is presence, and $0$ is - absence, $i$ and $j$ are row and column indices, and $n$ is the - number of rows. The equations give the indices for columns, but - the indices can be reversed for corresponding row indexing. - Ordering by $s$ packs presences to the top left corner, and - ordering by $t$ pack zeros away from the top left corner. The final - sorting should be ``a compromise'' \citep{RodGir06} between these - scores, and \pkg{vegan} uses $s+t$. The result should be cool, - but the packing does not try to minimize the temperature - \citep{RodGir06}. I do not know how the ``compromise'' is - defined, and this can cause some differences to other - implementations. - \item The following function is used to define the fill line: - \begin{equation} - y = (1-(1-x)^p)^{1/p} - \end{equation} - This is similar to the equation suggested by - \citet[eq. 4]{RodGir06}, but omits all terms dependent on the - numbers of species or sites, because I could not understand why - they were needed. The differences are visible only in small data - sets. The $y$ and $x$ are the coordinates in the unit square, and - the parameter $p$ is selected so that the curve covers the same - area as is the proportion of presences - (Fig. \ref{fig:nestedtemp}). The parameter $p$ is found - numerically using \proglang{R} functions \code{integrate} and - \code{uniroot}. The fill line used in the original matrix - temperature software \citep{AtmarPat93} is supposed to be similar - \citep{RodGir06}. Small details in the fill line combined with - differences in scores used in the unit square (especially in the - corners) can cause large differences in the results. - \item A line with slope\,$= -1$ is drawn through the point and the $x$ - coordinate of the intersection of this line and the fill line is - found using function \code{uniroot}. The difference of this - intersection and the row coordinate gives the argument $d$ of matrix - temperature (Fig. \ref{fig:nestedtemp}). - \item In other software, ``duplicated'' species occurring on every - site are removed, as well as empty sites and species after - reordering \cite{RodGir06}. This is not done in \pkg{vegan}. -\end{itemize} - - -\section{Scaling in redundancy analysis} - -This chapter discusses the scaling of scores (results) in redundancy -analysis and principal component analysis performed by function -\code{rda} in the \pkg{vegan} library. - -Principal component analysis decomposes a centred data matrix -$\mathbf{X} = \{x_{ij}\}$ into $K$ orthogonal components so that -$x_{ij} = \sqrt{n-1} \sum_{k=1}^K u_{ik} \sqrt{\lambda_k} v_{jk}$, -where $u_{ik}$ and $v_{jk}$ are orthonormal coefficient matrices and -$\lambda_k$ are eigenvalues. In \pkg{vegan} the eigenvalues sum up to -variance of the data, and therefore we need to multiply with the -square root of degrees of freedom $n-1$. Orthonormality means that -sums of squared columns is one and their cross-product is zero, or -$\sum_i u_{ik}^2 = \sum_j v_{jk}^2 = 1$, and -$\sum_i u_{ik} u_{il} = \sum_j v_{jk} v_{jl} = 0$ for $k \neq l$. This -is a decomposition, and the original matrix is found exactly from the -singular vectors and corresponding singular values, and first two -singular components give the rank $=2$ least squares estimate of the -original matrix. - -The coefficients $u_{ik}$ and $v_{jk}$ are scaled to unit length for all -axes $k$. Eigenvalues $\lambda_k$ give -the information of the importance of axes, or the `axis lengths.' -Instead of the orthonormal coefficients, or equal length axes, it is -customary to scale species (column) or site (row) scores or both by -eigenvalues to display the importance of axes and to describe the true -configuration of points. Table \ref{tab:scales} shows some -alternative scalings. These alternatives apply to principal -components analysis in all cases, and in redundancy analysis, they -apply to species scores and constraints or linear combination scores; -weighted averaging scores have somewhat wider dispersion. - -\begin{table*}[t] - \centering - \caption{\label{tab:scales} Alternative scalings for \textsc{rda} used - in the functions \code{prcomp} and \code{princomp}, and the - one used in the \pkg{vegan} function \code{rda} - and the proprietary software \proglang{Canoco} - scores in terms of orthonormal species ($v_{ik}$) and site scores - ($u_{jk}$), eigenvalues ($\lambda_k$), number of sites ($n$) and - species standard deviations ($s_j$). In \code{rda}, - $\mathrm{const} = \sqrt[4]{(n-1) \sum \lambda_k}$. Corresponding - negative scaling in \pkg{vegan} - % and corresponding positive scaling in \texttt{Canoco 3} - is derived - dividing each species by its standard deviation $s_j$ (possibly - with some additional constant multiplier). } - \begin{tabular}{lcc} - \\ - \toprule -& \textbf{Site scores} $u_{ik}^*$ & -\textbf{Species scores} $v_{jk}^*$ \\ -\midrule -\code{prcomp, princomp} & -$u_{ik} \sqrt{n-1} \sqrt{\lambda_k}$ & -$v_{jk}$ \\ -\code{stats::biplot} & -$u_{ik}$ & -$v_{jk} \sqrt{n} \sqrt{\lambda_k}$ \\ -\code{stats::biplot, pc.biplot=TRUE} & -$u_{ik} \sqrt{n-1}$ & -$v_{jk} \sqrt{\lambda_k}$\\ -\code{rda, scaling="sites"} & -$u_{ik} \sqrt{\lambda_k/ \sum \lambda_k} \times \mathrm{const}$ & -$v_{jk} \times \mathrm{const}$ -\\ -\code{rda, scaling="species"} & -$u_{ik} \times \mathrm{const}$ & -$v_{jk} \sqrt{\lambda_k/ \sum \lambda_k} \times \mathrm{const}$ \\ -\code{rda, scaling="symmetric"} & -$u_{ik} \sqrt[4]{\lambda_k/ \sum \lambda_k} \times \mathrm{const}$ & -$v_{jk} \sqrt[4]{\lambda_k/ \sum \lambda_k} \times \mathrm{const}$ \\ -\code{rda, correlation=TRUE} & -$u_{ik}^*$ & -$\sqrt{\sum \lambda_k /(n-1)} s_j^{-1} v_{jk}^*$ -\\ -% \code{Canoco 3, scaling=-1} & -% $u_{ik} \sqrt{n-1} \sqrt{\lambda_k / \sum \lambda_k}$ & -% $v_{jk} \sqrt{n}$ \\ -% \code{Canoco 3, scaling=-2} & -% $u_{ik} \sqrt{n-1}$ & -% $v_{jk} \sqrt{n} \sqrt{\lambda_k / \sum \lambda_k}$ -% \\ -% \code{Canoco 3, scaling=-3} & -% $u_{ik} \sqrt{n-1} \sqrt[4]{\lambda_k / \sum \lambda_k}$ & -% $v_{jk} \sqrt{n} \sqrt[4]{\lambda_k / \sum \lambda_k}$ -\bottomrule -\end{tabular} -\end{table*} - -In community ecology, it is common to plot both species and sites in -the same graph. If this graph is a graphical display of \textsc{pca}, -or a graphical, low-dimensional approximation of the data, the graph -is called a biplot. The graph is a biplot if the transformed scores -satisfy $x_{ij} = c \sum_k u_{ij}^* v_{jk}^*$ where $c$ is a scaling -constant. In functions \code{princomp}, \code{prcomp} and \code{rda} -with \code{scaling = "sites"}, the plotted scores define a biplot so that -the eigenvalues are expressed for sites, and species are left -unscaled. -% For \texttt{Canoco 3} $c = n^{-1} \sqrt{n-1} -% \sqrt{\sum \lambda_k}$ with negative \proglang{Canoco} scaling -% values. All these $c$ are constants for a matrix, so these are all -% biplots with different internal scaling of species and site scores -% with respect to each other. For \proglang{Canoco} with positive scaling -% values and \pkg{vegan} with negative scaling values, no constant -% $c$ can be found, but the correction is dependent on species standard -% deviations $s_j$, and these scores do not define a biplot. - -There is no natural way of scaling species and site scores to each -other. The eigenvalues in redundancy and principal components -analysis are scale-dependent and change when the data are multiplied -by a constant. If we have percent cover data, the eigenvalues are -typically very high, and the scores scaled by eigenvalues will have -much wider dispersion than the orthonormal set. If we express the -percentages as proportions, and divide the matrix by $100$, the -eigenvalues will be reduced by factor $100^2$, and the scores scaled -by eigenvalues will have a narrower dispersion. For graphical biplots -we should be able to fix the relations of row and column scores to be -invariant against scaling of data. The solution in \proglang{R} -standard function \code{biplot} is to scale site and species scores -independently, and typically very differently -(Table~\ref{tab:scales}), but plot each independently to fill the -graph area. The solution in \proglang{Canoco} and \code{rda} is to -use proportional eigenvalues $\lambda_k / \sum \lambda_k$ instead of -original eigenvalues. These proportions are invariant with scale -changes, and typically they have a nice range for plotting two data -sets in the same graph. - -The \textbf{vegan} package uses a scaling constant $c = \sqrt[4]{(n-1) - \sum \lambda_k}$ in order to be able to use scaling by proportional -eigenvalues (like in \proglang{Canoco}) and still be able to have a -biplot scaling. Because of this, the scaling of \code{rda} scores is -non-standard. However, the \code{scores} function lets you to set -the scaling constant to any desired values. It is also possible to -have two separate scaling constants: the first for the species, and -the second for sites and friends, and this allows getting scores of -other software or \proglang{R} functions (Table \ref{tab:rdaconst}). - -\begin{table*}[t] - \centering - \caption{\label{tab:rdaconst} Values of the \code{const} argument in - \textbf{vegan} to get the scores that are equal to those from - other functions and software. Number of sites (rows) is $n$, - the number of species (columns) is $m$, and the sum of all - eigenvalues is $\sum_k \lambda_k$ (this is saved as the item - \code{tot.chi} in the \code{rda} result)}. - \begin{tabular}{lccc} - \\ - \toprule -& \textbf{Scaling} &\textbf{Species constant} & \textbf{Site constant} \\ -\midrule -\pkg{vegan} & any & $\sqrt[4]{(n-1) \sum \lambda_k}$ & $\sqrt[4]{(n-1) \sum \lambda_k}$\\ -\code{prcomp}, \code{princomp} & \code{1} & $1$ & $\sqrt{(n-1) \sum_k \lambda_k}$\\ -\proglang{Canoco\,v3} & \code{-1, -2, -3} & $\sqrt{n-1}$ & $\sqrt{n}$\\ -\proglang{Canoco\,v4} & \code{-1, -2, -3} & $\sqrt{m}$ & $\sqrt{n}$\\ -\bottomrule -\end{tabular} -\end{table*} - -The scaling is controlled by three arguments in the \code{scores} -function in \pkg{vegan}: -\begin{enumerate} - \item \code{scaling} with options \code{"sites"}, \code{"species"} - and \code{"symmetric"} defines the set of scores which is scaled - by eigenvalues (Table~\ref{tab:scales}). - \item \code{const} can be used to set the numeric scaling constant - to non-default values (Table~\ref{tab:rdaconst}). - \item \code{correlation} can be used to modify species scores so - that they show the relative change of species abundance, or their - correlation with the ordination (Table~\ref{tab:scales}). This is - no longer a biplot scaling. -\end{enumerate} - -\section{Weighted average and linear combination scores} - -Constrained ordination methods such as Constrained Correspondence -Analysis (CCA) and Redundancy Analysis (RDA) produce two kind of site -scores \cite{Braak86, Palmer93}: -\begin{itemize} -\item -LC or Linear Combination Scores which are linear combinations of -constraining variables. -\item -WA or Weighted Averages Scores which are such weighted averages of -species scores that are as similar to LC scores as possible. -\end{itemize} -Many computer programs for constrained ordinations give only or -primarily LC scores following recommendation of -\citet{Palmer93}. However, functions \code{cca} and \code{rda} in -the \pkg{vegan} package use primarily WA scores. This chapter -explains the reasons for this choice. - -Briefly, the main reasons are that -\begin{itemize} -\item LC scores \emph{are} linear combinations, so they give us only - the (scaled) environmental variables. This means that they are - independent of vegetation and cannot be found from the species - composition. Moreover, identical combinations of environmental - variables give identical LC scores irrespective of vegetation. -\item \citet{McCune97} has demonstrated that noisy environmental - variables result in deteriorated LC scores whereas WA scores - tolerate some errors in environmental variables. All environmental - measurements contain some errors, and therefore it is safer to use - WA scores. -\end{itemize} -This article studies mainly the first point. The users of -\pkg{vegan} have a choice of either LC or WA (default) scores, but -after reading this article, I believe that most of them do not want to -use LC scores, because they are not what they were looking for in -ordination. - -\subsection{LC Scores are Linear Combinations} - -Let us perform a simple CCA analysis using only two environmental -variables so that we can see the constrained solution completely in -two dimensions: -<<>>= -library(vegan) -data(varespec) -data(varechem) -orig <- cca(varespec ~ Al + K, varechem) -@ -Function \code{cca} in \pkg{vegan} uses WA scores as -default. So we must specifically ask for LC scores -(Fig. \ref{fig:ccalc}). -<>= -plot(orig, dis=c("lc","bp")) -@ -\begin{figure} -<>= -<> -@ -\caption{LC scores in CCA of the original data.} -\label{fig:ccalc} -\end{figure} - -What would happen to linear combinations of LC scores if we shuffle -the ordering of sites in species data? Function \code{sample()} below -shuffles the indices. -<<>>= -i <- sample(nrow(varespec)) -shuff <- cca(varespec[i,] ~ Al + K, varechem) -@ -\begin{figure} -<>= -plot(shuff, dis=c("lc","bp")) -@ -\caption{LC scores of shuffled species data.} -\label{fig:ccashuff} -\end{figure} -It seems that site scores are fairly similar, but oriented differently -(Fig. \ref{fig:ccashuff}). We can use Procrustes rotation to see how -similar the site scores indeed are (Fig. \ref{fig:ccaproc}). -<>= -plot(procrustes(scores(orig, dis="lc"), - scores(shuff, dis="lc"))) -@ -\begin{figure} -<>= -<> -@ -\caption{Procrustes rotation of LC scores from CCA of original and shuffled data.} -\label{fig:ccaproc} -\end{figure} -There is a small difference, but this will disappear if we use -Redundancy Analysis (RDA) instead of CCA -(Fig. \ref{fig:rdaproc}). Here we use a new shuffling as well. -<<>>= -tmp1 <- rda(varespec ~ Al + K, varechem) -i <- sample(nrow(varespec)) # Different shuffling -tmp2 <- rda(varespec[i,] ~ Al + K, varechem) -@ -\begin{figure} -<>= -plot(procrustes(scores(tmp1, dis="lc"), - scores(tmp2, dis="lc"))) -@ -\caption{Procrustes rotation of LC scores in RDA of the original and shuffled data.} -\label{fig:rdaproc} -\end{figure} - -LC scores indeed are linear combinations of constraints (environmental -variables) and \emph{independent of species data}: You can -shuffle your species data, or change the data completely, but the LC -scores will be unchanged in RDA. In CCA the LC scores are -\emph{weighted} linear combinations with site totals of species data -as weights. Shuffling species data in CCA changes the weights, and -this can cause changes in LC scores. The magnitude of changes depends -on the variability of site totals. - -The original data and shuffled data differ in their goodness of -fit: -<<>>= -orig -shuff -@ -Similarly their WA scores will be (probably) very different -(Fig. \ref{fig:ccawa}). -\begin{figure} -<>= -plot(procrustes(orig, shuff)) -@ -\caption{Procrustes rotation of WA scores of CCA with the original and - shuffled data.} -\label{fig:ccawa} -\end{figure} - -The example used only two environmental variables so that we can -easily plot all constrained axes. With a larger number of -environmental variables the full configuration remains similarly -unchanged, but its orientation may change, so that two-dimensional -projections look different. In the full space, the differences should -remain within numerical accuracy: -<<>>= -tmp1 <- rda(varespec ~ ., varechem) -tmp2 <- rda(varespec[i,] ~ ., varechem) -proc <- procrustes(scores(tmp1, dis="lc", choi=1:14), - scores(tmp2, dis="lc", choi=1:14)) -max(residuals(proc)) -@ -In \code{cca} the difference would be somewhat larger than now -observed \Sexpr{format.pval(max(residuals(proc)))} because site -weights used for environmental variables are shuffled with the species -data. - -\subsection{Factor constraints} - -It seems that users often get confused when they perform constrained -analysis using only one factor (class variable) as constraint. The -following example uses the classical dune meadow data \cite{Jongman87}: -<<>>= -data(dune) -data(dune.env) -orig <- cca(dune ~ Moisture, dune.env) -@ -When the results are plotted using LC scores, sample plots fall only -in four alternative positions (Fig. \ref{fig:factorlc}). -\begin{figure} -<>= -plot(orig, dis="lc") -@ -\caption{LC scores of the dune meadow data using only one factor as a - constraint.} -\label{fig:factorlc} -\end{figure} -In the previous chapter we saw that this happens because LC scores -\emph{are} the environmental variables, and they can be distinct only -if the environmental variables are distinct. However, normally the user -would like to see how well the environmental variables separate the -vegetation, or inversely, how we could use the vegetation to -discriminate the environmental conditions. For this purpose we should -plot WA scores, or LC scores and WA scores together: The LC scores -show where the site \emph{should} be, the WA scores shows where the -site \emph{is}. - -Function \code{ordispider} adds line segments to connect each WA -score with the corresponding LC (Fig. \ref{fig:walcspider}). -<>= -plot(orig, display="wa", type="points") -ordispider(orig, col="red") -text(orig, dis="cn", col="blue") -@ -\begin{figure} -<>= -<> -@ -\caption{A ``spider plot'' connecting WA scores to corresponding LC - scores. The shorter the web segments, the better the ordination.} -\label{fig:walcspider} -\end{figure} -This is the standard way of displaying results of discriminant -analysis, too. Moisture classes \code{1} and \code{2} seem to be -overlapping, and cannot be completely separated by their -vegetation. Other classes are more distinct, but there seems to be a -clear arc effect or a ``horseshoe'' despite using CCA. - -\subsection{Conclusion} - -LC scores are only the (weighted and scaled) constraints and -independent of vegetation. If you plot them, you plot only your -environmental variables. WA scores are based on vegetation data but -are constrained to be as similar to the LC scores as only -possible. Therefore \pkg{vegan} calls LC scores as -\code{constraints} and WA scores as \code{site scores}, and uses -primarily WA scores in plotting. However, the user makes the ultimate -choice, since both scores are available. - -\bibliography{vegan} - -\end{document} diff --git a/scTCRpy/rpackages/vegan/doc/diversity-vegan.R b/scTCRpy/rpackages/vegan/doc/diversity-vegan.R deleted file mode 100644 index 1654d16d6..000000000 --- a/scTCRpy/rpackages/vegan/doc/diversity-vegan.R +++ /dev/null @@ -1,248 +0,0 @@ -### R code from vignette source 'diversity-vegan.Rnw' - -################################################### -### code chunk number 1: diversity-vegan.Rnw:21-26 -################################################### -par(mfrow=c(1,1)) -options(width=55) -figset <- function() par(mar=c(4,4,1,1)+.1) -options(SweaveHooks = list(fig = figset)) -options("prompt" = "> ", "continue" = " ") - - -################################################### -### code chunk number 2: diversity-vegan.Rnw:58-60 -################################################### -library(vegan) -data(BCI) - - -################################################### -### code chunk number 3: diversity-vegan.Rnw:78-79 -################################################### -H <- diversity(BCI) - - -################################################### -### code chunk number 4: diversity-vegan.Rnw:86-87 -################################################### -J <- H/log(specnumber(BCI)) - - -################################################### -### code chunk number 5: diversity-vegan.Rnw:113-115 -################################################### -k <- sample(nrow(BCI), 6) -R <- renyi(BCI[k,]) - - -################################################### -### code chunk number 6: diversity-vegan.Rnw:122-123 -################################################### -getOption("SweaveHooks")[["fig"]]() -print(plot(R)) - - -################################################### -### code chunk number 7: diversity-vegan.Rnw:134-135 -################################################### -alpha <- fisher.alpha(BCI) - - -################################################### -### code chunk number 8: diversity-vegan.Rnw:171-172 -################################################### -quantile(rowSums(BCI)) - - -################################################### -### code chunk number 9: diversity-vegan.Rnw:175-176 -################################################### -Srar <- rarefy(BCI, min(rowSums(BCI))) - - -################################################### -### code chunk number 10: diversity-vegan.Rnw:184-185 -################################################### -S2 <- rarefy(BCI, 2) - - -################################################### -### code chunk number 11: diversity-vegan.Rnw:189-190 -################################################### -all(rank(Srar) == rank(S2)) - - -################################################### -### code chunk number 12: diversity-vegan.Rnw:196-197 -################################################### -range(diversity(BCI, "simp") - (S2 -1)) - - -################################################### -### code chunk number 13: diversity-vegan.Rnw:260-264 -################################################### -data(dune) -data(dune.taxon) -taxdis <- taxa2dist(dune.taxon, varstep=TRUE) -mod <- taxondive(dune, taxdis) - - -################################################### -### code chunk number 14: diversity-vegan.Rnw:267-268 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(mod) - - -################################################### -### code chunk number 15: diversity-vegan.Rnw:294-296 -################################################### -tr <- hclust(taxdis, "aver") -mod <- treedive(dune, tr) - - -################################################### -### code chunk number 16: diversity-vegan.Rnw:318-321 -################################################### -k <- sample(nrow(BCI), 1) -fish <- fisherfit(BCI[k,]) -fish - - -################################################### -### code chunk number 17: diversity-vegan.Rnw:324-325 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(fish) - - -################################################### -### code chunk number 18: diversity-vegan.Rnw:353-354 -################################################### -prestondistr(BCI[k,]) - - -################################################### -### code chunk number 19: diversity-vegan.Rnw:385-387 -################################################### -rad <- radfit(BCI[k,]) -rad - - -################################################### -### code chunk number 20: diversity-vegan.Rnw:390-391 -################################################### -getOption("SweaveHooks")[["fig"]]() -print(radlattice(rad)) - - -################################################### -### code chunk number 21: a -################################################### -sac <- specaccum(BCI) -plot(sac, ci.type="polygon", ci.col="yellow") - - -################################################### -### code chunk number 22: diversity-vegan.Rnw:461-462 -################################################### -getOption("SweaveHooks")[["fig"]]() -sac <- specaccum(BCI) -plot(sac, ci.type="polygon", ci.col="yellow") - - -################################################### -### code chunk number 23: diversity-vegan.Rnw:490-491 -################################################### -ncol(BCI)/mean(specnumber(BCI)) - 1 - - -################################################### -### code chunk number 24: diversity-vegan.Rnw:508-510 -################################################### -beta <- vegdist(BCI, binary=TRUE) -mean(beta) - - -################################################### -### code chunk number 25: diversity-vegan.Rnw:517-518 -################################################### -betadiver(help=TRUE) - - -################################################### -### code chunk number 26: diversity-vegan.Rnw:536-538 -################################################### -z <- betadiver(BCI, "z") -quantile(z) - - -################################################### -### code chunk number 27: diversity-vegan.Rnw:548-553 -################################################### -data(dune) -data(dune.env) -z <- betadiver(dune, "z") -mod <- with(dune.env, betadisper(z, Management)) -mod - - -################################################### -### code chunk number 28: diversity-vegan.Rnw:556-557 -################################################### -getOption("SweaveHooks")[["fig"]]() -boxplot(mod) - - -################################################### -### code chunk number 29: diversity-vegan.Rnw:668-669 -################################################### -specpool(BCI) - - -################################################### -### code chunk number 30: diversity-vegan.Rnw:674-676 -################################################### -s <- sample(nrow(BCI), 25) -specpool(BCI[s,]) - - -################################################### -### code chunk number 31: diversity-vegan.Rnw:687-688 -################################################### -estimateR(BCI[k,]) - - -################################################### -### code chunk number 32: diversity-vegan.Rnw:757-759 -################################################### -veiledspec(prestondistr(BCI[k,])) -veiledspec(BCI[k,]) - - -################################################### -### code chunk number 33: diversity-vegan.Rnw:773-774 -################################################### -smo <- beals(BCI) - - -################################################### -### code chunk number 34: a -################################################### -j <- which(colnames(BCI) == "Ceiba.pentandra") -plot(beals(BCI, species=j, include=FALSE), BCI[,j], - ylab="Occurrence", main="Ceiba pentandra", - xlab="Probability of occurrence") - - -################################################### -### code chunk number 35: diversity-vegan.Rnw:787-788 -################################################### -getOption("SweaveHooks")[["fig"]]() -j <- which(colnames(BCI) == "Ceiba.pentandra") -plot(beals(BCI, species=j, include=FALSE), BCI[,j], - ylab="Occurrence", main="Ceiba pentandra", - xlab="Probability of occurrence") - - diff --git a/scTCRpy/rpackages/vegan/doc/diversity-vegan.Rnw b/scTCRpy/rpackages/vegan/doc/diversity-vegan.Rnw deleted file mode 100644 index d2d1d21d5..000000000 --- a/scTCRpy/rpackages/vegan/doc/diversity-vegan.Rnw +++ /dev/null @@ -1,796 +0,0 @@ -% -*- mode: noweb; noweb-default-code-mode: R-mode; -*- -%\VignetteIndexEntry{Diversity analysis in vegan} -\documentclass[a4paper,10pt,twocolumn]{article} -\usepackage{vegan} %% vegan setup - -%% TODO: SSarrhenius, adipart, beals update, betadisper -%% expansion (+ permutest), contribdiv, eventstar, multipart, refer to -%% FD, check Kindt reference to specaccum, check estimateR ref - -\title{Vegan: ecological diversity} \author{Jari Oksanen} - -\date{\footnotesize{ - processed with vegan \Sexpr{packageDescription("vegan", field="Version")} - in \Sexpr{R.version.string} on \today}} - -%% need no \usepackage{Sweave} -\begin{document} -\bibliographystyle{jss} - -\SweaveOpts{strip.white=true} -<>= -par(mfrow=c(1,1)) -options(width=55) -figset <- function() par(mar=c(4,4,1,1)+.1) -options(SweaveHooks = list(fig = figset)) -options("prompt" = "> ", "continue" = " ") -@ - -\maketitle -\begin{abstract} - This document explains diversity related methods in - \pkg{vegan}. The methods are briefly described, and the equations - used them are given often in more detail than in their help - pages. The methods discussed include common diversity indices and - rarefaction, families of diversity indices, species abundance - models, species accumulation models and beta diversity, extrapolated - richness and probability of being a member of the species pool. The - document is still incomplete and does not cover all diversity - methods in \pkg{vegan}. -\end{abstract} -\tableofcontents - - -\noindent The \pkg{vegan} package has two major components: -multivariate analysis (mainly ordination), and methods for diversity -analysis of ecological communities. This document gives an -introduction to the latter. Ordination methods are covered in other -documents. Many of the diversity functions were written by Roeland -Kindt, Bob O'Hara and P{\'e}ter S{\'o}lymos. - -Most diversity methods assume that data are counts of individuals. -The methods are used with other data types, and some people argue that -biomass or cover are more adequate than counts of individuals of -variable sizes. However, this document mainly uses a data set with -counts: stem counts of trees on $1$\,ha plots in the Barro Colorado -Island. The following steps make these data available for the -document: -<<>>= -library(vegan) -data(BCI) -@ - -\section{Diversity indices} - -Function \code{diversity} finds the most commonly used diversity -indices \citep{Hill73number}: -\begin{align} -H &= - \sum_{i=1}^S p_i \log_b p_i & \text{Shannon--Weaver}\\ -D_1 &= 1 - \sum_{i=1}^S p_i^2 &\text{Simpson}\\ -D_2 &= \frac{1}{\sum_{i=1}^S p_i^2} &\text{inverse Simpson}\,, -\end{align} -where $p_i$ is the proportion of species $i$, and $S$ is the number of -species so that $\sum_{i=1}^S p_i = 1$, and $b$ is the base of the -logarithm. It is most common to use natural logarithms (and then we -mark index as $H'$), but $b=2$ has -theoretical justification. The default is to use natural logarithms. -Shannon index is calculated with: -<<>>= -H <- diversity(BCI) -@ -which finds diversity indices for all sites. - -\pkg{Vegan} does not have indices for evenness (equitability), but -the most common of these, Pielou's evenness $J = H'/\log(S)$ is easily -found as: -<<>>= -J <- H/log(specnumber(BCI)) -@ -where \code{specnumber} is a simple \pkg{vegan} function to find -the numbers of species. - -\pkg{vegan} also can estimate series of R\'{e}nyi and Tsallis -diversities. R{\'e}nyi diversity of order $a$ is \citep{Hill73number}: -\begin{equation} -H_a = \frac{1}{1-a} \log \sum_{i=1}^S p_i^a \,, -\end{equation} -and the corresponding Hill number is $N_a = \exp(H_a)$. Many common -diversity indices are special cases of Hill numbers: $N_0 = S$, $N_1 = -\exp(H')$, $N_2 = D_2$, and $N_\infty = 1/(\max p_i)$. The -corresponding R\'{e}nyi diversities are $H_0 = \log(S)$, $H_1 = H'$, $H_2 = -- \log(\sum p_i^2)$, and $H_\infty = - \log(\max p_i)$. -Tsallis diversity of order $q$ is \citep{Tothmeresz95}: -\begin{equation} - H_q = \frac{1}{q-1} \left(1 - \sum_{i=1}^S p^q \right) \, . -\end{equation} -These correspond to common diversity indices: $H_0 = S-1$, $H_1 = H'$, -and $H_2 = D_1$, and can be converted to Hill numbers: -\begin{equation} - N_q = (1 - (q-1) H_q )^\frac{1}{1-q} \, . -\end{equation} - -We select a random subset of five sites for R\'{e}nyi diversities: -<<>>= -k <- sample(nrow(BCI), 6) -R <- renyi(BCI[k,]) -@ -We can really regard a site more diverse if all of its R\'{e}nyi -diversities are higher than in another site. We can inspect this -graphically using the standard \code{plot} function for the -\code{renyi} result (Fig.~\ref{fig:renyi}). -\begin{figure} -<>= -print(plot(R)) -@ -\caption{R\'{e}nyi diversities in six randomly selected plots. The plot - uses Trellis graphics with a separate panel for each site. The dots - show the values for sites, and the lines the extremes and median in - the data set.} -\label{fig:renyi} -\end{figure} - -Finally, the $\alpha$ parameter of Fisher's log-series can be used as -a diversity index \citep{FisherEtal43}: -<<>>= -alpha <- fisher.alpha(BCI) -@ - -\section{Rarefaction} - -Species richness increases with sample size, and differences in -richness actually may be caused by differences in sample size. To -solve this problem, we may try to rarefy species richness to the same -number of individuals. Expected number of species in a community -rarefied from $N$ to $n$ individuals is \citep{Hurlbert71}: -\begin{equation} -\label{eq:rare} -\hat S_n = \sum_{i=1}^S (1 - q_i)\,, \quad\text{where } q_i = -\frac{{N-x_i \choose n}}{{N \choose n}} \,. -\end{equation} -Here $x_i$ is the count of species $i$, and ${N \choose n}$ is the -binomial coefficient, or the number of ways we can choose $n$ from -$N$, and $q_i$ give the probabilities that species $i$ does \emph{not} occur in a -sample of size $n$. This is positive only when $N-x_i \ge n$, but for -other cases $q_i = 0$ or the species is sure to occur in the sample. -The variance of rarefied richness is \citep{HeckEtal75}: -\begin{multline} -\label{eq:rarevar} -s^2 = q_i (1-q_i) \\ + 2 \sum_{i=1}^S \sum_{j>i} \left[ \frac{{N- x_i - x_j - \choose n}}{ {N - \choose n}} - q_i q_j\right] \,. -\end{multline} -Equation~\ref{eq:rarevar} actually is of the same form as the variance -of sum of correlated variables: -\begin{equation} -\VAR \left(\sum x_i \right) = \sum \VAR (x_i) + 2 \sum_{i=1}^S -\sum_{j>i} \COV (x_i, x_j) \,. -\end{equation} - -The number of stems per hectare varies in our -data set: -<<>>= -quantile(rowSums(BCI)) -@ -To express richness for the same number of individuals, we can use: -<<>>= -Srar <- rarefy(BCI, min(rowSums(BCI))) -@ -Rarefaction curves often are seen as an objective solution for -comparing species richness with different sample sizes. However, rank -orders typically differ among different rarefaction sample sizes, -rarefaction curves can cross. - -As an extreme case we may rarefy sample size to two individuals: -<<>>= -S2 <- rarefy(BCI, 2) -@ -This will not give equal rank order with the previous rarefaction -richness: -<<>>= -all(rank(Srar) == rank(S2)) -@ -Moreover, the rarefied richness for two individuals is a finite -sample variant of Simpson's diversity index \citep{Hurlbert71}\,--\,or -more precisely of $D_1 + 1$, and these two are almost identical in -BCI: -<<>>= -range(diversity(BCI, "simp") - (S2 -1)) -@ -Rarefaction is sometimes presented as an ecologically meaningful -alternative to dubious diversity indices \citep{Hurlbert71}, but the -differences really seem to be small. - -\section{Taxonomic and functional diversity} - -Simple diversity indices only consider species identity: all different -species are equally different. In contrast, taxonomic and functional -diversity indices judge the differences of species. Taxonomic and -functional diversities are used in different fields of science, but -they really have very similar reasoning, and either could be used -either with taxonomic or functional traits of species. - -\subsection{Taxonomic diversity: average distance of traits} - -The two basic indices are called taxonomic diversity $\Delta$ and -taxonomic distinctness $\Delta^*$ \citep{ClarkeWarwick98}: -\begin{align} - \Delta &= \frac{\sum \sum_{i>= -data(dune) -data(dune.taxon) -taxdis <- taxa2dist(dune.taxon, varstep=TRUE) -mod <- taxondive(dune, taxdis) -@ -\begin{figure} -<>= -plot(mod) -@ -\caption{Taxonomic diversity $\Delta^+$ for the dune meadow data. The - points are diversity values of single sites, and the funnel is their - approximate confidence intervals ($2 \times$ standard error).} -\label{fig:taxondive} -\end{figure} - -\subsection{Functional diversity: the height of trait tree} - -In taxonomic diversity the primary data were taxonomic trees which -were transformed to pairwise distances among species. In functional -diversity the primary data are species traits which are translated to -pairwise distances among species and then to clustering trees of -species traits. The argument for using trees is that in this way a -single deviant species will have a small influence, since its -difference is evaluated only once instead of evaluating its distance -to all other species \citep{PetcheyGaston06}. - -Function \code{treedive} implements functional diversity defined as -the total branch length in a trait dendrogram connecting all species, -but excluding the unnecessary root segments of the tree -\citep{PetcheyGaston02, PetcheyGaston06}. The example uses the -taxonomic distances of the previous chapter. These are first converted -to a hierarchic clustering (which actually were their original form -before \code{taxa2dist} converted them into distances) -<<>>= -tr <- hclust(taxdis, "aver") -mod <- treedive(dune, tr) -@ - -\section{Species abundance models} - -Diversity indices may be regarded as variance measures of species -abundance distribution. We may wish to inspect abundance -distributions more directly. \pkg{Vegan} has functions for -Fisher's log-series and Preston's log-normal models, and in addition -several models for species abundance distribution. - -\subsection{Fisher and Preston} - -In Fisher's log-series, the expected number of species $\hat f$ with $n$ -individuals is \citep{FisherEtal43}: -\begin{equation} -\hat f_n = \frac{\alpha x^n}{n} \,, -\end{equation} -where $\alpha$ is the diversity parameter, and $x$ is a nuisance -parameter defined by $\alpha$ and total number -of individuals $N$ in the site, $x = N/(N-\alpha)$. Fisher's -log-series for a randomly selected plot is (Fig.~\ref{fig:fisher}): -<<>>= -k <- sample(nrow(BCI), 1) -fish <- fisherfit(BCI[k,]) -fish -@ -\begin{figure} -<>= -plot(fish) -@ -\caption{Fisher's log-series fitted to one randomly selected site - (\Sexpr{k}).} -\label{fig:fisher} -\end{figure} -We already saw $\alpha$ as a diversity index. - -Preston's log-normal model is the main challenger to Fisher's -log-series \citep{Preston48}. Instead of plotting species by -frequencies, it bins species into frequency classes of increasing -sizes. As a result, upper bins with high range of frequencies become -more common, and sometimes the result looks similar to Gaussian -distribution truncated at the left. - -There are two alternative functions for the log-normal model: -\code{prestonfit} and \code{prestondistr}. Function \code{prestonfit} -uses traditionally binning approach, and is burdened with arbitrary -choices of binning limits and treatment of ties. It seems that Preston -split ties between adjacent octaves: only half of the species observed -once were in the first octave, and half were transferred to the next -octave, and the same for all species at the octave limits occurring 2, -4, 8, 16\ldots times \citep{WilliamsonGaston05}. Function -\code{prestonfit} can either split the ties or keep all limit cases in -the lower octave. Function \code{prestondistr} directly maximizes -truncated log-normal likelihood without binning data, and it is the -recommended alternative. Log-normal models usually fit poorly to the -BCI data, but here our random plot (number \Sexpr{k}): -<<>>= -prestondistr(BCI[k,]) -@ - -\subsection{Ranked abundance distribution} - -An alternative approach to species abundance distribution is to plot -logarithmic abundances in decreasing order, or against ranks of -species \citep{Whittaker65}. These are known as ranked abundance -distribution curves, species abundance curves, dominance--diversity -curves or Whittaker plots. Function \code{radfit} fits some of the -most popular models \citep{Bastow91} using maximum likelihood -estimation: -\begin{align} -\hat a_r &= \frac{N}{S} \sum_{k=r}^S \frac{1}{k} &\text{brokenstick}\\ -\hat a_r &= N \alpha (1-\alpha)^{r-1} & \text{preemption} \\ -\hat a_r &= \exp \left[\log (\mu) + \log (\sigma) \Phi \right] -&\text{log-normal}\\ -\hat a_r &= N \hat p_1 r^\gamma &\text{Zipf}\\ -\hat a_r &= N c (r + \beta)^\gamma &\text{Zipf--Mandelbrot} -\end{align} -In all these, $\hat a_r$ is the expected abundance of species at rank $r$, $S$ -is the number of species, $N$ is the number of individuals, $\Phi$ is -a standard normal function, $\hat p_1$ is the estimated proportion of -the most abundant species, and $\alpha$, $\mu$, $\sigma$, $\gamma$, -$\beta$ and $c$ are the estimated parameters in each model. - -It is customary to define the models for proportions $p_r$ instead of -abundances $a_r$, but there is no reason for this, and \code{radfit} -is able to work with the original abundance data. We have count data, -and the default Poisson error looks appropriate, and our example data -set gives (Fig.~\ref{fig:rad}): -<<>>= -rad <- radfit(BCI[k,]) -rad -@ -\begin{figure} -<>= -print(radlattice(rad)) -@ -\caption{Ranked abundance distribution models for a random plot - (no. \Sexpr{k}). The best model has the lowest \textsc{aic}.} -\label{fig:rad} -\end{figure} - -Function \code{radfit} compares the models using alternatively -Akaike's or Schwartz's Bayesian information criteria. These are based -on log-likelihood, but penalized by the number of estimated -parameters. The penalty per parameter is $2$ in \textsc{aic}, and -$\log S$ in \textsc{bic}. Brokenstick is regarded as a null model and -has no estimated parameters in \pkg{vegan}. Preemption model has -one estimated parameter ($\alpha$), log-normal and Zipf models two -($\mu, \sigma$, or $\hat p_1, \gamma$, resp.), and Zipf--Mandelbrot -model has three ($c, \beta, \gamma$). - -Function \code{radfit} also works with data frames, and fits models -for each site. It is curious that log-normal model rarely is the -choice, although it generally is regarded as the canonical model, in -particular in data sets like Barro Colorado tropical forests. - -\section{Species accumulation and beta diversity} - -Species accumulation models and species pool models study collections -of sites, and their species richness, or try to estimate the number of -unseen species. - -\subsection{Species accumulation models} - -Species accumulation models are similar to rarefaction: they study the -accumulation of species when the number of sites increases. There are -several alternative methods, including accumulating sites in the order -they happen to be, and repeated accumulation in random order. In -addition, there are three analytic models. Rarefaction pools -individuals together, and applies rarefaction equation (\ref{eq:rare}) -to these individuals. Kindt's exact accumulator resembles rarefaction -\citep{UglandEtal03}: -\begin{multline} -\label{eq:kindt} -\hat S_n = \sum_{i=1}^S (1 - p_i), \,\quad \text{where } -p_i = \frac{{N- f_i \choose n}}{{N \choose n}} \,, -\end{multline} -and $f_i$ is the frequency of species $i$. Approximate variance -estimator is: -\begin{multline} -\label{eq:kindtvar} -s^2 = p_i (1 - p_i) \\ + 2 \sum_{i=1}^S \sum_{j>i} \left( r_{ij} - \sqrt{p_i(1-p_i)} \sqrt{p_j (1-p_j)}\right) \,, -\end{multline} -where $r_{ij}$ is the correlation coefficient between species $i$ and -$j$. Both of these are unpublished: eq.~\ref{eq:kindt} was developed -by Roeland Kindt, and eq.~\ref{eq:kindtvar} by Jari Oksanen. The third -analytic method was suggested by \citet{Coleman82}: -\begin{equation} -\label{eq:cole} -S_n = \sum_{i=1}^S (1 - p_i), \quad \text{where } p_i = \left(1 - - \frac{1}{n}\right)^{f_i} \,, -\end{equation} -and the suggested variance is $s^2 = p_i (1-p_i)$ which ignores the -covariance component. In addition, eq.~\ref{eq:cole} does not -properly handle sampling without replacement and underestimates the -species accumulation curve. - -The recommended is Kindt's exact method (Fig.~\ref{fig:sac}): -<>= -sac <- specaccum(BCI) -plot(sac, ci.type="polygon", ci.col="yellow") -@ -\begin{figure} -<>= -<> -@ -\caption{Species accumulation curve for the BCI data; exact method.} -\label{fig:sac} -\end{figure} - -\subsection{Beta diversity} - -\citet{Whittaker60} divided diversity into various components. The -best known are diversity in one spot that he called alpha diversity, -and the diversity along gradients that he called beta diversity. The -basic diversity indices are indices of alpha diversity. Beta diversity -should be studied with respect to gradients \citep{Whittaker60}, but -almost everybody understand that as a measure of general heterogeneity -\citep{Tuomisto10a, Tuomisto10b}: how many more species do you have in -a collection of sites compared to an average site. - -The best known index of beta diversity is based on the ratio of total -number of species in a collection of sites $S$ and the average -richness per one site $\bar \alpha$ \citep{Tuomisto10a}: -\begin{equation} - \label{eq:beta} - \beta = S/\bar \alpha - 1 \,. -\end{equation} -Subtraction of one means that $\beta = 0$ when there are no excess -species or no heterogeneity between sites. For this index, no specific -functions are needed, but this index can be easily found with the help -of \pkg{vegan} function \code{specnumber}: -<<>>= -ncol(BCI)/mean(specnumber(BCI)) - 1 -@ - -The index of eq.~\ref{eq:beta} is problematic because $S$ increases -with the number of sites even when sites are all subsets of the same -community. \citet{Whittaker60} noticed this, and suggested the index -to be found from pairwise comparison of sites. If the number of shared -species in two sites is $a$, and the numbers of species unique to each -site are $b$ and $c$, then $\bar \alpha = (2a + b + c)/2$ and $S = -a+b+c$, and index~\ref{eq:beta} can be expressed as: -\begin{equation} - \label{eq:betabray} - \beta = \frac{a+b+c}{(2a+b+c)/2} - 1 = \frac{b+c}{2a+b+c} \,. -\end{equation} -This is the S{\o}rensen index of dissimilarity, and it can be found -for all sites using \pkg{vegan} function \code{vegdist} with -binary data: -<<>>= -beta <- vegdist(BCI, binary=TRUE) -mean(beta) -@ - -There are many other definitions of beta diversity in addition to -eq.~\ref{eq:beta}. All commonly used indices can be found using -\code{betadiver} \citep{KoleffEtal03}. The indices in \code{betadiver} -can be referred to by subscript name, or index number: -<<>>= -betadiver(help=TRUE) -@ -Some of these indices are duplicates, and many of them are well known -dissimilarity indices. -One of the more interesting indices is based -on the Arrhenius species--area model -\begin{equation} - \label{eq:arrhenius} - \hat S = c X^z\,, -\end{equation} -where $X$ is the area (size) of the patch or site, and $c$ and $z$ are -parameters. Parameter $c$ is uninteresting, but $z$ gives the -steepness of the species area curve and is a measure of beta -diversity. In islands typically $z \approx 0.3$. This kind of -islands can be regarded as subsets of the same community, indicating -that we really should talk about gradient differences if $z \gtrapprox 0.3$. We -can find the value of $z$ for a pair of plots using function -\code{betadiver}: -<<>>= -z <- betadiver(BCI, "z") -quantile(z) -@ -The size $X$ and parameter $c$ cancel out, and the index gives the -estimate $z$ for any pair of sites. - -Function \code{betadisper} can be used to analyse beta diversities -with respect to classes or factors \citep{Anderson06, AndersonEtal06}. -There is no such classification available for the Barro Colorado -Island data, and the example studies beta diversities in the -management classes of the dune meadows (Fig.~\ref{fig:betadisper}): -<<>>= -data(dune) -data(dune.env) -z <- betadiver(dune, "z") -mod <- with(dune.env, betadisper(z, Management)) -mod -@ -\begin{figure} -<>= -boxplot(mod) -@ -\caption{Box plots of beta diversity measured as the average steepness - ($z$) of the species area curve in the Arrhenius model $S = cX^z$ in - Management classes of dune meadows.} -\label{fig:betadisper} -\end{figure} - -\section{Species pool} -\subsection{Number of unseen species} - -Species accumulation models indicate that not all species were seen in -any site. These unseen species also belong to the species pool. -Functions \code{specpool} and \code{estimateR} implement some -methods of estimating the number of unseen species. Function -\code{specpool} studies a collection of sites, and -\code{estimateR} works with counts of individuals, and can be used -with a single site. Both functions assume that the number of unseen -species is related to the number of rare species, or species seen only -once or twice. - -The incidence-based functions group species by their number of -occurrences $f_i = f_0, f_1, \ldots, f_N$, where $f$ is the number of -species occurring in exactly $i$ sites in the data: $f_N$ is the number -of species occurring on every $N$ site, $f_1$ the number of species -occurring once, and $f_0$ the number of species in the species pool -but not found in the sample. The total number of species in the pool -$S_p$ is -\begin{equation} -S_p = \sum_{i=0}^N f_i = f_0+ S_o \,, -\end{equation} -where $S_o = \sum_{i>0} f_i$ is the observed number of species. The -sampling proportion $i/N$ is an estimate for the commonness of the -species in the community. When species is present in the community but -not in the sample, $i=0$ is an obvious under-estimate, and -consequently, for values $i>0$ the species commonness is -over-estimated \citep{Good53}. The models for the pool size estimate -the number of species missing in the sample $f_0$. - -Function \code{specpool} implements the following models to estimate -the number of missing species $f_0$. Chao estimator is \citep{Chao87, ChiuEtal14}: -\begin{equation} -\label{eq:chao} -\hat f_0 = \begin{cases} - \frac{f_1^2}{2 f_2} \frac{N-1}{N} &\text{if } f_2 > 0 \\ -\frac{f_1 (f_1 -1)}{2} \frac{N-1}{N} & \text{if } f_2 = 0 \,. -\end{cases} -\end{equation} -The latter case for $f_2=0$ is known as the bias-corrected -form. \citet{ChiuEtal14} introduced the small-sample correction term -$\frac{N}{N-1}$, but it was not originally used \citep{Chao87}. - -The first and second order jackknife estimators are -\citep{SmithVanBelle84}: -\begin{align} -\hat f_0 &= f_1 \frac{N-1}{N} \\ -\hat f_0 & = f_1 \frac{2N-3}{N} + f_2 \frac{(N-2)^2}{N(N-1)} \,. -\end{align} -The bootstrap estimator is \citep{SmithVanBelle84}: -\begin{equation} -\hat f_0 = \sum_{i=1}^{S_o} (1-p_i)^N \,. -\end{equation} -The idea in jackknife seems to be that we missed about as many species -as we saw only once, and the idea in bootstrap that if we repeat -sampling (with replacement) from the same data, we miss as many -species as we missed originally. - -The variance estimaters only concern the estimated number of missing -species $\hat f_0$, although they are often expressed as they would -apply to the pool size $S_p$; this is only true if we assume that -$\VAR(S_o) = 0$. The variance of the Chao estimate is \citep{ChiuEtal14}: -\begin{multline} -\label{eq:var-chao-basic} -\VAR(\hat f_0) = f_1 \left(A^2 \frac{G^3}{4} + A^2 G^2 + A \frac{G}{2} \right),\\ -\text{where } A = \frac{N-1}{N}\;\text{and } G = \frac{f_1}{f_2} \,. -\end{multline} -%% The variance of bias-corrected Chao estimate can be approximated by -%% replacing the terms of eq.~\ref{eq:var-chao-basic} with the -%% corresponding terms of the bias-correcter form of in eq.~\ref{eq:chao}: -%% \begin{multline} -%% \label{eq:var-chao-bc} -%% s^2 = A \frac{f_1(f_1-1)}{2} + A^2 \frac{f_1(2 f_1+1)^2}{(f_2+1)^2}\\ -%% + A^2 \frac{f_1^2 f_2 (f_1 -1)^2}{4 (f_2 + 1)^4} -%% \end{multline} -For the bias-corrected form of eq.~\ref{eq:chao} (case $f_2 = 0$), the variance is -\citep[who omit small-sample correction in some terms]{ChiuEtal14}: -\begin{multline} -\label{eq:var-chao-bc0} -\VAR(\hat f_0) = \tfrac{1}{4} A^2 f_1 (2f_1 -1)^2 + \tfrac{1}{2} A f_1 -(f_1-1) \\- \tfrac{1}{4}A^2 \frac{f_1^4}{S_p} \,. -\end{multline} - -The variance of the first-order jackknife is based on the number of -``singletons'' $r$ (species occurring only once in the data) in sample -plots \citep{SmithVanBelle84}: -\begin{equation} -\VAR(\hat f_0) = \left(\sum_{i=1}^N r_i^2 - \frac{f_1}{N}\right) -\frac{N-1}{N} \,. -\end{equation} -Variance of the second-order jackknife is not evaluated in -\code{specpool} (but contributions are welcome). - -The variance of bootstrap estimator is\citep{SmithVanBelle84}: -\begin{multline} -\VAR(\hat f_0) = \sum_{i=1}^{S_o} q_i (1-q_i) \\ +2 \sum_{i \neq - j}^{S_o} \left[(Z_{ij}/N)^N - q_i q_j \right] \\ -\text{where } q_i = (1-p_i)^N \, , -\end{multline} -and $Z_{ij}$ is the number of sites where both species are absent. - -The extrapolated richness values for the whole BCI data are: -<<>>= -specpool(BCI) -@ -If the estimation of pool size really works, we should get the same -values of estimated richness if we take a random subset of a half of -the plots (but this is rarely true): -<<>>= -s <- sample(nrow(BCI), 25) -specpool(BCI[s,]) -@ - -\subsection{Pool size from a single site} - -The \code{specpool} function needs a collection of sites, but there -are some methods that estimate the number of unseen species for each -single site. These functions need counts of individuals, and species -seen only once or twice, or other rare species, take the place of -species with low frequencies. Function \code{estimateR} implements -two of these methods: -<<>>= -estimateR(BCI[k,]) -@ -In abundance based models $a_i$ denotes the number of species with $i$ -individuals, and takes the place of $f_i$ of previous models. -Chao's method is similar as the bias-corrected model -eq.~\ref{eq:chao} \citep{Chao87, ChiuEtal14}: -\begin{equation} - \label{eq:chao-bc} - S_p = S_o + \frac{a_1 (a_1 - 1)}{2 (a_2 + 1)}\,. -\end{equation} -When $f_2=0$, eq.~\ref{eq:chao-bc} reduces to the bias-corrected form -of eq.~\ref{eq:chao}, but quantitative estimators are based on -abundances and do not use small-sample correction. This is not usually -needed because sample sizes are total numbers of individuals, and -these are usually high, unlike in frequency based models, where the -sample size is the number of sites \citep{ChiuEtal14}. - -A commonly used approximate variance estimator of eq.~\ref{eq:chao-bc} is: -\begin{multline} - \label{eq:var-chao-bc} - s^2 = \frac{a_1(a_1-1)}{2} + \frac{a_1(2 a_1+1)^2}{(a_2+1)^2}\\ - + \frac{a_1^2 a_2 (a_1 -1)^2}{4 (a_2 + 1)^4} \,. -\end{multline} -However, \pkg{vegan} does not use this, but instead the following more -exact form which was directly derived from eq.~\ref{eq:chao-bc} -following \citet[web appendix]{ChiuEtal14}: -\begin{multline} - s^2 = \frac{1}{4} \frac{1}{(a_2+1)^4 S_p} [a_1 (S_p a_1^3 - a_2 + 4 S_p a_1^2 a_2^2 \\+ 2 S_p a_1 a_2^3 + 6 S_p a_1^2 a_2 + 2 S_p - a_1 a_2^2 -2 S_p a_2^3 \\+ 4 S_p a_1^2 + S_p a_1 a_2 -5 S_p a_2^2 - a_1^3 - 2 - a_1^2 a_2\\ - a_1 a_2^2 - 2 S_p a_1 - 4 S_p a_2 - S_p ) ]\,. -\end{multline} -The variance estimators only concern the number of unseen species like previously. - -The \textsc{ace} is estimator is defined as \citep{OHara05}: -\begin{equation} -\begin{split} -S_p &= S_\mathrm{abund} + \frac{S_\mathrm{rare}}{C_\mathrm{ACE}} + -\frac{a_1}{C_\mathrm{ACE}} \gamma^2\, , \quad \text{where}\\ -C_\mathrm{ACE} &= 1 - \frac{a_1}{N_\mathrm{rare}}\\ -\gamma^2 &= \frac{S_\mathrm{rare}}{C_\mathrm{ACE}} \sum_{i=1}^{10} i -(i-1) a_1 \frac{N_\mathrm{rare} - 1}{N_\mathrm{rare}}\,. -\end{split} -\end{equation} -Now $a_1$ takes the place of $f_1$ above, and means the number of -species with only one individual. -Here $S_\mathrm{abund}$ and $S_\mathrm{rare}$ are the numbers of -species of abundant and rare species, with an arbitrary upper limit of -10 individuals for a rare species, and $N_\mathrm{rare}$ is the total -number of individuals in rare species. The variance estimator uses -iterative solution, and it is best interpreted from the source code or -following \citet{OHara05}. - -The pool size -is estimated separately for each site, but if input is a data frame, -each site will be analysed. - -If log-normal abundance model is appropriate, it can be used to -estimate the pool size. Log-normal model has a finite number of -species which can be found integrating the log-normal: -\begin{equation} -S_p = S_\mu \sigma \sqrt{2 \pi} \,, -\end{equation} -where $S_\mu$ is the modal height or the expected number of species at -maximum (at $\mu$), and $\sigma$ is the width. Function -\code{veiledspec} estimates this integral from a model fitted either -with \code{prestondistr} or \code{prestonfit}, and fits the latter -if raw site data are given. Log-normal model may fit poorly, but we -can try: -<<>>= -veiledspec(prestondistr(BCI[k,])) -veiledspec(BCI[k,]) -@ - -\subsection{Probability of pool membership} - -Beals smoothing was originally suggested as a tool of regularizing data -for ordination. It regularizes data too strongly, -but it has been suggested as a method of estimating which of the -missing species could occur in a site, or which sites are suitable for -a species. The probability for each species at each site is assessed -from other species occurring on the site. - -Function \code{beals} implement Beals smoothing \citep{McCune87, - DeCaceresLegendre08}: -<<>>= -smo <- beals(BCI) -@ -We may see how the estimated probability of occurrence and observed -numbers of stems relate in one of the more familiar species. We study -only one species, and to avoid circular reasoning we do not include -the target species in the smoothing (Fig.~\ref{fig:beals}): -<>= -j <- which(colnames(BCI) == "Ceiba.pentandra") -plot(beals(BCI, species=j, include=FALSE), BCI[,j], - ylab="Occurrence", main="Ceiba pentandra", - xlab="Probability of occurrence") -@ -\begin{figure} -<>= -<> -@ -\caption{Beals smoothing for \emph{Ceiba pentandra}.} -\label{fig:beals} -\end{figure} - -\bibliography{vegan} - -\end{document} diff --git a/scTCRpy/rpackages/vegan/doc/index.html b/scTCRpy/rpackages/vegan/doc/index.html deleted file mode 100644 index 762859042..000000000 --- a/scTCRpy/rpackages/vegan/doc/index.html +++ /dev/null @@ -1,48 +0,0 @@ - - -R: Vignettes and other documentation - - - -

Vignettes and other documentation - -

-
-
-[Top] -
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Vignettes from package 'vegan'

- ------- - - - - - - - - - - - - - - - - - - - - - - - - -
vegan::decision-veganDesign decisions and implementationPDFsourceR code
vegan::diversity-veganDiversity analysis in veganPDFsourceR code
vegan::intro-veganIntroduction to ordination in veganPDFsourceR code
vegan::partitioningPartition of VariationPDFsourceR code
vegan::FAQ-veganvegan FAQHTMLsourceR code
- diff --git a/scTCRpy/rpackages/vegan/doc/intro-vegan.R b/scTCRpy/rpackages/vegan/doc/intro-vegan.R deleted file mode 100644 index 02324eb28..000000000 --- a/scTCRpy/rpackages/vegan/doc/intro-vegan.R +++ /dev/null @@ -1,198 +0,0 @@ -### R code from vignette source 'intro-vegan.Rnw' - -################################################### -### code chunk number 1: intro-vegan.Rnw:18-23 -################################################### -par(mfrow=c(1,1)) -options(width=72) -figset <- function() par(mar=c(4,4,1,1)+.1) -options(SweaveHooks = list(fig = figset)) -options("prompt" = "> ", "continue" = " ") - - -################################################### -### code chunk number 2: intro-vegan.Rnw:71-74 -################################################### -library(vegan) -data(dune) -ord <- decorana(dune) - - -################################################### -### code chunk number 3: intro-vegan.Rnw:77-78 -################################################### -ord - - -################################################### -### code chunk number 4: intro-vegan.Rnw:101-103 -################################################### -ord <- metaMDS(dune) -ord - - -################################################### -### code chunk number 5: a -################################################### -plot(ord) - - -################################################### -### code chunk number 6: intro-vegan.Rnw:118-119 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(ord) - - -################################################### -### code chunk number 7: a -################################################### -plot(ord, type = "n") -points(ord, display = "sites", cex = 0.8, pch=21, col="red", bg="yellow") -text(ord, display = "spec", cex=0.7, col="blue") - - -################################################### -### code chunk number 8: intro-vegan.Rnw:140-141 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(ord, type = "n") -points(ord, display = "sites", cex = 0.8, pch=21, col="red", bg="yellow") -text(ord, display = "spec", cex=0.7, col="blue") - - -################################################### -### code chunk number 9: intro-vegan.Rnw:206-208 -################################################### -data(dune.env) -attach(dune.env) - - -################################################### -### code chunk number 10: a -################################################### -plot(ord, disp="sites", type="n") -ordihull(ord, Management, col=1:4, lwd=3) -ordiellipse(ord, Management, col=1:4, kind = "ehull", lwd=3) -ordiellipse(ord, Management, col=1:4, draw="polygon") -ordispider(ord, Management, col=1:4, label = TRUE) -points(ord, disp="sites", pch=21, col="red", bg="yellow", cex=1.3) - - -################################################### -### code chunk number 11: intro-vegan.Rnw:219-220 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(ord, disp="sites", type="n") -ordihull(ord, Management, col=1:4, lwd=3) -ordiellipse(ord, Management, col=1:4, kind = "ehull", lwd=3) -ordiellipse(ord, Management, col=1:4, draw="polygon") -ordispider(ord, Management, col=1:4, label = TRUE) -points(ord, disp="sites", pch=21, col="red", bg="yellow", cex=1.3) - - -################################################### -### code chunk number 12: intro-vegan.Rnw:250-252 -################################################### -ord.fit <- envfit(ord ~ A1 + Management, data=dune.env, perm=999) -ord.fit - - -################################################### -### code chunk number 13: a -################################################### -plot(ord, dis="site") -plot(ord.fit) - - -################################################### -### code chunk number 14: b -################################################### -ordisurf(ord, A1, add=TRUE) - - -################################################### -### code chunk number 15: intro-vegan.Rnw:268-270 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(ord, dis="site") -plot(ord.fit) -ordisurf(ord, A1, add=TRUE) - - -################################################### -### code chunk number 16: intro-vegan.Rnw:290-292 -################################################### -ord <- cca(dune ~ A1 + Management, data=dune.env) -ord - - -################################################### -### code chunk number 17: a -################################################### -plot(ord) - - -################################################### -### code chunk number 18: intro-vegan.Rnw:299-300 -################################################### -getOption("SweaveHooks")[["fig"]]() -plot(ord) - - -################################################### -### code chunk number 19: intro-vegan.Rnw:317-318 -################################################### -cca(dune ~ ., data=dune.env) - - -################################################### -### code chunk number 20: intro-vegan.Rnw:327-328 -################################################### -anova(ord) - - -################################################### -### code chunk number 21: intro-vegan.Rnw:336-337 -################################################### -anova(ord, by="term", permutations=199) - - -################################################### -### code chunk number 22: intro-vegan.Rnw:342-343 -################################################### -anova(ord, by="mar", permutations=199) - - -################################################### -### code chunk number 23: a -################################################### -anova(ord, by="axis", permutations=499) - - -################################################### -### code chunk number 24: intro-vegan.Rnw:355-357 -################################################### -ord <- cca(dune ~ A1 + Management + Condition(Moisture), data=dune.env) -ord - - -################################################### -### code chunk number 25: intro-vegan.Rnw:362-363 -################################################### -anova(ord, by="term", permutations=499) - - -################################################### -### code chunk number 26: intro-vegan.Rnw:371-373 -################################################### -how <- how(nperm=499, plots = Plots(strata=dune.env$Moisture)) -anova(ord, by="term", permutations = how) - - -################################################### -### code chunk number 27: intro-vegan.Rnw:377-378 -################################################### -detach(dune.env) - - diff --git a/scTCRpy/rpackages/vegan/doc/intro-vegan.Rnw b/scTCRpy/rpackages/vegan/doc/intro-vegan.Rnw deleted file mode 100644 index b9389eaed..000000000 --- a/scTCRpy/rpackages/vegan/doc/intro-vegan.Rnw +++ /dev/null @@ -1,381 +0,0 @@ -% -*- mode: noweb; noweb-default-code-mode: R-mode; -*- -%\VignetteIndexEntry{Introduction to ordination in vegan} -\documentclass[a4paper,10pt]{article} -\usepackage{vegan} % vegan settings - -\title{Vegan: an introduction to ordination} -\author{Jari Oksanen} - -\date{\footnotesize{ - processed with vegan -\Sexpr{packageDescription("vegan", field="Version")} -in \Sexpr{R.version.string} on \today}} - -%% need no \usepackage{Sweave} -\begin{document} - -\SweaveOpts{strip.white=true} -<>= -par(mfrow=c(1,1)) -options(width=72) -figset <- function() par(mar=c(4,4,1,1)+.1) -options(SweaveHooks = list(fig = figset)) -options("prompt" = "> ", "continue" = " ") -@ - -\maketitle -\begin{abstract} - The document describes typical, simple work pathways of - vegetation ordination. Unconstrained ordination uses as examples - detrended correspondence analysis and non-metric multidimensional - scaling, and shows how to interpret their results by fitting - environmental vectors and factors or smooth environmental surfaces - to the graph. The basic plotting command, and more advanced plotting - commands for congested plots are also discussed, as well as adding - items such as ellipses, convex hulls, and other items for - classes. The constrained ordination uses constrained (canonical) - correspondence analysis as an example. It is first shown how a model - is defined, then the document discusses model building and - significance tests of the whole analysis, single constraints and - axes. -\end{abstract} -\tableofcontents - -\vspace{3ex} -\noindent \pkg{Vegan} is a package for community ecologists. This -documents explains how the commonly used ordination methods can be -performed in \pkg{vegan}. The document only is a very basic -introduction. Another document (\emph{vegan tutorial}) -(\url{http://cc.oulu.fi/~jarioksa/opetus/method/vegantutor.pdf}) gives -a longer and more detailed introduction to ordination. The -current document only describes a small part of all \pkg{vegan} -functions. For most functions, the canonical references are the -\pkg{vegan} help pages, and some of the most important additional -functions are listed at this document. - -\section{Ordination} - -The \pkg{vegan} package contains all common ordination methods: -Principal component analysis (function \code{rda}, or \code{prcomp} in -the base \proglang{R}), correspondence analysis (\code{cca}), -detrended correspondence analysis (\code{decorana}) and a wrapper for -non-metric multidimensional scaling (\code{metaMDS}). Functions -\code{rda} and \code{cca} mainly are designed for constrained -ordination, and will be discussed later. In this chapter I describe -functions \code{decorana} and \code{metaMDS}. - -\subsection{Detrended correspondence analysis} - - -Detrended correspondence analysis (\textsc{dca}) is done like this: -<<>>= -library(vegan) -data(dune) -ord <- decorana(dune) -@ -This saves ordination results in \code{ord}: -<<>>= -ord -@ -The display of results is very brief: only eigenvalues and used -options are listed. Actual ordination results are not shown, but you -can see them with command \code{summary(ord)}, or extract the scores -with command \code{scores}. The \code{plot} function also -automatically knows how to access the scores. - -\subsection{Non-metric multidimensional scaling} - - -Function \code{metaMDS} is a bit special case. The actual ordination -is performed by function \pkg{vegan} function \code{monoMDS} (or -alternatively using \code{isoMDS} of the \pkg{MASS} package). -Function \code{metaMDS} is a wrapper to perform non-metric -multidimensional scaling (\textsc{nmds}) like recommended in community -ordination: it uses adequate dissimilarity measures (function -\code{vegdist}), then it runs \textsc{nmds} several times with random -starting configurations, compares results (function -\code{procrustes}), and stops after finding twice a similar minimum -stress solution. Finally it scales and rotates the solution, and adds -species scores to the configuration as weighted averages (function -\code{wascores}): -<<>>= -ord <- metaMDS(dune) -ord -@ - -\section{Ordination graphics} - -Ordination is nothing but a way of drawing graphs, and it is best to -inspect ordinations only graphically (which also implies that they -should not be taken too seriously). - -All ordination results of \pkg{vegan} can be displayed with a -\code{plot} command (Fig. \ref{fig:plot}): -<>= -plot(ord) -@ -\begin{figure} -<>= -<> -@ -\caption{Default ordination plot.} -\label{fig:plot} -\end{figure} -Default \code{plot} command uses either black circles for sites and -red pluses for species, or black and red text for sites and species, -resp. The choices depend on the number of items in the plot and -ordination method. You can override the default choice by setting -\code{type = "p"} for points, or \code{type = "t"} for text. For -a better control of ordination graphics you can first draw an empty -plot (\code{type = "n"}) and then add species and sites separately -using \code{points} or \code{text} functions. In this way you can -combine points and text, and you can select colours and character -sizes freely (Fig. \ref{fig:plot.args}): -<>= -plot(ord, type = "n") -points(ord, display = "sites", cex = 0.8, pch=21, col="red", bg="yellow") -text(ord, display = "spec", cex=0.7, col="blue") -@ -\begin{figure} -<>= -<> -@ -\caption{A more colourful ordination plot where sites are points, and - species are text.} -\label{fig:plot.args} -\end{figure} - -All \pkg{vegan} ordination methods have a specific \code{plot} -function. In addition, \pkg{vegan} has an alternative plotting -function \code{ordiplot} that also knows many non-\pkg{vegan} -ordination methods, such as \code{prcomp}, \code{cmdscale} and -\code{isoMDS}. All \pkg{vegan} plot functions return invisibly -an \code{ordiplot} object, so that you can use \code{ordiplot} -support functions with the results (\code{points}, \code{text}, -\code{identify}). - -Function \code{ordirgl} (requires \pkg{rgl} package) provides -dynamic three-dimensional graphics that can be spun around or zoomed -into with your mouse. Function \pkg{ordiplot3d} (requires package -\code{scatterplot3d}) displays simple three-dimensional -scatterplots. - -\subsection{Cluttered plots} - -Ordination plots are often congested: there is a large number of sites -and species, and it may be impossible to display all clearly. In -particular, two or more species may have identical scores and are -plotted over each other. \pkg{Vegan} does not have (yet?) -automatic tools for clean plotting in these cases, but here some -methods you can try: -\begin{itemize} -\item Zoom into graph setting axis limits \code{xlim} and - \code{ylim}. You must typically set both, because \pkg{vegan} - will maintain equal aspect ratio of axes. -\item Use points and add label only to some points with \code{identify} - command. -\item Use \code{select} argument in ordination \code{text} and - \code{points} functions to only show the specified items. -\item Use \code{ordilabel} function that uses opaque background to - the text: some text labels will be covered, but the uppermost are - readable. -\item Use automatic \code{orditorp} function that uses text only if - this can be done without overwriting previous labels, but points in - other cases. -\item Use automatic \code{ordipointlabel} function that uses both - points and text labels, and tries to optimize the location of the - text to avoid overwriting. -\item Use interactive \code{orditkplot} function that draws both - points and labels for ordination scores, and allows you to drag - labels to better positions. You can export the results of the edited - graph to encapsulated \proglang{postscript}, \proglang{pdf}, - \proglang{png} or \proglang{jpeg} files, or copy directly to - encapsulated \proglang{postscript}, or return the edited positions - to \proglang{R} for further processing. -\end{itemize} - -\subsection{Adding items to ordination plots} - -\pkg{Vegan} has a group of functions for adding information about -classification or grouping of points onto ordination diagrams. -Function \code{ordihull} adds convex hulls, \code{ordiellipse} adds -ellipses enclosing all points in the group (ellipsoid hulls) or -ellipses of standard deviation, standard error or confidence areas, -and \code{ordispider} combines items to their centroid -(Fig. \ref{fig:ordihull}): -<<>>= -data(dune.env) -attach(dune.env) -@ -<>= -plot(ord, disp="sites", type="n") -ordihull(ord, Management, col=1:4, lwd=3) -ordiellipse(ord, Management, col=1:4, kind = "ehull", lwd=3) -ordiellipse(ord, Management, col=1:4, draw="polygon") -ordispider(ord, Management, col=1:4, label = TRUE) -points(ord, disp="sites", pch=21, col="red", bg="yellow", cex=1.3) -@ -\begin{figure} -<>= -<> -@ -\caption{Convex hull, ellipsoid hull, standard error ellipse and a spider web diagram - for Management levels in ordination.} -\label{fig:ordihull} -\end{figure} -In addition, you can overlay a cluster dendrogram from \code{hclust} -using \code{ordicluster} or a minimum spanning tree from -\code{spantree} with its \code{lines} function. Segmented arrows -can be added with \code{ordiarrows}, lines with -\code{ordisegments} and regular grids with \code{ordigrid}. - -\section{Fitting environmental variables} - -\pkg{Vegan} provides two functions for fitting environmental -variables onto ordination: -\begin{itemize} -\item \code{envfit} fits vectors of continuous variables and centroids - of levels of class variables (defined as \code{factor} in - \proglang{R}). The arrow shows the direction of the (increasing) - gradient, and the length of the arrow is proportional to the - correlation between the variable and the ordination. -\item \code{ordisurf} (which requires package \pkg{mgcv}) fits - smooth surfaces for continuous variables onto ordination using - thinplate splines with cross-validatory selection of smoothness. -\end{itemize} - -Function \code{envfit} can be called with a \code{formula} -interface, and it optionally can assess the ``significance'' of the -variables using permutation tests: -<<>>= -ord.fit <- envfit(ord ~ A1 + Management, data=dune.env, perm=999) -ord.fit -@ -The result can be drawn directly or added to an ordination diagram -(Fig. \ref{fig:envfit}): -<>= -plot(ord, dis="site") -plot(ord.fit) -@ - -Function \code{ordisurf} directly adds a fitted surface onto -ordination, but it returns the result of the fitted thinplate spline -\code{gam} (Fig. \ref{fig:envfit}): -<>= -ordisurf(ord, A1, add=TRUE) -@ -\begin{figure} -<>= -<> -<> -@ -\caption{Fitted vector and smooth surface for the thickness of A1 - horizon (\code{A1}, in cm), and centroids of Management levels.} -\label{fig:envfit} -\end{figure} - -\section{Constrained ordination} - -\pkg{Vegan} has three methods of constrained ordination: -constrained or ``canonical'' correspondence analysis (function -\code{cca}), redundancy analysis (function \code{rda}) and -distance-based redundancy analysis (function \code{capscale}). All -these functions can have a conditioning term that is ``partialled -out''. I only demonstrate \code{cca}, but all functions accept -similar commands and can be used in the same way. - -The preferred way is to use \code{formula} interface, where the left -hand side gives the community data frame and the right hand side lists -the constraining variables: -<<>>= -ord <- cca(dune ~ A1 + Management, data=dune.env) -ord -@ -The results can be plotted with (Fig. \ref{fig:cca}): -<>= -plot(ord) -@ -\begin{figure} -<>= -<> -@ -\caption{Default plot from constrained correspondence analysis.} -\label{fig:cca} -\end{figure} -There are three groups of items: sites, species and centroids (and -biplot arrows) of environmental variables. All these can be added -individually to an empty plot, and all previously explained tricks of -controlling graphics still apply. - -It is not recommended to perform constrained ordination with all -environmental variables you happen to have: adding the number of -constraints means slacker constraint, and you finally end up with -solution similar to unconstrained ordination. In that case it is -better to use unconstrained ordination with environmental fitting. -However, if you really want to do so, it is possible with the -following shortcut in \code{formula}: -<<>>= -cca(dune ~ ., data=dune.env) -@ - -\subsection{Significance tests} - -\pkg{vegan} provides permutation tests for the significance of -constraints. The test mimics standard analysis of variance function -(\code{anova}), and the default test analyses all constraints -simultaneously: -<<>>= -anova(ord) -@ -The function actually used was \code{anova.cca}, but you do not need -to give its name in full, because \proglang{R} automatically chooses the -correct \code{anova} variant for the result of constrained -ordination. - -It is also possible to analyse terms separately: -<<>>= -anova(ord, by="term", permutations=199) -@ -This test is sequential: the terms are analysed -in the order they happen to be in the model. You can also analyse -significances of marginal effects (``Type III effects''): -<<>>= -anova(ord, by="mar", permutations=199) -@ - -Moreover, it is possible to analyse significance of each axis: -<>= -anova(ord, by="axis", permutations=499) -@ - -\subsection{Conditioned or partial ordination} - -All constrained ordination methods can have terms that are partialled -out from the analysis before constraints: -<<>>= -ord <- cca(dune ~ A1 + Management + Condition(Moisture), data=dune.env) -ord -@ -This partials out the effect of \code{Moisture} before analysing the -effects of \code{A1} and \code{Management}. This also influences -the significances of the terms: -<<>>= -anova(ord, by="term", permutations=499) -@ -If we had a designed experiment, we may wish to restrict the -permutations so that the observations only are permuted within levels -of \code{Moisture}. Restricted permutation is based on the powerful -\pkg{permute} package. Function \code{how()} can be used to define -permutation schemes. In the following, we set the levels with -\code{plots} argument: -<<>>= -how <- how(nperm=499, plots = Plots(strata=dune.env$Moisture)) -anova(ord, by="term", permutations = how) -@ - -%%%%%%%%%%%%%%%%%%% -<>= -detach(dune.env) -@ - -\end{document} diff --git a/scTCRpy/rpackages/vegan/doc/partitioning.R b/scTCRpy/rpackages/vegan/doc/partitioning.R deleted file mode 100644 index 37d499f10..000000000 --- a/scTCRpy/rpackages/vegan/doc/partitioning.R +++ /dev/null @@ -1,34 +0,0 @@ -### R code from vignette source 'partitioning.Rnw' - -################################################### -### code chunk number 1: partitioning.Rnw:20-26 -################################################### -par(mfrow=c(1,1)) -figset <- function() par(mar=c(4,4,1,1)+.1) -options(SweaveHooks = list(fig = figset)) -library(vegan) -labs <- paste("Table", 1:4) -cls <- c("hotpink", "skyblue", "orange", "limegreen") - - -################################################### -### code chunk number 2: partitioning.Rnw:39-40 -################################################### -getOption("SweaveHooks")[["fig"]]() -showvarparts(2, bg = cls, Xnames=labs) - - -################################################### -### code chunk number 3: partitioning.Rnw:51-52 -################################################### -getOption("SweaveHooks")[["fig"]]() -showvarparts(3, bg = cls, Xnames=labs) - - -################################################### -### code chunk number 4: partitioning.Rnw:64-65 -################################################### -getOption("SweaveHooks")[["fig"]]() -showvarparts(4, bg = cls, Xnames=labs) - - diff --git a/scTCRpy/rpackages/vegan/doc/partitioning.Rnw b/scTCRpy/rpackages/vegan/doc/partitioning.Rnw deleted file mode 100644 index 14dda7ce5..000000000 --- a/scTCRpy/rpackages/vegan/doc/partitioning.Rnw +++ /dev/null @@ -1,80 +0,0 @@ -%\VignetteIndexEntry{Partition of Variation} -%% This file rewrites Pierre Legendre's introduction and takes pages -%% of Pierre Legendre's pdf documents and puts them together. - -\documentclass[10pt]{article} -\usepackage{vegan} %% vegan setup -\usepackage{pdfpages} -\setkeys{Gin}{width=0.6\linewidth} - - -\title{Diagrams and Procedures for Partition of Variation} -\author{Pierre Legendre} -\date{\footnotesize{ - processed with vegan \Sexpr{packageDescription("vegan", field="Version")} - in \Sexpr{R.version.string} on \today}} - -\begin{document} -%% Sweave document -\SweaveOpts{strip.white=true} -<>= -par(mfrow=c(1,1)) -figset <- function() par(mar=c(4,4,1,1)+.1) -options(SweaveHooks = list(fig = figset)) -library(vegan) -labs <- paste("Table", 1:4) -cls <- c("hotpink", "skyblue", "orange", "limegreen") -@ - -\maketitle - -\noindent Diagrams describing the partitions of variation of a -response data table by two (Fig.~\ref{fig:part2}), three -(Fig.~\ref{fig:part3}) and four tables (Fig.~\ref{fig:part4}) of -explanatory variables. The fraction names [a] to [p] in the output of -\code{varpart} function follow the notation in these Venn diagrams, -and the diagrams were produced using the \code{showvarparts} function. -%%%%%%%%%%%%%%% -\begin{figure}[!ht] -<>= -showvarparts(2, bg = cls, Xnames=labs) -@ -\caption{3 regression/ canonical analyses and 3 subtraction equations - are needed to estimate the $4\;(=2^2)$ fractions. - - [a] and [c] can be tested for significance (3 canonical analyses per - permutation). Fraction [b] cannot be tested singly.} -\label{fig:part2} -\end{figure} -%%%%%%%%%%% -\begin{figure}[!ht] -<>= -showvarparts(3, bg = cls, Xnames=labs) -@ -\caption{7 regression/ canonical analyses and 10 subtraction equations - are needed to estimate the $8\;(=2^3)$ fractions. - - [a] to [c] and subsets containing [a] to [c] can be tested for - significance (4 canonical analyses per permutation to test [a] to - [c]). Fractions [d] to [g] cannot be tested singly.} -\label{fig:part3} -\end{figure} -%%%%%%%%%%% -\begin{figure}[!ht] -<>= -showvarparts(4, bg = cls, Xnames=labs) -@ -\caption{15 regression/ canonical analyses and 27 subtraction equations - are needed to estimate the $16\;(=2^4)$ fractions. - - [a] to [d] and subsets containing [a] to [d] can be tested for - significance (5 canonical analyses per permutation to test [a] to - [d]). Fractions [e] to [o] cannot be tested singly.} -\label{fig:part4} -\end{figure} -\clearpage -\setkeys{Gin}{width=\paperwidth} -%% Add partitioning models 2-3 and 4. -\includepdf[fitpaper=true,pages=-]{varpart23.pdf} -\includepdf[fitpaper=true, pages=-]{varpart4.pdf} -\end{document} diff --git a/scTCRpy/rpackages/vegan/help/AnIndex b/scTCRpy/rpackages/vegan/help/AnIndex deleted file mode 100644 index e06574b0c..000000000 --- a/scTCRpy/rpackages/vegan/help/AnIndex +++ /dev/null @@ -1,552 +0,0 @@ -vegan-package vegan-package -add1.cca add1.cca -addCailliez vegan-internal -addLingoes vegan-internal -adipart adipart -adipart.default adipart -adipart.formula adipart -adonis adonis -adonis2 adonis -AIC.fitspecaccum specaccum -AIC.radfit radfit -AIC.radfit.frame radfit -alias.cca goodness.cca -anosim anosim -anova.betadisper betadisper -anova.cca anova.cca -as.fisher fisherfit -as.hclust.spantree spantree -as.mcmc.oecosimu oecosimu -as.mcmc.permat permatfull -as.mlm vegan-deprecated 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betadiver -plot.cascadeKM cascadeKM -plot.cca plot.cca -plot.clamtest clamtest -plot.contribdiv contribdiv -plot.decorana decorana -plot.envfit envfit -plot.fisher fisherfit -plot.fisherfit fisherfit -plot.fitspecaccum specaccum -plot.humpfit humpfit -plot.isomap isomap -plot.mantel.correlog mantel.correlog -plot.meandist mrpp -plot.metaMDS metaMDS -plot.monoMDS monoMDS -plot.MOStest MOStest -plot.nestednodf nestedtemp -plot.nestedtemp nestedtemp -plot.ordipointlabel ordipointlabel -plot.ordisurf ordisurf -plot.orditkplot orditkplot -plot.permat permatfull -plot.poolaccum specpool -plot.prc prc -plot.preston fisherfit -plot.prestonfit fisherfit -plot.procrustes procrustes -plot.rad radfit -plot.radfit radfit -plot.radfit.frame radfit -plot.radline radfit -plot.renyi renyi -plot.renyiaccum renyi -plot.spantree spantree -plot.specaccum specaccum -plot.taxondive taxondive -plot.varpart varpart -plot.varpart234 varpart -plot.vegandensity vegan-defunct -plot.wcmdscale wcmdscale -points.cca plot.cca -points.decorana decorana -points.humpfit humpfit -points.metaMDS metaMDS -points.monoMDS monoMDS -points.ordiplot ordiplot -points.orditkplot orditkplot -points.procrustes procrustes -points.radfit radfit -points.radline radfit -poolaccum specpool -postMDS metaMDS -prc prc -predict.cca predict.cca -predict.decorana predict.cca -predict.fitspecaccum specaccum -predict.humpfit humpfit -predict.procrustes procrustes -predict.radfit radfit -predict.radfit.frame radfit -predict.radline radfit -predict.rda predict.cca -predict.specaccum specaccum -pregraphKM cascadeKM -prepanel.ordi3d ordixyplot -prestondistr fisherfit -prestonfit fisherfit -print.betadisper betadisper -print.cca cca.object -print.commsim commsim -print.nullmodel nullmodel -print.permat permatfull -print.simmat nullmodel -print.specaccum specaccum -print.summary.cca plot.cca -print.summary.decorana decorana -print.summary.permat permatfull -procrustes procrustes -profile.humpfit humpfit -profile.MOStest MOStest -protest procrustes -pyrifos pyrifos -qqmath.permustats permustats -qqnorm.permustats permustats -qr.cca influence.cca -rad.lognormal radfit -rad.null radfit -rad.preempt radfit -rad.zipf radfit -rad.zipfbrot radfit -radfit radfit -radfit.data.frame radfit -radfit.default radfit -radlattice radfit -rankindex rankindex -rarecurve rarefy -rarefy rarefy -rareslope rarefy -raupcrick raupcrick -rda cca -rda.default cca -rda.formula cca -read.cep read.cep -renyi renyi -renyiaccum renyi -reorder.hclust reorder.hclust -residuals.cca predict.cca -residuals.procrustes procrustes -rev.hclust reorder.hclust -rrarefy rarefy -RsquareAdj RsquareAdj -RsquareAdj.cca RsquareAdj -RsquareAdj.default RsquareAdj -RsquareAdj.glm RsquareAdj -RsquareAdj.lm RsquareAdj -RsquareAdj.rda RsquareAdj -rstandard.cca influence.cca -rstudent.cca influence.cca -scores scores -scores.betadisper betadisper -scores.betadiver betadiver -scores.cca plot.cca -scores.decorana decorana -scores.default scores -scores.envfit envfit -scores.hclust reorder.hclust -scores.lda scores -scores.metaMDS metaMDS -scores.monoMDS monoMDS -scores.ordihull ordihull -scores.ordiplot ordiplot -scores.orditkplot orditkplot -scores.pcnm pcnm -scores.rda plot.cca -scores.wcmdscale wcmdscale -screeplot.cca screeplot.cca -screeplot.decorana screeplot.cca -screeplot.prcomp screeplot.cca -screeplot.princomp screeplot.cca -showvarparts varpart -sigma.cca influence.cca -simmat nullmodel -simper simper -simpleDBRDA varpart -simpleRDA2 varpart -simulate.capscale simulate.rda -simulate.cca simulate.rda -simulate.nullmodel nullmodel -simulate.rda simulate.rda -sipoo sipoo -sipoo.map sipoo -smbind nullmodel -spandepth spantree -spantree spantree -specaccum specaccum -specnumber diversity -specpool specpool -specpool2vect specpool -specslope specaccum -spenvcor goodness.cca -sppscores sppscores -sppscores<- sppscores -sppscores<-.capscale sppscores -sppscores<-.dbrda sppscores -sppscores<-.metaMDS sppscores -SSarrhenius SSarrhenius -SSD.cca influence.cca -SSgitay SSarrhenius -SSgleason SSarrhenius -SSlomolino SSarrhenius -stepacross stepacross -str.nullmodel nullmodel -stressplot goodness.metaMDS -stressplot.capscale stressplot.wcmdscale -stressplot.cca stressplot.wcmdscale -stressplot.dbrda stressplot.wcmdscale -stressplot.default goodness.metaMDS -stressplot.monoMDS goodness.metaMDS -stressplot.prcomp stressplot.wcmdscale -stressplot.princomp stressplot.wcmdscale -stressplot.rda stressplot.wcmdscale -stressplot.wcmdscale stressplot.wcmdscale -summary.anosim anosim -summary.bioenv bioenv -summary.cca plot.cca -summary.clamtest clamtest -summary.decorana decorana -summary.dispweight dispweight -summary.eigenvals eigenvals -summary.humpfit humpfit -summary.isomap isomap -summary.meandist mrpp -summary.ordiellipse ordihull -summary.ordihull ordihull -summary.permat permatfull -summary.permustats permustats -summary.poolaccum specpool -summary.prc prc -summary.procrustes procrustes -summary.radfit.frame radfit 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Community Ecology Package - -

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Documentation for package ‘vegan’ version 2.5-5

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Help Pages

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vegan-packageCommunity Ecology Package: Ordination, Diversity and Dissimilarities
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add1.ccaAdd or Drop Single Terms to a Constrained Ordination Model
adipartAdditive Diversity Partitioning and Hierarchical Null Model Testing
adipart.defaultAdditive Diversity Partitioning and Hierarchical Null Model Testing
adipart.formulaAdditive Diversity Partitioning and Hierarchical Null Model Testing
adonisPermutational Multivariate Analysis of Variance Using Distance Matrices
adonis2Permutational Multivariate Analysis of Variance Using Distance Matrices
AIC.fitspecaccumSpecies Accumulation Curves
AIC.radfitRank - Abundance or Dominance / Diversity Models
AIC.radfit.frameRank - Abundance or Dominance / Diversity Models
alias.ccaDiagnostic Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
anosimAnalysis of Similarities
anova.betadisperMultivariate homogeneity of groups dispersions (variances)
anova.ccaPermutation Test for Constrained Correspondence Analysis, Redundancy Analysis and Constrained Analysis of Principal Coordinates
as.fisherFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
as.hclust.spantreeMinimum Spanning Tree
as.mcmc.oecosimuEvaluate Statistics with Null Models of Biological Communities
as.mcmc.permatMatrix Permutation Algorithms for Presence-Absence and Count Data
as.mlmDeprecated Functions in vegan package
as.mlm.ccaDeprecated Functions in vegan package
as.mlm.rdaDeprecated Functions in vegan package
as.prestonFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
as.radRank - Abundance or Dominance / Diversity Models
as.ts.oecosimuEvaluate Statistics with Null Models of Biological Communities
as.ts.permatMatrix Permutation Algorithms for Presence-Absence and Count Data
avgdistAveraged Subsampled Dissimilarity Matrices
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BCIBarro Colorado Island Tree Counts
BCI.envBarro Colorado Island Tree Counts
bealsBeals Smoothing and Degree of Absence
betadisperMultivariate homogeneity of groups dispersions (variances)
betadiverIndices of beta Diversity
bgdispersalCoefficients of Biogeographical Dispersal Direction
bioenvBest Subset of Environmental Variables with Maximum (Rank) Correlation with Community Dissimilarities
bioenv.defaultBest Subset of Environmental Variables with Maximum (Rank) Correlation with Community Dissimilarities
bioenv.formulaBest Subset of Environmental Variables with Maximum (Rank) Correlation with Community Dissimilarities
bioenvdistBest Subset of Environmental Variables with Maximum (Rank) Correlation with Community Dissimilarities
biplot.ccaPCA biplot
biplot.CCorACanonical Correlation Analysis
biplot.rdaPCA biplot
boxplot.betadisperMultivariate homogeneity of groups dispersions (variances)
boxplot.permustatsExtract, Analyse and Display Permutation Results
boxplot.specaccumSpecies Accumulation Curves
bstickScreeplots for Ordination Results and Broken Stick Distributions
bstick.ccaScreeplots for Ordination Results and Broken Stick Distributions
bstick.decoranaScreeplots for Ordination Results and Broken Stick Distributions
bstick.defaultScreeplots for Ordination Results and Broken Stick Distributions
bstick.prcompScreeplots for Ordination Results and Broken Stick Distributions
bstick.princompScreeplots for Ordination Results and Broken Stick Distributions
- -

-- C --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
c.permustatsExtract, Analyse and Display Permutation Results
calibratePrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
calibrate.ccaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
calibrate.ordisurfFit and Plot Smooth Surfaces of Variables on Ordination.
capscale[Partial] Distance-based Redundancy Analysis
cascadeKMK-means partitioning using a range of values of K
cca[Partial] [Constrained] Correspondence Analysis and Redundancy Analysis
cca.default[Partial] [Constrained] Correspondence Analysis and Redundancy Analysis
cca.formula[Partial] [Constrained] Correspondence Analysis and Redundancy Analysis
cca.objectResult Object from Constrained Ordination
CCorACanonical Correlation Analysis
chaodistDesign your own Dissimilarities
cIndexKMK-means partitioning using a range of values of K
clamtestMultinomial Species Classification Method (CLAM)
coef.ccaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
coef.radfitRank - Abundance or Dominance / Diversity Models
coef.radfit.frameRank - Abundance or Dominance / Diversity Models
coef.rdaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
commsimCreate an Object for Null Model Algorithms
confint.MOStestMitchell-Olds & Shaw Test for the Location of Quadratic Extreme
contribdivContribution Diversity Approach
cooks.distance.ccaLinear Model Diagnostics for Constrained Ordination
cophenetic.spantreeMinimum Spanning Tree
coverscaleDisplay Compact Ordered Community Tables
cutreeordReorder a Hierarchical Clustering Tree
- -

-- D --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
dbrda[Partial] Distance-based Redundancy Analysis
decoranaDetrended Correspondence Analysis and Basic Reciprocal Averaging
decostandStandardization Methods for Community Ecology
density.permustatsExtract, Analyse and Display Permutation Results
densityplot.permustatsExtract, Analyse and Display Permutation Results
designdistDesign your own Dissimilarities
deviance.ccaStatistics Resembling Deviance and AIC for Constrained Ordination
deviance.fitspecaccumSpecies Accumulation Curves
deviance.radfitRank - Abundance or Dominance / Diversity Models
deviance.radfit.frameRank - Abundance or Dominance / Diversity Models
deviance.rdaStatistics Resembling Deviance and AIC for Constrained Ordination
df.residual.ccaLinear Model Diagnostics for Constrained Ordination
dispindmorisitaMorisita index of intraspecific aggregation
dispweightDispersion-based weighting of species counts
distconnectedConnectedness of Dissimilarities
diversityEcological Diversity Indices
downweightDetrended Correspondence Analysis and Basic Reciprocal Averaging
drarefyRarefaction Species Richness
drop1.ccaAdd or Drop Single Terms to a Constrained Ordination Model
duneVegetation and Environment in Dutch Dune Meadows.
dune.envVegetation and Environment in Dutch Dune Meadows.
dune.phylodisTaxonomic Classification and Phylogeny of Dune Meadow Species
dune.taxonTaxonomic Classification and Phylogeny of Dune Meadow Species
- -

-- E --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
eigengradWeighted Averages Scores for Species
eigenvalsExtract Eigenvalues from an Ordination Object
eigenvals.betadisperMultivariate homogeneity of groups dispersions (variances)
eigenvals.ccaExtract Eigenvalues from an Ordination Object
eigenvals.defaultExtract Eigenvalues from an Ordination Object
eigenvals.dudiExtract Eigenvalues from an Ordination Object
eigenvals.pcaExtract Eigenvalues from an Ordination Object
eigenvals.pcnmExtract Eigenvalues from an Ordination Object
eigenvals.pcoExtract Eigenvalues from an Ordination Object
eigenvals.prcompExtract Eigenvalues from an Ordination Object
eigenvals.princompExtract Eigenvalues from an Ordination Object
eigenvals.wcmdscaleExtract Eigenvalues from an Ordination Object
envfitFits an Environmental Vector or Factor onto an Ordination
envfit.defaultFits an Environmental Vector or Factor onto an Ordination
envfit.formulaFits an Environmental Vector or Factor onto an Ordination
estaccumRExtrapolated Species Richness in a Species Pool
estimateRExtrapolated Species Richness in a Species Pool
estimateR.data.frameExtrapolated Species Richness in a Species Pool
estimateR.defaultExtrapolated Species Richness in a Species Pool
estimateR.matrixExtrapolated Species Richness in a Species Pool
eventstarScale Parameter at the Minimum of the Tsallis Evenness Profile
extractAIC.ccaStatistics Resembling Deviance and AIC for Constrained Ordination
- -

-- F --

- - - - - - - - - - - - - - - - - - - - - - - - - - -
factorfitFits an Environmental Vector or Factor onto an Ordination
fieller.MOStestMitchell-Olds & Shaw Test for the Location of Quadratic Extreme
fisher.alphaEcological Diversity Indices
fisherfitFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
fitspecaccumSpecies Accumulation Curves
fitted.capscalePrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
fitted.ccaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
fitted.dbrdaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
fitted.procrustesProcrustes Rotation of Two Configurations and PROTEST
fitted.radfitRank - Abundance or Dominance / Diversity Models
fitted.radfit.frameRank - Abundance or Dominance / Diversity Models
fitted.rdaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
- -

-- G --

- - - - - - - - - - - - -
gdispweightDispersion-based weighting of species counts
goodnessDiagnostic Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
goodness.ccaDiagnostic Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
goodness.metaMDSGoodness of Fit and Shepard Plot for Nonmetric Multidimensional Scaling
goodness.monoMDSGoodness of Fit and Shepard Plot for Nonmetric Multidimensional Scaling
- -

-- H --

- - - - - - - - - - - - - - - - - - -
hatvalues.ccaLinear Model Diagnostics for Constrained Ordination
hatvalues.rdaLinear Model Diagnostics for Constrained Ordination
head.summary.ccaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
hiersimuAdditive Diversity Partitioning and Hierarchical Null Model Testing
hiersimu.defaultAdditive Diversity Partitioning and Hierarchical Null Model Testing
hiersimu.formulaAdditive Diversity Partitioning and Hierarchical Null Model Testing
humpfitDeprecated Functions in vegan package
humpfit-deprecatedNo-interaction Model for Hump-backed Species Richness vs. Biomass
- -

-- I --

- - - - - - - - - - - - - - - - -
identify.ordiplotAlternative plot and identify Functions for Ordination
indpowerIndicator Power of Species
inertcompDiagnostic Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
initMDSNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
intersetcorDiagnostic Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
isomapIsometric Feature Mapping Ordination
isomapdistIsometric Feature Mapping Ordination
- -

-- K --

- - - - - - -
kendall.globalKendall coefficient of concordance
kendall.postKendall coefficient of concordance
- -

-- L --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
labels.envfitFits an Environmental Vector or Factor onto an Ordination
lines.fitspecaccumSpecies Accumulation Curves
lines.humpfitNo-interaction Model for Hump-backed Species Richness vs. Biomass
lines.permatMatrix Permutation Algorithms for Presence-Absence and Count Data
lines.prestonFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
lines.prestonfitFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
lines.procrustesProcrustes Rotation of Two Configurations and PROTEST
lines.radfitRank - Abundance or Dominance / Diversity Models
lines.radlineRank - Abundance or Dominance / Diversity Models
lines.spantreeMinimum Spanning Tree
lines.specaccumSpecies Accumulation Curves
linestackPlots One-dimensional Diagrams without Overwriting Labels
logLik, radfitRank - Abundance or Dominance / Diversity Models
logLik, radfit.frameRank - Abundance or Dominance / Diversity Models
logLik.fitspecaccumSpecies Accumulation Curves
- -

-- M --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
make.cepnamesAbbreviates a Botanical or Zoological Latin Name into an Eight-character Name
make.commsimCreate an Object for Null Model Algorithms
mantelMantel and Partial Mantel Tests for Dissimilarity Matrices
mantel.correlogMantel Correlogram
mantel.partialMantel and Partial Mantel Tests for Dissimilarity Matrices
MDSrotateRotate First MDS Dimension Parallel to an External Variable
meandistMulti Response Permutation Procedure and Mean Dissimilarity Matrix
metaMDSNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
metaMDSdistNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
metaMDSiterNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
metaMDSredistNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
miteOribatid Mite Data with Explanatory Variables
mite.envOribatid Mite Data with Explanatory Variables
mite.pcnmOribatid Mite Data with Explanatory Variables
mite.xyOribatid Mite Data with Explanatory Variables
model.frame.ccaResult Object from Constrained Ordination
model.matrix.ccaResult Object from Constrained Ordination
model.matrix.rdaResult Object from Constrained Ordination
monoMDSGlobal and Local Non-metric Multidimensional Scaling and Linear and Hybrid Scaling
MOStestMitchell-Olds & Shaw Test for the Location of Quadratic Extreme
mrppMulti Response Permutation Procedure and Mean Dissimilarity Matrix
msoFunctions for performing and displaying a spatial partitioning of cca or rda results
msoplotFunctions for performing and displaying a spatial partitioning of cca or rda results
multipartMultiplicative Diversity Partitioning
multipart.defaultMultiplicative Diversity Partitioning
multipart.formulaMultiplicative Diversity Partitioning
- -

-- N --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
nestedbetajacNestedness Indices for Communities of Islands or Patches
nestedbetasorNestedness Indices for Communities of Islands or Patches
nestedcheckerNestedness Indices for Communities of Islands or Patches
nesteddiscNestedness Indices for Communities of Islands or Patches
nestedn0Nestedness Indices for Communities of Islands or Patches
nestednodfNestedness Indices for Communities of Islands or Patches
nestedtempNestedness Indices for Communities of Islands or Patches
no.sharedConnectedness of Dissimilarities
nobs.adonisExtract the Number of Observations from a vegan Fit.
nobs.betadisperExtract the Number of Observations from a vegan Fit.
nobs.ccaExtract the Number of Observations from a vegan Fit.
nobs.CCorAExtract the Number of Observations from a vegan Fit.
nobs.decoranaExtract the Number of Observations from a vegan Fit.
nobs.fitspecaccumSpecies Accumulation Curves
nobs.isomapExtract the Number of Observations from a vegan Fit.
nobs.metaMDSExtract the Number of Observations from a vegan Fit.
nobs.pcnmExtract the Number of Observations from a vegan Fit.
nobs.procrustesExtract the Number of Observations from a vegan Fit.
nobs.radExtract the Number of Observations from a vegan Fit.
nobs.varpartExtract the Number of Observations from a vegan Fit.
nobs.wcmdscaleExtract the Number of Observations from a vegan Fit.
nullmodelNull Model and Simulation
- -

-- O --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
oecosimuEvaluate Statistics with Null Models of Biological Communities
ordConstrainedResult Object from Constrained Ordination
orderingKMK-means partitioning using a range of values of K
ordiareatestDisplay Groups or Factor Levels in Ordination Diagrams
ordiArrowMulSupport Functions for Drawing Vectors
ordiarrowsAdd Arrows and Line Segments to Ordination Diagrams
ordiArrowTextXYSupport Functions for Drawing Vectors
ordibarDisplay Groups or Factor Levels in Ordination Diagrams
ordicloudTrellis (Lattice) Plots for Ordination
ordiclusterDisplay Groups or Factor Levels in Ordination Diagrams
ordiellipseDisplay Groups or Factor Levels in Ordination Diagrams
ordigridAdd Arrows and Line Segments to Ordination Diagrams
ordihullDisplay Groups or Factor Levels in Ordination Diagrams
ordilabelAdd Text on Non-transparent Label to an Ordination Plot.
ordilattice.getEnvfitTrellis (Lattice) Plots for Ordination
ordimedianMultivariate homogeneity of groups dispersions (variances)
ordiplotAlternative plot and identify Functions for Ordination
ordipointlabelOrdination Plots with Points and Optimized Locations for Text
ordiR2stepChoose a Model by Permutation Tests in Constrained Ordination
ordiresidsPlots of Residuals and Fitted Values for Constrained Ordination
ordisegmentsAdd Arrows and Line Segments to Ordination Diagrams
ordispiderDisplay Groups or Factor Levels in Ordination Diagrams
ordisplomTrellis (Lattice) Plots for Ordination
ordistepChoose a Model by Permutation Tests in Constrained Ordination
ordisurfFit and Plot Smooth Surfaces of Variables on Ordination.
ordisurf.defaultFit and Plot Smooth Surfaces of Variables on Ordination.
ordisurf.formulaFit and Plot Smooth Surfaces of Variables on Ordination.
orditkplotOrdination Plot with Movable Labels
orditorpAdd Text or Points to Ordination Plots
ordixyplotTrellis (Lattice) Plots for Ordination
ordiYbarResult Object from Constrained Ordination
- -

-- P --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
panel.ordiTrellis (Lattice) Plots for Ordination
panel.ordi3dTrellis (Lattice) Plots for Ordination
panel.ordiarrowsTrellis (Lattice) Plots for Ordination
pcnmPrincipal Coordinates of Neighbourhood Matrix
permatfullMatrix Permutation Algorithms for Presence-Absence and Count Data
permatswapMatrix Permutation Algorithms for Presence-Absence and Count Data
permustatsExtract, Analyse and Display Permutation Results
permustats.adonisExtract, Analyse and Display Permutation Results
permustats.anosimExtract, Analyse and Display Permutation Results
permustats.anova.ccaExtract, Analyse and Display Permutation Results
permustats.CCorAExtract, Analyse and Display Permutation Results
permustats.envfitExtract, Analyse and Display Permutation Results
permustats.factorfitExtract, Analyse and Display Permutation Results
permustats.mantelExtract, Analyse and Display Permutation Results
permustats.mrppExtract, Analyse and Display Permutation Results
permustats.msoExtract, Analyse and Display Permutation Results
permustats.oecosimuExtract, Analyse and Display Permutation Results
permustats.ordiareatestExtract, Analyse and Display Permutation Results
permustats.permutest.betadisperExtract, Analyse and Display Permutation Results
permustats.permutest.ccaExtract, Analyse and Display Permutation Results
permustats.protestExtract, Analyse and Display Permutation Results
permustats.vectorfitExtract, Analyse and Display Permutation Results
permutationsPermutation tests in Vegan
permutestPermutation Test for Constrained Correspondence Analysis, Redundancy Analysis and Constrained Analysis of Principal Coordinates
permutest.betadisperPermutation test of multivariate homogeneity of groups dispersions (variances)
permutest.ccaPermutation Test for Constrained Correspondence Analysis, Redundancy Analysis and Constrained Analysis of Principal Coordinates
persp.renyiaccumRenyi and Hill Diversities and Corresponding Accumulation Curves
persp.tsallisaccumTsallis Diversity and Corresponding Accumulation Curves
plot.anosimAnalysis of Similarities
plot.betadisperMultivariate homogeneity of groups dispersions (variances)
plot.betadiverIndices of beta Diversity
plot.cascadeKMK-means partitioning using a range of values of K
plot.ccaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
plot.clamtestMultinomial Species Classification Method (CLAM)
plot.contribdivContribution Diversity Approach
plot.decoranaDetrended Correspondence Analysis and Basic Reciprocal Averaging
plot.envfitFits an Environmental Vector or Factor onto an Ordination
plot.fisherFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
plot.fisherfitFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
plot.fitspecaccumSpecies Accumulation Curves
plot.humpfitNo-interaction Model for Hump-backed Species Richness vs. Biomass
plot.isomapIsometric Feature Mapping Ordination
plot.mantel.correlogMantel Correlogram
plot.meandistMulti Response Permutation Procedure and Mean Dissimilarity Matrix
plot.metaMDSNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
plot.monoMDSGlobal and Local Non-metric Multidimensional Scaling and Linear and Hybrid Scaling
plot.MOStestMitchell-Olds & Shaw Test for the Location of Quadratic Extreme
plot.nestednodfNestedness Indices for Communities of Islands or Patches
plot.nestedtempNestedness Indices for Communities of Islands or Patches
plot.ordipointlabelOrdination Plots with Points and Optimized Locations for Text
plot.ordisurfFit and Plot Smooth Surfaces of Variables on Ordination.
plot.orditkplotOrdination Plot with Movable Labels
plot.permatMatrix Permutation Algorithms for Presence-Absence and Count Data
plot.poolaccumExtrapolated Species Richness in a Species Pool
plot.prcPrincipal Response Curves for Treatments with Repeated Observations
plot.prestonFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
plot.prestonfitFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
plot.procrustesProcrustes Rotation of Two Configurations and PROTEST
plot.radRank - Abundance or Dominance / Diversity Models
plot.radfitRank - Abundance or Dominance / Diversity Models
plot.radfit.frameRank - Abundance or Dominance / Diversity Models
plot.radlineRank - Abundance or Dominance / Diversity Models
plot.renyiRenyi and Hill Diversities and Corresponding Accumulation Curves
plot.renyiaccumRenyi and Hill Diversities and Corresponding Accumulation Curves
plot.spantreeMinimum Spanning Tree
plot.specaccumSpecies Accumulation Curves
plot.taxondiveIndices of Taxonomic Diversity and Distinctness
plot.varpartPartition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
plot.varpart234Partition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
plot.wcmdscaleWeighted Classical (Metric) Multidimensional Scaling
points.ccaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
points.decoranaDetrended Correspondence Analysis and Basic Reciprocal Averaging
points.humpfitNo-interaction Model for Hump-backed Species Richness vs. Biomass
points.metaMDSNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
points.monoMDSGlobal and Local Non-metric Multidimensional Scaling and Linear and Hybrid Scaling
points.ordiplotAlternative plot and identify Functions for Ordination
points.orditkplotOrdination Plot with Movable Labels
points.procrustesProcrustes Rotation of Two Configurations and PROTEST
points.radfitRank - Abundance or Dominance / Diversity Models
points.radlineRank - Abundance or Dominance / Diversity Models
poolaccumExtrapolated Species Richness in a Species Pool
postMDSNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
prcPrincipal Response Curves for Treatments with Repeated Observations
predict.ccaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
predict.decoranaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
predict.fitspecaccumSpecies Accumulation Curves
predict.humpfitNo-interaction Model for Hump-backed Species Richness vs. Biomass
predict.procrustesProcrustes Rotation of Two Configurations and PROTEST
predict.radfitRank - Abundance or Dominance / Diversity Models
predict.radfit.frameRank - Abundance or Dominance / Diversity Models
predict.radlineRank - Abundance or Dominance / Diversity Models
predict.rdaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
predict.specaccumSpecies Accumulation Curves
pregraphKMK-means partitioning using a range of values of K
prepanel.ordi3dTrellis (Lattice) Plots for Ordination
prestondistrFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
prestonfitFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
print.betadisperMultivariate homogeneity of groups dispersions (variances)
print.ccaResult Object from Constrained Ordination
print.commsimCreate an Object for Null Model Algorithms
print.nullmodelNull Model and Simulation
print.permatMatrix Permutation Algorithms for Presence-Absence and Count Data
print.simmatNull Model and Simulation
print.specaccumSpecies Accumulation Curves
print.summary.ccaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
print.summary.decoranaDetrended Correspondence Analysis and Basic Reciprocal Averaging
print.summary.permatMatrix Permutation Algorithms for Presence-Absence and Count Data
procrustesProcrustes Rotation of Two Configurations and PROTEST
profile.humpfitNo-interaction Model for Hump-backed Species Richness vs. Biomass
profile.MOStestMitchell-Olds & Shaw Test for the Location of Quadratic Extreme
protestProcrustes Rotation of Two Configurations and PROTEST
pyrifosResponse of Aquatic Invertebrates to Insecticide Treatment
- -

-- Q --

- - - - - - - - -
qqmath.permustatsExtract, Analyse and Display Permutation Results
qqnorm.permustatsExtract, Analyse and Display Permutation Results
qr.ccaLinear Model Diagnostics for Constrained Ordination
- -

-- R --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
rad.lognormalRank - Abundance or Dominance / Diversity Models
rad.nullRank - Abundance or Dominance / Diversity Models
rad.preemptRank - Abundance or Dominance / Diversity Models
rad.zipfRank - Abundance or Dominance / Diversity Models
rad.zipfbrotRank - Abundance or Dominance / Diversity Models
radfitRank - Abundance or Dominance / Diversity Models
radfit.data.frameRank - Abundance or Dominance / Diversity Models
radfit.defaultRank - Abundance or Dominance / Diversity Models
radlatticeRank - Abundance or Dominance / Diversity Models
rankindexCompares Dissimilarity Indices for Gradient Detection
rarecurveRarefaction Species Richness
rarefyRarefaction Species Richness
rareslopeRarefaction Species Richness
raupcrickRaup-Crick Dissimilarity with Unequal Sampling Densities of Species
rda[Partial] [Constrained] Correspondence Analysis and Redundancy Analysis
rda.default[Partial] [Constrained] Correspondence Analysis and Redundancy Analysis
rda.formula[Partial] [Constrained] Correspondence Analysis and Redundancy Analysis
read.cepReads a CEP (Canoco) data file
renyiRenyi and Hill Diversities and Corresponding Accumulation Curves
renyiaccumRenyi and Hill Diversities and Corresponding Accumulation Curves
reorder.hclustReorder a Hierarchical Clustering Tree
residuals.ccaPrediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
residuals.procrustesProcrustes Rotation of Two Configurations and PROTEST
rev.hclustReorder a Hierarchical Clustering Tree
rrarefyRarefaction Species Richness
RsquareAdjAdjusted R-square
RsquareAdj.ccaAdjusted R-square
RsquareAdj.defaultAdjusted R-square
RsquareAdj.glmAdjusted R-square
RsquareAdj.lmAdjusted R-square
RsquareAdj.rdaAdjusted R-square
rstandard.ccaLinear Model Diagnostics for Constrained Ordination
rstudent.ccaLinear Model Diagnostics for Constrained Ordination
- -

-- S --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
scoresGet Species or Site Scores from an Ordination
scores.betadisperMultivariate homogeneity of groups dispersions (variances)
scores.betadiverIndices of beta Diversity
scores.ccaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
scores.decoranaDetrended Correspondence Analysis and Basic Reciprocal Averaging
scores.defaultGet Species or Site Scores from an Ordination
scores.envfitFits an Environmental Vector or Factor onto an Ordination
scores.hclustReorder a Hierarchical Clustering Tree
scores.ldaGet Species or Site Scores from an Ordination
scores.metaMDSNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
scores.monoMDSGlobal and Local Non-metric Multidimensional Scaling and Linear and Hybrid Scaling
scores.ordihullDisplay Groups or Factor Levels in Ordination Diagrams
scores.ordiplotAlternative plot and identify Functions for Ordination
scores.orditkplotOrdination Plot with Movable Labels
scores.pcnmPrincipal Coordinates of Neighbourhood Matrix
scores.rdaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
scores.wcmdscaleWeighted Classical (Metric) Multidimensional Scaling
screeplot.ccaScreeplots for Ordination Results and Broken Stick Distributions
screeplot.decoranaScreeplots for Ordination Results and Broken Stick Distributions
screeplot.prcompScreeplots for Ordination Results and Broken Stick Distributions
screeplot.princompScreeplots for Ordination Results and Broken Stick Distributions
showvarpartsPartition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
sigma.ccaLinear Model Diagnostics for Constrained Ordination
simmatNull Model and Simulation
simperSimilarity Percentages
simpleDBRDAPartition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
simpleRDA2Partition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
simulate.capscaleSimulate Responses with Gaussian Error or Permuted Residuals for Constrained Ordination
simulate.ccaSimulate Responses with Gaussian Error or Permuted Residuals for Constrained Ordination
simulate.nullmodelNull Model and Simulation
simulate.rdaSimulate Responses with Gaussian Error or Permuted Residuals for Constrained Ordination
sipooBirds in the Archipelago of Sipoo (Sibbo and Borgå)
sipoo.mapBirds in the Archipelago of Sipoo (Sibbo and Borgå)
smbindNull Model and Simulation
spandepthMinimum Spanning Tree
spantreeMinimum Spanning Tree
specaccumSpecies Accumulation Curves
specnumberEcological Diversity Indices
specpoolExtrapolated Species Richness in a Species Pool
specpool2vectExtrapolated Species Richness in a Species Pool
specslopeSpecies Accumulation Curves
spenvcorDiagnostic Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
sppscoresAdd or Replace Species Scores in Distance-Based Ordination
sppscores<-Add or Replace Species Scores in Distance-Based Ordination
sppscores<-.capscaleAdd or Replace Species Scores in Distance-Based Ordination
sppscores<-.dbrdaAdd or Replace Species Scores in Distance-Based Ordination
sppscores<-.metaMDSAdd or Replace Species Scores in Distance-Based Ordination
SSarrheniusSelf-Starting nls Species-Area Models
SSD.ccaLinear Model Diagnostics for Constrained Ordination
SSgitaySelf-Starting nls Species-Area Models
SSgleasonSelf-Starting nls Species-Area Models
SSlomolinoSelf-Starting nls Species-Area Models
stepacrossStepacross as Flexible Shortest Paths or Extended Dissimilarities
str.nullmodelNull Model and Simulation
stressplotGoodness of Fit and Shepard Plot for Nonmetric Multidimensional Scaling
stressplot.capscaleDisplay Ordination Distances Against Observed Distances in Eigenvector Ordinations
stressplot.ccaDisplay Ordination Distances Against Observed Distances in Eigenvector Ordinations
stressplot.dbrdaDisplay Ordination Distances Against Observed Distances in Eigenvector Ordinations
stressplot.defaultGoodness of Fit and Shepard Plot for Nonmetric Multidimensional Scaling
stressplot.monoMDSGoodness of Fit and Shepard Plot for Nonmetric Multidimensional Scaling
stressplot.prcompDisplay Ordination Distances Against Observed Distances in Eigenvector Ordinations
stressplot.princompDisplay Ordination Distances Against Observed Distances in Eigenvector Ordinations
stressplot.rdaDisplay Ordination Distances Against Observed Distances in Eigenvector Ordinations
stressplot.wcmdscaleDisplay Ordination Distances Against Observed Distances in Eigenvector Ordinations
summary.anosimAnalysis of Similarities
summary.bioenvBest Subset of Environmental Variables with Maximum (Rank) Correlation with Community Dissimilarities
summary.ccaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
summary.clamtestMultinomial Species Classification Method (CLAM)
summary.decoranaDetrended Correspondence Analysis and Basic Reciprocal Averaging
summary.dispweightDispersion-based weighting of species counts
summary.eigenvalsExtract Eigenvalues from an Ordination Object
summary.humpfitNo-interaction Model for Hump-backed Species Richness vs. Biomass
summary.isomapIsometric Feature Mapping Ordination
summary.meandistMulti Response Permutation Procedure and Mean Dissimilarity Matrix
summary.ordiellipseDisplay Groups or Factor Levels in Ordination Diagrams
summary.ordihullDisplay Groups or Factor Levels in Ordination Diagrams
summary.permatMatrix Permutation Algorithms for Presence-Absence and Count Data
summary.permustatsExtract, Analyse and Display Permutation Results
summary.poolaccumExtrapolated Species Richness in a Species Pool
summary.prcPrincipal Response Curves for Treatments with Repeated Observations
summary.procrustesProcrustes Rotation of Two Configurations and PROTEST
summary.radfit.frameRank - Abundance or Dominance / Diversity Models
summary.simperSimilarity Percentages
summary.specaccumSpecies Accumulation Curves
summary.taxondiveIndices of Taxonomic Diversity and Distinctness
swanBeals Smoothing and Degree of Absence
- -

-- T --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
tabascoDisplay Compact Ordered Community Tables
tail.summary.ccaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
taxa2distIndices of Taxonomic Diversity and Distinctness
taxondiveIndices of Taxonomic Diversity and Distinctness
text.ccaPlot or Extract Results of Constrained Correspondence Analysis or Redundancy Analysis
text.decoranaDetrended Correspondence Analysis and Basic Reciprocal Averaging
text.metaMDSNonmetric Multidimensional Scaling with Stable Solution from Random Starts, Axis Scaling and Species Scores
text.monoMDSGlobal and Local Non-metric Multidimensional Scaling and Linear and Hybrid Scaling
text.ordiplotAlternative plot and identify Functions for Ordination
text.orditkplotOrdination Plot with Movable Labels
text.procrustesProcrustes Rotation of Two Configurations and PROTEST
toleranceSpecies tolerances and sample heterogeneities
tolerance.ccaSpecies tolerances and sample heterogeneities
treedistFunctional Diversity and Community Distances from Species Trees
treediveFunctional Diversity and Community Distances from Species Trees
treeheightFunctional Diversity and Community Distances from Species Trees
tsallisTsallis Diversity and Corresponding Accumulation Curves
tsallisaccumTsallis Diversity and Corresponding Accumulation Curves
TukeyHSD.betadisperMultivariate homogeneity of groups dispersions (variances)
- -

-- U --

- - - - -
update.nullmodelNull Model and Simulation
- -

-- V --

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
varechemVegetation and environment in lichen pastures
varespecVegetation and environment in lichen pastures
varpartPartition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
varpart2Partition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
varpart3Partition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
varpart4Partition the Variation of Community Matrix by 2, 3, or 4 Explanatory Matrices
vcov.ccaLinear Model Diagnostics for Constrained Ordination
vectorfitFits an Environmental Vector or Factor onto an Ordination
veganCommunity Ecology Package: Ordination, Diversity and Dissimilarities
vegan-deprecatedDeprecated Functions in vegan package
vegandocsDisplay vegan Documentation
vegdistDissimilarity Indices for Community Ecologists
vegemiteDisplay Compact Ordered Community Tables
veiledspecFit Fisher's Logseries and Preston's Lognormal Model to Abundance Data
vif.ccaDiagnostic Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)
- -

-- W --

- - - - - - - - - - - - - - -
wascoresWeighted Averages Scores for Species
wcmdscaleWeighted Classical (Metric) Multidimensional Scaling
weights.ccaResult Object from Constrained Ordination
weights.decoranaResult Object from Constrained Ordination
weights.rdaResult Object from Constrained Ordination
wisconsinStandardization Methods for Community Ecology
- diff --git a/scTCRpy/rpackages/vegan/html/R.css b/scTCRpy/rpackages/vegan/html/R.css deleted file mode 100644 index f10f5ea66..000000000 --- a/scTCRpy/rpackages/vegan/html/R.css +++ /dev/null @@ -1,97 +0,0 @@ -body { - background: white; - color: black; -} - -a:link { - background: white; - color: blue; -} - -a:visited { - background: white; - color: rgb(50%, 0%, 50%); -} - -h1 { - background: white; - color: rgb(55%, 55%, 55%); - font-family: monospace; - font-size: x-large; - text-align: center; -} - -h2 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-size: large; - text-align: center; -} - -h3 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-size: large; -} - -h4 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-style: italic; - font-size: large; -} - -h5 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; -} - -h6 { - background: white; - color: rgb(40%, 40%, 40%); - font-family: monospace; - font-style: italic; -} - -img.toplogo { - width: 4em; - vertical-align: middle; -} - -img.arrow { - width: 30px; - height: 30px; - border: 0; -} - -span.acronym { - font-size: small; -} - -span.env { - font-family: monospace; -} - -span.file { - font-family: monospace; -} - -span.option{ - font-family: monospace; -} - -span.pkg { - font-weight: bold; -} - -span.samp{ - font-family: monospace; -} - -div.vignettes a:hover { - background: rgb(85%, 85%, 85%); -} diff --git a/scTCRpy/talkR.py b/scTCRpy/talkR.py deleted file mode 100644 index a5fb2d5ae..000000000 --- a/scTCRpy/talkR.py +++ /dev/null @@ -1,262 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - -import subprocess - -from utils import stampTime, tabWriter, tabReader, readORrun, objectContainer, path_to_scripts - -def __main__(): - print 'This module is not intended for direct command line usage. Currently supports import to Python only.' - moduleTest() - return - -def moduleTest(): - return - -def featRchain(experiment): - forced = experiment.force_recompute - verbose = experiment.verbose - fbase = experiment.seqDivFile - - def relax(fn, clonTab=None, verbose= False): - return - - def exportChainFeatures(fn, clonTab, verbose= False): - clonTab = experiment.cellTCR.clonotypeTable - headrow = ['Subtype', 'Clonotype', 'Count', 'nTRA', 'nTRB', 'aTRA', 'aTRB'] - chains = {} - for clone, info in clonTab.iteritems(): - chains[clone] = [clone, str(info['cellNum']), info['chains']['TRA']['primary_cdr3_nt'], info['chains']['TRB']['primary_cdr3_nt'], info['chains']['TRA']['primary_cdr3_aa'], info['chains']['TRB']['primary_cdr3_aa']] - tabWriter(chains, fn, colnames=headrow, tabformatter='dict_of_list', rowname=None, addheader=True) - if verbose: - stampTime('R script input file compiled to ' + fn) - return - - def runDiver(fn, inPut=None, verbose= False): - subprocess.call(['Rscript', path_to_scripts+'chainDiversityCalc.r', inPut, fn]) - diversities = readDiver(fn) - if verbose: - stampTime('R script for chain diversity calculation run, results saved to ' + fn) - return diversities - - def readDiver(fn, inPut=None, verbose= False): - #the order is: nucA, nucB, nucC, aaA, aaB, aaC - def lineParse(data, line, lineNum, firstline, includend, test_only=False): - if test_only: - return locals() - if firstline: - firstline = False - else: - data[line[0]] = line[2:] - return data, firstline, includend - diversities = tabReader(fn, tabformatter=lambda x: {}, lineformatter=lineParse) - if verbose: - stampTime('Read chain diversities from ' + fn) - return diversities - - def runKider(fn, inPut=None, verbose= False): - subprocess.call(['Rscript', path_to_scripts+'kideraCalc.r', inPut, fn]) - kideras = readKider(fn) - if verbose: - stampTime('R script for chain diversity calculation run, results saved to ' + fn) - return kideras - - def readKider(fn, inPut=None, verbose= False): - def lineParse(data, line, lineNum, firstline, includend, test_only=False): - if test_only: - return locals() - if firstline: - firstline = False - else: - d = {} - d['A'] = line[2:12] - d['B'] = line[12:22] - d['C'] = line[22:32] - data[line[0]] = d - return data, firstline, includend - kideras = tabReader(fn, tabformatter=lambda x: {}, lineformatter=lineParse) - if verbose: - stampTime('Read Kidera factors from ' + fn) - return kideras - - def addToCells(diversities, kideras): - for cell, dat in experiment.cellTCR.cellTable.iteritems(): - clonotype = dat['clonotype'] - if clonotype in diversities: - div = diversities[clonotype] - else: - div = ['NA', 'NA','NA', 'NA','NA', 'NA'] - dat['diver'] = div - if clonotype in kideras: - kid = kideras[clonotype] - else: - kid = {'A': ['NA', 'NA','NA', 'NA','NA', 'NA','NA', 'NA','NA', 'NA'], 'B': ['NA', 'NA','NA', 'NA','NA', 'NA','NA', 'NA','NA', 'NA'], 'C': ['NA', 'NA','NA', 'NA','NA', 'NA','NA', 'NA','NA', 'NA']} - dat['kideras'] = kid - return - readORrun(fbase+'_r_input.tsv', forced, relax, exportChainFeatures, None, kwargs={'clonTab': experiment.cellTCR.clonotypeTable, 'verbose': verbose}) - diversities = readORrun(fbase+'_r_out.tsv', forced, readDiver, runDiver, {}, kwargs={'inPut': fbase+'_r_input.tsv', 'verbose': verbose}) - kideras = readORrun(experiment.kiderFile, forced, readKider, runKider, {}, kwargs={'inPut': fbase+'_r_input.tsv', 'verbose': verbose}) - addToCells(diversities, kideras) - return - -def exportCellFeatures(x): - dat = [] - dat.append([ - 'cell_barcode', - 'clonotypeID', - 'clontype_alias', - 'clontype_size', - 'primary_alpha_nt', - 'primary_beta_nt', - 'primary_alpha_aa', - 'primary_beta_aa', - 'secondary_alpha_nt', - 'secondary_beta_nt', - 'secondary_alpha_aa', - 'secondary_beta_aa', - 'primary_alpha_expression', - 'primary_beta_expression', - 'secondary_alpha_expression', - 'secondary_beta_expression', - 'primary_alpha_nt_diversity', - 'primary_beta_nt_diversity', - 'combined_nt_diversity', - 'primary_alpha_aa_diversity', - 'primary_beta_aa_diversity', - 'combined_aa_diversity', - 'KideraA1', - 'KideraA2', - 'KideraA3', - 'KideraA4', - 'KideraA5', - 'KideraA6', - 'KideraA7', - 'KideraA8', - 'KideraA9', - 'KideraA10', - 'KideraB1', - 'KideraB2', - 'KideraB3', - 'KideraB4', - 'KideraB5', - 'KideraB6', - 'KideraB7', - 'KideraB8', - 'KideraB9', - 'KideraB10', - 'KideraC1', - 'KideraC2', - 'KideraC3', - 'KideraC4', - 'KideraC5', - 'KideraC6', - 'KideraC7', - 'KideraC8', - 'KideraC9', - 'KideraC10' - ]) - cells, clonotypes = x.cellTCR.cellTable, x.cellTCR.clonotypeTable - fn = x.cellFeatFile - for cell, info in cells.iteritems(): - row = [cell] - clonotype = clonotypes[info['clonotype']] - row.append(info['clonotype']) - row.append(clonotype['alias']) - row.append(clonotype['cellNum']) - row += [ - clonotype['chains']['TRA']['primary_cdr3_nt'], - clonotype['chains']['TRB']['primary_cdr3_nt'], - clonotype['chains']['TRA']['primary_cdr3_aa'], - clonotype['chains']['TRB']['primary_cdr3_aa'], - clonotype['chains']['TRA']['secondary_cdr3_nt'], - clonotype['chains']['TRB']['secondary_cdr3_nt'], - clonotype['chains']['TRA']['secondary_cdr3_aa'], - clonotype['chains']['TRB']['secondary_cdr3_aa'], - str(info['chain_epression']['TRA']), - str(info['chain_epression']['TRB']), - str(info['chain_epression']['_TRA']), - str(info['chain_epression']['_TRB']) - ] - row += info['diver'] - row += info['kideras']['A'] - row += info['kideras']['B'] - row += info['kideras']['C'] - clonotype['chains']['TRA']['primary_cdr3_nt'] - dat.append(row) - tabWriter(dat, fn, tabformatter='list_of_list') - return - -def groupDiversity(x): - verbose = x.verbose - forced = x.force_recompute - fbase = x.clonDivFile - rFile = fbase+'_r_out.tsv' - inPut = fbase+'_r_input.tsv' - ct_dic = {} - def readDiv(fn, x=x, inPut=inPut, ct_dic=ct_dic, verbose=True): - def lineParse(data, line, lineNum, firstline, includend, test_only=False): - if test_only: - return locals() - if firstline: - firstline = False - else: - data[line[0]] = float(line[1]) - return data, firstline, includend - diversities= tabReader(fn, tabformatter=lambda x: {}, lineformatter=lineParse) - ct_dic = tabReader(inPut+'_recode.txt', tabformat='dict_of_list', evenflatter=True) - return diversities, ct_dic - def runDiv(fn, x=x, inPut=inPut, ct_dic=ct_dic, verbose=True): - clonotype_freqs, clonotype_cells, header = {}, {}, [] - ct_n = 0 - for k, v in x.cellTCR.clonotypeTable.iteritems(): - clonotype_freqs[k] = [] - clonotype_cells[k] = set(v['cells']) - for respect, group in x.cellGroups.iteritems(): - if respect not in ['major clonotypes']: - for k, v in group.iteritems(): - v = set(v) - ct_n += 1 - cat = 'Cat'+str(ct_n) - ct_dic[cat] = k - header.append(respect.replace(' ', '_')+'.'+cat) - for c, d in clonotype_freqs.iteritems(): - d.append(len(v&clonotype_cells[c])) - if respect not in ['samples', 'major clonotypes']: - for sk, sv in x.cellGroups['samples'].iteritems(): - sv = set(sv) - srespect = sk + 'xXxXxXxXxXxXxXxXx' + respect - for k, v in group.iteritems(): - ct_n += 1 - cat = 'Cat'+str(ct_n) - ct_dic[cat] = k - v = set(v)&sv - header.append(srespect.replace(' ', '_')+'.'+cat) - for c, d in clonotype_freqs.iteritems(): - d.append(len(v&clonotype_cells[c])) - tabWriter(clonotype_freqs, inPut, tabformatter='dict_of_list', colnames=header, rowname='CLONOTYPE') - subprocess.call(['Rscript', path_to_scripts+'cloneDiversityCalc.r', inPut, fn]) - if verbose: - stampTime('Clonotype diversity calculations saved to ' + fn) - tabWriter(ct_dic, inPut+'_recode.txt', tabformatter='dict_of_singles') - diversities, nothing = readDiv(fn) - return diversities, ct_dic - diversities, ct_dic = readORrun(rFile, forced, readDiv, runDiv, [{},{}], kwargs={'x': x, 'inPut': inPut, 'ct_dic': ct_dic, 'verbose': verbose}) - groupdiv = {'SAMPLESPLIT': {}} - for k, v in diversities.iteritems(): - sg = groupdiv - if k.find('xXxXxXxXxXxXxXxXx') > 0: - s, k = k.split('xXxXxXxXxXxXxXxXx') - if s not in groupdiv['SAMPLESPLIT']: - groupdiv['SAMPLESPLIT'][s] = {} - sg = groupdiv['SAMPLESPLIT'][s] - k = k.replace('_', ' ') - a, b = k.split('.', 1) - b = ct_dic[b] - if a not in sg: - sg[a] = {} - sg[a][b] = v - x.divGroup=groupdiv - return - -if __name__ == '__main__': - __main__() \ No newline at end of file diff --git a/scTCRpy/talkR.pyc b/scTCRpy/talkR.pyc deleted file mode 100644 index 8e98a6a3b5f3202b139f72d344ff7e95b5fcdc67..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10377 zcmc&)OKe=%dH(Orki$16N-{OE9FOhBW*nKaELnbpk);_~vJr(=4~Z}pYz!{v+@UTx zFW!4cp#lqUf?+bk$vfBKf}m+?lz2 z7)>cRCFkBb&;OkBzyJSC{y(QDy8k%!UR~0^9KNsNiT@NYi;pKwk-M&(lcp!FoZQVx zD=&BR(kjT^g0x2D?ufKT#-MPU*|{hA4Uo7N%} z-!W0Ioy=lhXMHoMC)G|n@xwMou&wJS_i9O}7T2S&o5TY&c<8HgtrK_w2e;lzL^lSM ztSk^o%Q1?wdM}L2t&Z(AgEC6(PErorNzk@|EjKz**@h9;EY~}&mfyDJCKfLDVt*r8 zELVF`6tt7(gL2&Kb~{lLm%|pn%Soqv=RtC>(=K<~%?FDM+)Ofoms@Bxh?51>WcZ3N zpnBn6rxjd?!}dlqs0YpFh4fGtR{fSAqqbT#-3J#Ezqw@=yAP5v%&FB{Kt`>`a!d@1 zH{un&!+0p5OV7^DEDu*6mX5Or!zHKj#6+ohbwZ!S!_(&R#BZSGBauy!Jx>ds?B%qO zlf68*loUvy_fajd{Gmw06PuF{Fmfav@S&%TdHDdtM*FkCnmPFxgB8e!IUO^iV~YJT zU{;LD^}!b91MFl>k`dX(>SHp$>&b3Gc6B8?E}`4psB8kngk0IpiJg=y4@W#Noin9# zKEkw734v6dlLE8T*%^D|T!*J06D;#lPIim`)07F>#o%$-osfq)$RXKv28dj)6P~W``ef&_T#lqa z#rA*2%aMH(WCV$nq|hoB)CR6W!=h{y$pXM3D8JJsiZ*i;(i`us%ex@-2x$$!t12Wev>68jv`n%rGnVgxHhBshyK# z6a*-Epnvqw0{q!i96FC3sB$A+PUDcvirSUldh(#_6j~K1?`Vyvs&=5gu#!l7)m-lv zDq8USEhtnRxghq-y%yF6>FzN&L*H<~*=liN1>YZlW zZ$aHm0SFW=G+N6~sB76^!+KKdG-}N-PIUFC^KA}SMeJL9PZe=o#!ypsx(Ld#+e^xg z5b6>-v5Rj@b+<6a1(e}%0_drPJWJ+NOn?KM4lETto|ZdToJ5AqtJqlXv;!@yM!mqG zRTc=)5VWD)FjOkK2zQ3f8WKPmk}J8%VY|^8D%@bFi}hMi)5`V7QE>_R8E~N3tE9-y zz)yNn5T8YJJcpNfQ{Lm=s8{mtdSAl#Nl!(UBqsx?INU%11_-hML4bThy{Y06*(eBE z3bF$3LJ0yvmb*r#hqz=|4k~74flwXGnbbNE2g)ipLI`*qCJ(B9v%VO`Z=hp9H`+2C z1&fvrE7viu*YuMRh6*NM& zB}Ul3(@Pw&s1N!JhC%EILt4_Z>xPd{7xD$Rtx%ppIVO9E_XNl>AOg2YSyFvjK>#P^<$Rngvy6USLk0w0lF%h<{D69 zD*zUq6D$Ifw&$qbu=10D#!^m7ODuxI1!<77l;veuANWgnM12(11I-Sw9V=#Uq7XPo zitAcG@s%8OOH?pep#C6=8(|bD+QVAdt~YxYj*WA(pc86zP89vervV{zi93E22j@|V zsaOOADB>B<&3WbvsMjgv{YSCI?cUirV*C+5psla$*E;d=X@vjQN2rV;8kK|H4i0FFuX9_9+hb{PQVW_w zp}dCj?_X2UkPS3N7nRS}6t~nNf}^7P0*c}(x;S}Ra%uxwN*hL81H7qG*HAqhV z)~G}kWvc>ON2GmLEvC9KG@bf^UyUyCG-s1lG1U`J-D3gO5A4t!LNnwGDtomH2`T*DwkW;7MP zlu)$N=8Jf_$#iz<+nO?(oQ%*6=ve0{VT8Kru+`=U=Me(n>o=608bI?Lr{j@h={Gt) z@Yh#6Rjd}%OvMyz@t*W1;0;aZ&Zgfz2$1UeT2xa{eldyftCO+yZ9m$GhitpoYu{LV#WpIA4#uh=XnTV>b~Nn5R*7wsU|p^Ca37aX5Goao7Sq zL_NN4LjhX{kG7(*?#F@AF+`7Hn#8DMdz+;JuAKnsLPVt+QbVygHa;70^No7UCkTcl z7jK;Zx!L^Omm%@y2wmJ8@9lZ-X5#b>__{^)X<@NmmQo3c7Gykdbeb?U zFw#jIaO^7w8(tYSAO~V!MF{^Ip4Sn^r`50Sud;V(e{ZJB-mmTN%~aWYb$>6aU_x&7 z4Rm4vwwr0d;`^^Gp{#DyaO>GFn@r0B6-RY?^ zy-cG)kFFy!vA}GmhkNSJb4c?2O!pzM`+E0*-Pc3g=iHX-7d1# zl`GlGE7{7c*~)9G^!tmxnCYryw@}G$p_1J~CA)=6b_IsG>@0=lWDvL!Tpjz5o(Wm`QOX`M6Lu+g3nAE*yO$01OG1IIh&jD z=JfvrMxJtB1{E#6Eg~oWInYb%>>LfX-!R#1$@ug0^yk(0o`<830FQ35Mxn4#Kr)8?dUrbpK25`A$ya&0NI=39mpmCP14-KH`*#lbjf2Q zvhmtTsME5CJCkWn;hn%%QKF|kERojHvxLjYVXXwfYycQoS9P~=(Lxvwu)1DoA?z91 znvo9{~;f;Ja!3)5u{XyXJ;hN5)?^^xDb>e-=OkY8B9t$&<}AXdk!-QD)NnH$4@kYXL)mbDrj7mpqL<(I{TYXH-Vddpp0RSoV_~KNv3I00St+9$KIPj~!o-Ff5E8 zFnBT#y+Rqr4xm67vtap2)jZHhaKIsKygZ(EH-bAabI<*ip35kYSB3v z@0{%XMfSKq8Xos|IpP2%0Dg^VaYvr}e2@Ge-rDvb@XFvYM$}R+c zn9F-`4>r+pf({9;X6G>w`X{|KgE@-6lS9aY>sqw-?`8=SNCL=HGEcClH8A=Jc-AYn zccIx&$=+$%#g%SeR58-gT2{4X{+t|46Wj7Pd280c#EfCOQjeqxp=713@0z|BRYqd` zw55s56>9vQZyos8yAYLcL+9O`BRmRC_4Yf<@2vjOoi|-3p1Dsh8z7gm?@I4^j_vQ~ z=$pLE?Ol9O&oCD2U;4VAnYcXrk*C!QDDyK+Il6lWJ2+i01%8hMXx^fr+w|*!4sHf*bq@_scJPWo-C!yj=QmQnnIG~sftLoB1KQLYQLr5! zXrntCt44G;*)AcMAjl%^w)A~J-BR4e&wSKGZE(-Jxq3?Fx~XzO=roD9+}C{#VPg1? zoTqsx1%&JFd-(m1OX~dy6V#9$D+xtS?yfg~&4uXzi+36>DwBF=p?0U>Ku)220?$(@ zKZ{x!&kV}0^@O%BV#X7Ck2DWQ($J|m5~1XlmFwTQbNkJeJGb9nH9tq)NyJY$<;Q&e zlx@V`0%_n(%rZ)Y%tAl=M+0TZ4vsMjC^bS{KGMgDyUj2$zd%W`h(KBSWPvj0cBa7p zkX6G`oTS-8wQ-II{)WBG|0xV58UUHUWbw8@(G9=KkXhYuDB?kif zE40LPY0@MiC@=w>pP736nVU!smRQC=o%oZWLYk08Y1}gu3jK|bN`aoXmxL~n>!K%j z{ix_9Cfzj}r|Ivn%nD22=j-qAQiRlM{3S=N=1z;&N8m(?;FA04+Ep&E`d})zm>VA% O&yRm|d~N)#Det$e)-!GZ diff --git a/scTCRpy/utils.py b/scTCRpy/utils.py deleted file mode 100644 index 2afafaac8..000000000 --- a/scTCRpy/utils.py +++ /dev/null @@ -1,522 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- - -import os, datetime -import base64, cStringIO - -path_to_scripts = '/home/singlecell/scripts/Tamas/scTCRpy/' - -def fig2hex(f, tag=None, dpi=200): - if tag == None: - tag = '' - my_stringIObytes = cStringIO.StringIO() - f.savefig(my_stringIObytes, format='png', dpi=dpi) - my_stringIObytes.seek(0) - s = base64.b64encode(my_stringIObytes.read()) - return '\n' - -def stampTime(text, before=None): - parttime = datetime.datetime.now() - text += ' ' + parttime.strftime("%H:%M:%S.%f") - if before != None: - text += ' (elapsed time: ' + str(parttime-before) + ')' - print text - return parttime - -def readORrun(fn, forced, ifread, ifrun, iffail, parentDir=None, kwargs={}): - if parentDir != None: - if len(parentDir) > 0: - if parentDir[-1] == '/': - parentDir = parentDir[:-1] - fn = parentDir + '/' + fn - if fn != None: - kwargs['fn'] = fn - if os.path.isfile(fn): - if forced: - return ifrun(**kwargs) - else: - return ifread(**kwargs) - else: - if os.access(os.path.dirname(fn), os.W_OK): - return ifrun(**kwargs) - else: - return iffail - else: - return iffail - -def tabWriter(data, fn, sep='\t', linebr='\n', nan='', rowname='Rowname', rownames=None, colnames=None, header=None, addheader=True, lineformatter=None, tabformatter=None, transposed=False): - def basic_str(l): - return [str(x) for x in l] - data_valid = False - if isinstance(data, dict): - data_valid = True - mainitem = 'dict' - subitem = 'singles' - heads = data.keys() - firstitem = data[heads[0]] - if transposed: - if colnames == None: - colnames = heads - else: - if rownames == None: - rownames = heads - if isinstance(firstitem, dict): - subitem = 'dict' - if transposed: - if rownames == None: - rownames = list(firstitem.keys()) - else: - if colnames == None: - colnames = list(firstitem.keys()) - else: - if isinstance(firstitem, list): - subitem = 'list' - else: - if isinstance(data, list): - data_valid = True - mainitem = 'list' - subitem = 'singles' - firstitem = data[0] - if isinstance(firstitem, dict): - subitem = 'dict' - if transposed: - if rownames == None: - rownames = list(firstitem.keys()) - else: - if colnames == None: - colnames = list(firstitem.keys()) - else: - if isinstance(firstitem, list): - subitem = 'list' - if lineformatter == None: - lineformatter = basic_str - def dict_of_dict(): - o = '' - if addheader: - o += sep.join([rowname]+colnames)+linebr - for row in rownames: - items = [] - for col in colnames: - items.append(data[row][col]) - o += sep.join(lineformatter(items))+linebr - return o - def dict_of_list(): - o = '' - if transposed: - if addheader: - o += sep.join([rowname]+colnames)+linebr - for i in range(0, len(firstitem)): - items = [] - for col in colnames: - items.append(data[col][i]) - o += sep.join(lineformatter(items))+linebr - else: - if addheader: - if colnames != None: - if rowname != None: - o += sep.join([rowname]+colnames)+linebr - else: - o += sep.join(colnames)+linebr - for row in rownames: - o += sep.join(lineformatter([row]+data[row]))+linebr - return o - def dict_of_singles(rownames=rownames): - o = '' - if rownames == None: - rownames = list(data.keys()) - for row in rownames: - o += row + sep + str(data[row]) + linebr - return o - def list_of_dict(): - o = '' - if transposed: - for row in rownames: - items = [] - for col in data: - if row in col: - items.append(col[row]) - else: - items.append(nan) - o += sep.join(lineformatter([row]+items))+linebr - else: - if addheader: - o += sep.join([rowname]+colnames)+linebr - for row in data: - items = [] - for col in colnames: - if col in row: - items.append(row[col]) - else: - items.append(nan) - o += sep.join(lineformatter(items))+linebr - return o - def list_of_list(): - o = '' - if transposed: - N, M = 0, 0 - if colnames != None: - M = len(colnames) - if addheader: - o += sep.join([rowname]+colnames)+linebr - else: - M = len(data) - if rownames != None: - N = len(rownames) - for i in range(0, N): - items = [] - for j in range(0, M): - items.append(data[j][i]) - o += sep.join(lineformatter([rownames[i]]+items))+linebr - else: - N = len(data[0]) - for i in range(0, N): - items = [] - for j in range(0, M): - items.append(data[j][i]) - o += sep.join(lineformatter(items))+linebr - else: - if colnames != None: - if addheader: - o += sep.join([rowname]+colnames)+linebr - if rownames != None: - for i in range(0, len(rownames)): - o += sep.join(lineformatter([rownames[i]]+list(data[i])))+linebr - else: - for row in data: - o += sep.join(lineformatter(row))+linebr - return o - def list_of_singles(): - o = '' - for e in data: - o += str(e) + linebr - return o - method_dict = {'dict_of_dict': dict_of_dict, 'dict_of_list': dict_of_list, 'dict_of_singles': dict_of_singles, 'list_of_dict': list_of_dict, 'list_of_list': list_of_list, 'list_of_singles': list_of_singles} - if tabformatter == None: - if data_valid: - tabformatter = method_dict[mainitem+'_of_'+subitem] - else: - stampTime('Failed saving file '+fn) - return - else: - if isinstance(tabformatter, str): - tabformatter = method_dict[tabformatter] - f = open(fn, 'w') - f.write(tabformatter()) - f.close() - return - -def tabReader(fn, data=None, sep=None, linebr='\n', nan='', add_nan=True, rowname='Rowname', rownames=None, colnames=None, header=None, hasheader=True, lineformatter=None, tabformatter=None, tabformat=None, transposed=False, includelist=[], evenflatter=False, floating=[], namedcols=True, uniquekeys=True, keycol=0): - if sep == None: - ext = fn[-4:] - if ext == '.csv': - sep = ',' - else: - sep = '\t' - def argsetter(locarg, pararg): - ardi = {} - positional = ['data', 'line', 'lineNum', 'firstline', 'includend', 'test_only'] - for k, v in locarg.iteritems(): - if k in pararg: - if k not in positional: - ardi[k] = pararg[k] - return ardi - def dict_of_obj_init(data): - if data == None: - data = namedMatrix() - else: - if isinstance(data, dict): - data = namedMatrix(aggr=data) - return data - def dict_of_obj_parse(data, line, lineNum, firstline, includend, test_only=False, uniquekeys=True, keycol=0, includelist=[], evenflatter=False, floating=[], nan='DROP', add_nan=True, header=None, hasheader=True): - if test_only: - return locals() - if hasheader and firstline: - firstline = False - if header == None: - header = [] - if len(includelist) > 0: - for i in includelist: - header.append(line[i]) - else: - header = line[:keycol]+line[keycol+1:] - data.setColNames(header) - else: - includend, no_nan = True, True - for i in floating: - e = line[i] - try: - e = float(e) - except: - no_nan = False - if nan != 'DROP': - e = nan - else: - e = None - line[i] = e - dat = [] - if len(includelist) > 0: - for i in includelist: - dat.append(line[i]) - else: - dat = line[:keycol]+line[keycol+1:] - if evenflatter: - dat = dat[0] - key = line[keycol] - dat = data.matrixRow(key, dat, data) - if includend: - if no_nan: - data.add(dat) - else: - if add_nan: - data.add(nan) - return data, firstline, includend - def dict_of_dict_init(data): - if data == None: - data = {'DeFaUlT': []} - return data - def dict_of_dict_parse(data, line, lineNum, firstline, includend, test_only=False, uniquekeys=True, keycol=0, includelist=[], evenflatter=False, floating=[], nan='DROP', add_nan=True, header=None, hasheader=True): - if test_only: - return locals() - if hasheader and firstline: - firstline = False - if header == None: - header = [] - if len(includelist) > 0: - for i in includelist: - header.append(line[i]) - else: - header = line[:keycol]+line[keycol+1:] - data['DeFaUlT'] = header - else: - includend, no_nan = True, True - for i in floating: - e = line[i] - try: - e = float(e) - except: - no_nan = False - if nan != 'DROP': - e = nan - else: - e = None - line[i] = e - dat = [] - if len(includelist) > 0: - for i in includelist: - dat.append(line[i]) - else: - dat = line[:keycol]+line[keycol+1:] - if evenflatter: - dat = dat[0] - dat = dict(zip(data['DeFaUlT'], dat)) - key = line[keycol] - if includend: - if no_nan: - if uniquekeys: - data[key] = dat - else: - if key not in data: - data[key] = [] - data[key].append(dat) - else: - if add_nan: - if uniquekeys: - data[key] = dat - else: - if key not in data: - data[key] = [] - data[key].append(dat) - return data, firstline, includend - def dict_of_list_init(data): - if data == None: - data = {} - return data - def dict_of_list_parse(data, line, lineNum, firstline, includend, test_only=False, uniquekeys=True, keycol=0, includelist=[], evenflatter=False, floating=[], nan='DROP', add_nan=True): - if test_only: - return locals() - includend, no_nan = True, True - for i in floating: - e = line[i] - try: - e = float(e) - except: - no_nan = False - if nan != 'DROP': - e = nan - else: - e = None - line[i] = e - dat = [] - if len(includelist) > 0: - for i in includelist: - dat.append(line[i]) - else: - dat = line[:keycol]+line[keycol+1:] - if evenflatter: - dat = dat[0] - key = line[keycol] - if includend: - if no_nan: - if uniquekeys: - data[key] = dat - else: - if key not in data: - data[key] = [] - data[key].append(dat) - else: - if add_nan: - if uniquekeys: - data[key] = dat - else: - if key not in data: - data[key] = [] - data[key].append(dat) - return data, firstline, includend - def list_of_list_init(data): - if data == None: - data = [] - return data - def list_of_list_parse(data, line, lineNum, firstline, includend, evenflatter=evenflatter, test_only=False): - if test_only: - return locals() - if evenflatter: - data.append(line[0]) - else: - data.append(line) - return data, firstline, includend - method_dict = {'dict_of_obj': [dict_of_obj_init, dict_of_obj_parse], - 'dict_of_dict': [dict_of_dict_init, dict_of_dict_parse], - 'dict_of_list': [dict_of_list_init, dict_of_list_parse], - 'list_of_list': [list_of_list_init, list_of_list_parse] - } - init_in_dict, parse_in_dict = True, True - if tabformatter != None: - init_in_dict = False - datinit = tabformatter - if lineformatter != None: - parse_in_dict = False - datparse = lineformatter - if tabformat in method_dict: - if init_in_dict: - datinit = method_dict[tabformat][0] - if parse_in_dict: - datparse = method_dict[tabformat][1] - else: - if init_in_dict: - datinit = list_of_list_init - if parse_in_dict: - datparse = list_of_list_parse - data = datinit(data) - passargs = argsetter(datparse(None, None, None, None, None, test_only=True), locals()) - with open(fn) as f: - firstline, includend = True, False - lineNum = 0 - for line in f: - line = line.split(linebr)[0] - line = line.split('\r')[0] - line = line.replace('"', '') - line = line.split(sep) - data, firstline, includend = datparse(data, line, lineNum, firstline, includend, **passargs) - lineNum += 1 - if isinstance(data, dict): - if 'DeFaUlT' in data: - del data['DeFaUlT'] - return data - -class namedMatrix: - def __init__(self, unique=True, aggr={}): - self.data = aggr - self.order = list(aggr.keys()) - self.colnames = {} - self.N = len(self.colnames) - self.unique = unique - return - - def __getitem__(self, key): - return self.data[key] - - def __len__(self): - return len(self.data) - - def __iter__(self): - for i in range(len(self.order)): - name = self.order[i] - yield i, name, self.data[name] - - def rows(self): - return self.data.keys() - - def setColNames(self, names): - for i in range(0, len(names)): - self.colnames[names[i]] = i - self.N = len(self.colnames) - return - - def add(self, row): - key = row.key - self.data[key] = row - if key not in self.order: - self.order.append(key) - return - - class matrixRow: - def __init__(self, key, data, parent): - N = parent.N - data = data[:N] - if len(data) < N: - for i in range(len(data, N)): - data.append('') - if parent.unique: - self.data = tuple(data) - else: - if key not in parent.data: - self.data = [] - for i in range(0, N): - self.data.append([data[i]]) - else: - self.data = parent.data[key].data - for i in range(0, N): - self.data[i].append(data[i]) - self.key = key - self.keys = parent.colnames - return - - def __getitem__(self, key): - e = self.keys[key] - return self.data[e] - -class objectContainer: - def __init__(self): - self.data = {} - self.order = [] - return - - def __str__(self): - return str(len(self.order)) + 'items' - - def __len__(self): - return len(self.order) - - def __getitem__(self, key): - if isinstance(key, str): - return self.data[key] - else: - if isinstance(key, int): - return self.data[self.order[key]] - else: - return self.data[key.label] - - def __iter__(self): - for i in range(len(self.order)): - name = self.order[i] - yield i, name, self.data[name] - - def __setitem__(self, key, item): - if key not in self.data: - self.order.append(key) - self.data[key] = item - return - - def add(self, sample): - print 'This is a prototype class!!! The "add" function has to be miplemented for individual use cases!' - return \ No newline at end of file diff --git a/scTCRpy/utils.pyc b/scTCRpy/utils.pyc deleted file mode 100644 index 8d34cec47112b1a7c2b5f67d1948c6af20959b48..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 16278 zcmc(mZE##ydB@LPy{;@-vTRwFah&yAtT@32Vw^V<2o_058@XILb?`)NSG!m8+N)jd z-jyxGsswCerxZG!=?m#wni4uK9i|^T17(J3r^8T6KLFo4?F@yPGEDhU+7A6dhqmj|%giDu>6#s^{`7V+ko?f04Jn<4q!>D#XX-*Av!|)N9S^JbnNK; z8Ed;YbL6ps$$r|_yGf=i<*2Ud@-y`sBzo`3y6RdleZ0iE3(`Bbl%qF2eqzq z$Bstje5DXsi&~T`OlnmSh^5$!zctFMocEMCgS$y{@n>QQX%;z??pCGin*uajxZ z^& z80NuR6ap5FKe*ZwfG$^ipEcdV(kzMMtJfms%DG^)3&)xyosp}JoU(d zXC65)bNIk~EIMQ+qf%}ujtZH2aWQ%%V@>b%q!SO|Vx$_QV7YS97z5?zRTzqSRzu08 z(bh_$mAYq7gwRCRWhqyytIGx{Y-FwH7MD&j)+n>bgCs5(4#uJJ-9b-~7ZdboIY!~H zlWFt_X3j&qlM)GDp%_Fy->nkWrrZQdBlUVTdC#o&NX1myghn z!)$)BjfAVFdImAZv?Fvy{K}?QZO)XUvQ<=KQ;elt{jBk(7|$0=k!5rFd=$qfy@g_} zoP*n9dX5RK);4!``Nx@^DqPBe5s@Tx5N#a z`Dqcgm-=1xTdw+}j=6qxT^r{8k*mHIXhTfVYa4nkz%H-}%FBP;+-tKc*((h?_NvXX zw`3H*(3b)c#4j`o+8cW@xWPhS463gNZKAw-T^knqYVgw*>fAN2&(Q9RL1W0hWSV-_ zxvPP@nsQgW-PImgJi@F^^!uOw!cssN5FRicuNh11VSS1I^TB+R|`4*-3S0S0GC&|x*@R`#jg)a;wgYXdzDVW7zbmSRGbtv=~aw?{TB zPcc8ufvZQY!Hm&r+JMcIO^ZEODS9~0EiFam!lcTKk`=SnHLSS7c0F2*tt4M5nbk1h z&J_x00okZ#cAF|8r8gdO=?ud>+R%S z+l^ZJAJGkH1COD+Sdh8}D*=5>0BjUHjg`-PR{Dv0P|4d#CS`8Jhm<_5 zA-t``x@@KKc?SMm-ek12U4NnKs*%o#6La9Zz-I7!pdOc(DZ@!}+b+LZ{@ z{!}lH#bB@}br%A5e~{OnL=>HhOuU{aBk=-C$#^6_sJ(*`{&)smfef+Yu-Z(`-XkDD zMi2)CW*jx7EiaOpaiPeAj8;92?C3Blz)G}S^fe-fZzX3)bw0Wjd*sxrJh4Q4Pi!j_ zi9Jz8#oH&_Fuj{vm>}kdC`!C=(P<1C?e{9ErM75vV2ui1b1S2P$z-_$7atWZre+^7 zT!c3Gu5SRQDuhpe_Ups`s>%%mkQrfHB>gGM(UYbt}5|vgz8NHWh&m zYt+93x+{J?3u~7s==Mvr1!;-@+FYV7+Uxz-)-CaC>z3HUf#%viZw=NkTYUFwC4Dm> z4_lmVYt@Yus;xo>@#S23ohpS>8j{D|HXciHnt1$C4Rnu&Gd%WaJQ!?CZ3*r}ftvX2 zOA?G0KKJvO61|JfCK%oCY8L~X>*sMhP#jdmyU@Vm8B;3*DwShl=8ciqU|w%zgRAqd zJZQa);_B4`R-9yGztl5uD?gNX58NX@2K0FeBFF%0uH4_OV-yJ~#@p}{A6$sv(_y?n z>pAV0L^a+V20;P|v@#VxEj#{6ce;8!rB&EjU!tRxyfs+6lIL!3B}Way<)E-@J3p?Q zen)V+{FM&!el8G6KNo;eNv|7{^m74F)+XcT)XED9d0O-|B0Xf9+#0Ks78noF%{x5n zsqzh$b=a-k=k;%&TZM!jqi}{^w~w)uw^f3iPFp1f^5ZD}Sbc~%amZi^x?^da-n7yC#$g1<`Iy2VRx;IYSq;)Sdb$pz}Yn#l|AbPhy<#XG~ zZ^supk{aQ+&#R6WwA&RtoEmESNurRbW;&>#m)F6Sy;82J*_{$cXy0<$y=@{8GITv$ ziDGRRzD>nwQnQ$yHv&!ac6+EJ`_n|7#QuCqWA$r9wl@^)2*P)f58tEYNhR-9@{E#` zBxW%MTCsNg1mOx|Uj!EkJ7yFS$ry((PRidj2j_`gDUQtVC^BPQ&z190C_>ptr)tYl z_yliln;a^lU}Ufs6?||T=wc1m#&Y=*&da1VGsC<~9b}lj_&lXt+wYN&WL~#-%e};j z_jOxiZm#8i8>EMaRV6=3?t1n@u2d|TE5Dd4mgQXee-8-1rt$Q|yqq(Ai4C zyzfv`8yhE8vtA2MsI2Y7t=o|8RMzLV43D&~o|`)@YSzRDi;lY>jg=Wj1oVF}*lg(_ zGM`RL2ZJ5F?MMv=gFL?7!1ZYG zypFtwctj2DIsPhwj@wc2KHyPl4*GQW>X_5nJ!R3J`z&t6+4radMc5;-lpxF%3kDSU zvO#8CK*OnnXax3$JZYidN_eF_?XC3(!Xo%<*nl#Ey(R;sgsULtzL413T@7U_|3huCdErMtPy{ZhP5EnBs1& zo1fHb#;xvg4Zf!M)6L_GNKr`1>Ru5e31?vKpSLO+Z*r^qtZK+L?l!OR<)D#qwGRip z5&)WjP!mb-J+AhJR`E89_qpm9g7R*s4u2G|sx=LFH;3M9cHitT5thb2t#8fGn3jLJ zN2d8Q5fG@cUmyUFu|~i0`)>7sZ82brxc3Leywyp!a@jQwfTSOCtM|Iby^8r__2^+s zZWa}`uPeewA-=Q2rfD`pvgoH__GzsQ)|hk+eBpzxvEO~7$30I#k_I+ARYYzcWChjj zsne&~$@`u|_34)cd%m11z&c{!IIv$%k>T|n)i0;S0UbJoPt(Q!;uBWfP4;j;z5t_~ zyWhuD^TKo)R6R31@p%&T0*OZE5q3Pdf_-;Fs26(W3amUKYT?U6zp2@W%)vti%#lh~ za-X~;2z0*B)$+DWj;3s!(&N>2vH!9yeMZXe?mFpui2=kGMiW{m}E?ZB$b#_#aQP%XTR$LKGU0eRuHg33Fjwa|H$ zbLSojCRLJLsvb6lHoJb=1!`a&lcR^<-@-!8pEtDIxe1-o-n07| z#Y2Bp0!1pE;mpQITTh~om~&543JHKBwEm0GS-0|#N~UJV75b(FUOw_kNY$V1p2lnc zh?VrYj4Ft%a@f3koUaC+meTSe4H=XVD!|Q8iem&Z@|9WF7+~mUZD>CzUk)yTvS{ZP z)9@`EQzu;z82pVGAfgfM;d=&!TU_Hi!Yra(B1GaF0580Ap0-;hKBA)iS-aDu{jW(wwuLLr?Fd{K+UoID` z%aH;uwhA%?NNvUhTI8JCdvqZx&zG1l38dQOIGLfGA%iKV&FWbGoC>t6v$;5N@!qW> z;cB^JO{Vp(rQ*`rZzkTLzHQNHNjz2h2DZh~_b7~?CK1&1nd75F!>Ih~)I>1Cv0(`3 zrwbRTi*j70)PT8KzJ-n&svqNbFRgontv7(F48tQ6i4N{^uvgwqfRC^qiTY?d6+MxA zwsgt^jaYrhAg7ZAMhaoh7KAMp>4iBX!Sx7pk6gU<2=hrpm{0!SAq;*HH0yH5%OBU3 z4t{P8FC3z83NPITFaOkpmw&l2y!^q<;N_uP!OPBDz)Nl|yr3X>DSnF+f)4Nb04xSj zhC4vaL`o3z&47SF?b8~89w5L!Hf2_!KJkVMKT-@n-? 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Spawns a separate runner for each environment defined in the hatch matrix obtained above. test: - runs-on: ${{ matrix.os }} + needs: get-environments strategy: fail-fast: false matrix: - include: - - os: ubuntu-latest - python: "3.10" - - os: ubuntu-latest - python: "3.12" - - os: ubuntu-latest - python: "3.12" - pip-flags: "--pre" - name: PRE-RELEASE DEPENDENCIES - - name: ${{ matrix.name }} Python ${{ matrix.python }} - - env: - OS: ${{ matrix.os }} - PYTHON: ${{ matrix.python }} + os: [ubuntu-latest] + env: ${{ fromJSON(needs.get-environments.outputs.envs) }} + + name: ${{ matrix.env.label }} + runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 @@ -48,12 +69,31 @@ jobs: - name: Install uv uses: astral-sh/setup-uv@v5 with: + python-version: ${{ matrix.env.python }} cache-dependency-glob: pyproject.toml + - name: create hatch environment + run: uvx hatch env create ${{ matrix.env.name }} - name: run tests using hatch env: MPLBACKEND: agg PLATFORM: ${{ matrix.os }} DISPLAY: :42 - run: uvx hatch test --cover --python ${{ matrix.python }} + run: uvx hatch run ${{ matrix.env.name }}:run-cov + - name: generate coverage report + run: uvx hatch run ${{ matrix.env.name }}:coverage xml - name: Upload coverage uses: codecov/codecov-action@v4 + + # Check that all tests defined above pass. This makes it easy to set a single "required" test in branch + # protection instead of having to update it frequently. See https://github.com/re-actors/alls-green#why. + check: + name: Tests pass in all hatch environments + if: always() + needs: + - get-environments + - test + runs-on: ubuntu-latest + steps: + - uses: re-actors/alls-green@release/v1 + with: + jobs: ${{ toJSON(needs) }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0fcce11e2..680ea850e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: hooks: - id: pyproject-fmt - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.2 + rev: v0.11.6 hooks: - id: ruff types_or: [python, pyi, jupyter] diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index d4bb2cbb9..000000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/contributing.md b/docs/contributing.md index 95920f65a..b83f39811 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -206,7 +206,7 @@ hatch run docs:open ```bash source .venv/bin/activate cd docs -make html +sphinx-build -M html . _build -W (xdg-)open _build/html/index.html ``` diff --git a/pyproject.toml b/pyproject.toml index 505845666..0adc87a40 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,9 +66,26 @@ scripts.build = "sphinx-build -M html docs docs/_build {args}" scripts.open = "python -m webbrowser -t docs/_build/html/index.html" scripts.clean = "git clean -fdX -- {args:docs}" +# Test the lowest and highest supported Python versions with normal deps +[[tool.hatch.envs.hatch-test.matrix]] +deps = [ "stable" ] +python = [ "3.10", "3.13" ] + +# Test the newest supported Python version also with pre-release deps +[[tool.hatch.envs.hatch-test.matrix]] +deps = [ "pre" ] +python = [ "3.13" ] + [tool.hatch.envs.hatch-test] features = [ "test" ] +[tool.hatch.envs.hatch-test.overrides] +# If the matrix variable `deps` is set to "pre", +# set the environment variable `UV_PRERELEASE` to "allow". +matrix.deps.env-vars = [ + { key = "UV_PRERELEASE", value = "allow", if = [ "pre" ] }, +] + [tool.ruff] line-length = 120 src = [ "src" ] From 0d231204a0ce55c4b7a3545b1495015c07c6e1fc Mon Sep 17 00:00:00 2001 From: scverse-bot <108668866+scverse-bot@users.noreply.github.com> Date: Tue, 30 Sep 2025 06:33:38 +0000 Subject: [PATCH 3/9] Automated template update to v0.6.0 --- .cruft.json | 6 +- .github/workflows/test.yaml | 10 ++- .gitignore | 1 + .pre-commit-config.yaml | 19 ++--- .readthedocs.yaml | 23 +++--- biome.jsonc | 5 +- docs/contributing.md | 142 ++++++++++++++++++++++++++++++++---- pyproject.toml | 10 ++- 8 files changed, 165 insertions(+), 51 deletions(-) diff --git a/.cruft.json b/.cruft.json index 9699da957..7701b53dc 100644 --- a/.cruft.json +++ b/.cruft.json @@ -1,7 +1,7 @@ { "template": "https://github.com/scverse/cookiecutter-scverse", - "commit": "d4bdfc5ec4c5029aebf7c0cba65609e20358144d", - "checkout": "test8", + "commit": "d383d94fadff9e4e6fdb59d77c68cb900d7cedec", + "checkout": "v0.6.0", "context": { "cookiecutter": { "project_name": "scirpy", @@ -36,7 +36,7 @@ "trim_blocks": true }, "_template": "https://github.com/scverse/cookiecutter-scverse", - "_commit": "d4bdfc5ec4c5029aebf7c0cba65609e20358144d" + "_commit": "d383d94fadff9e4e6fdb59d77c68cb900d7cedec" } }, "directory": null diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index a4cea89e4..0bd76e8cf 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -78,11 +78,15 @@ jobs: MPLBACKEND: agg PLATFORM: ${{ matrix.os }} DISPLAY: :42 - run: uvx hatch run ${{ matrix.env.name }}:run-cov + run: uvx hatch run ${{ matrix.env.name }}:run-cov -v --color=yes -n auto - name: generate coverage report - run: uvx hatch run ${{ matrix.env.name }}:coverage xml + run: | + # See https://coverage.readthedocs.io/en/latest/config.html#run-patch + test -f .coverage || uvx hatch run ${{ matrix.env.name }}:cov-combine + uvx hatch run ${{ matrix.env.name }}:cov-report # report visibly + uvx hatch run ${{ matrix.env.name }}:coverage xml # create report for upload - name: Upload coverage - uses: codecov/codecov-action@v4 + uses: codecov/codecov-action@v5 # Check that all tests defined above pass. This makes it easy to set a single "required" test in branch # protection instead of having to update it frequently. See https://github.com/re-actors/alls-green#why. diff --git a/.gitignore b/.gitignore index 31e10b3ea..bd24e4e00 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,7 @@ __pycache__/ # Tests and coverage /data/ /node_modules/ +/.coverage* # docs /docs/generated/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 680ea850e..b9de3fe04 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,24 +7,24 @@ default_stages: minimum_pre_commit_version: 2.16.0 repos: - repo: https://github.com/biomejs/pre-commit - rev: v1.9.4 + rev: v2.2.4 hooks: - id: biome-format exclude: ^\.cruft\.json$ # inconsistent indentation with cruft - file never to be modified manually. - repo: https://github.com/tox-dev/pyproject-fmt - rev: v2.5.1 + rev: v2.6.0 hooks: - id: pyproject-fmt - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.6 + rev: v0.13.2 hooks: - - id: ruff + - id: ruff-check types_or: [python, pyi, jupyter] args: [--fix, --exit-non-zero-on-fix] - id: ruff-format types_or: [python, pyi, jupyter] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 + rev: v6.0.0 hooks: - id: detect-private-key - id: check-ast @@ -36,12 +36,3 @@ repos: # Check that there are no merge conflicts (could be generated by template sync) - id: check-merge-conflict args: [--assume-in-merge] - - repo: local - hooks: - - id: forbid-to-commit - name: Don't commit rej files - entry: | - Cannot commit .rej files. These indicate merge conflicts that arise during automated template updates. - Fix the merge conflicts manually and remove the .rej files. - language: fail - files: '.*\.rej$' diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 69897c3b3..c3f3f96fb 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,16 +1,15 @@ # https://docs.readthedocs.io/en/stable/config-file/v2.html version: 2 build: - os: ubuntu-20.04 + os: ubuntu-24.04 tools: - python: "3.10" -sphinx: - configuration: docs/conf.py - # disable this for more lenient docs builds - fail_on_warning: true -python: - install: - - method: pip - path: . - extra_requirements: - - doc + python: "3.12" + jobs: + create_environment: + - asdf plugin add uv + - asdf install uv latest + - asdf global uv latest + build: + html: + - uvx hatch run docs:build + - mv docs/_build $READTHEDOCS_OUTPUT diff --git a/biome.jsonc b/biome.jsonc index 2175c16e6..9f8f2208c 100644 --- a/biome.jsonc +++ b/biome.jsonc @@ -1,9 +1,10 @@ { - "$schema": "https://biomejs.dev/schemas/1.9.4/schema.json", + "$schema": "https://biomejs.dev/schemas/2.2.0/schema.json", + "vcs": { "enabled": true, "clientKind": "git", "useIgnoreFile": true }, "formatter": { "useEditorconfig": true }, "overrides": [ { - "include": ["./.vscode/*.json", "**/*.jsonc"], + "includes": ["./.vscode/*.json", "**/*.jsonc"], "json": { "formatter": { "trailingCommas": "all" }, "parser": { diff --git a/docs/contributing.md b/docs/contributing.md index b83f39811..699d94291 100644 --- a/docs/contributing.md +++ b/docs/contributing.md @@ -1,14 +1,33 @@ # Contributing guide -Scanpy provides extensive [developer documentation][scanpy developer guide], most of which applies to this project, too. -This document will not reproduce the entire content from there. -Instead, it aims at summarizing the most important information to get you started on contributing. - +This document aims at summarizing the most important information for getting you started on contributing to this project. We assume that you are already familiar with git and with making pull requests on GitHub. -If not, please refer to the [scanpy developer guide][]. +For more extensive tutorials, that also cover the absolute basics, +please refer to other resources such as the [pyopensci tutorials][], +the [scientific Python tutorials][], or the [scanpy developer guide][]. + +[pyopensci tutorials]: https://www.pyopensci.org/learn.html +[scientific Python tutorials]: https://learn.scientific-python.org/development/tutorials/ [scanpy developer guide]: https://scanpy.readthedocs.io/en/latest/dev/index.html +:::{tip} The *hatch* project manager + +We highly recommend to familiarize yourself with [`hatch`][hatch]. +Hatch is a Python project manager that + +- manages virtual environments, separately for development, testing and building the documentation. + Separating the environments is useful to avoid dependency conflicts. +- allows to run tests locally in different environments (e.g. different python versions) +- allows to run tasks defined in `pyproject.toml`, e.g. to build documentation. + +While the project is setup with `hatch` in mind, +it is still possible to use different tools to manage dependencies, such as `uv` or `pip`. + +::: + +[hatch]: https://hatch.pypa.io/latest/ + ## Installing dev dependencies In addition to the packages needed to _use_ this package, @@ -16,29 +35,103 @@ you need additional python packages to [run tests](#writing-tests) and [build th :::::{tabs} ::::{group-tab} Hatch -The easiest way is to get familiar with [hatch environments][], with which these tasks are simply: + +On the command line, you typically interact with hatch through its command line interface (CLI). +Running one of the following commands will automatically resolve the environments for testing and +building the documentation in the background: ```bash hatch test # defined in the table [tool.hatch.envs.hatch-test] in pyproject.toml hatch run docs:build # defined in the table [tool.hatch.envs.docs] ``` +When using an IDE such as VS Code, +you’ll have to point the editor at the paths to the virtual environments manually. +The environment you typically want to use as your main development environment is the `hatch-test` +environment with the latest Python version. + +To get a list of all environments for your projects, run + +```bash +hatch env show -i +``` + +This will list “Standalone” environments and a table of “Matrix” environments like the following: + +``` ++------------+---------+--------------------------+----------+---------------------------------+-------------+ +| Name | Type | Envs | Features | Dependencies | Scripts | ++------------+---------+--------------------------+----------+---------------------------------+-------------+ +| hatch-test | virtual | hatch-test.py3.10-stable | dev | coverage-enable-subprocess==1.0 | cov-combine | +| | | hatch-test.py3.13-stable | test | coverage[toml]~=7.4 | cov-report | +| | | hatch-test.py3.13-pre | | pytest-mock~=3.12 | run | +| | | | | pytest-randomly~=3.15 | run-cov | +| | | | | pytest-rerunfailures~=14.0 | | +| | | | | pytest-xdist[psutil]~=3.5 | | +| | | | | pytest~=8.1 | | ++------------+---------+--------------------------+----------+---------------------------------+-------------+ +``` + +From the `Envs` column, select the environment name you want to use for development. +In this example, it would be `hatch-test.py3.13-stable`. + +Next, create the environment with + +```bash +hatch env create hatch-test.py3.13-stable +``` + +Then, obtain the path to the environment using + +```bash +hatch env find hatch-test.py3.13-stable +``` + +In case you are using VScode, now open the command palette (Ctrl+Shift+P) and search for `Python: Select Interpreter`. +Choose `Enter Interpreter Path` and paste the path to the virtual environment from above. + +In this future, this may become easier through a hatch vscode extension. + +:::: + +::::{group-tab} uv + +A popular choice for managing virtual environments is [uv][]. +The main disadvantage compared to hatch is that it supports only a single environment per project at a time, +which requires you to mix the dependencies for running tests and building docs. +This can have undesired side-effects, +such as requiring to install a lower version of a library your project depends on, +only because an outdated sphinx plugin pins an older version. + +To initalize a virtual environment in the `.venv` directory of your project, simply run + +```bash +uv sync --all-extras +``` + +The `.venv` directory is typically automatically discovered by IDEs such as VS Code. + :::: ::::{group-tab} Pip -If you prefer managing environments manually, you can use `pip`: + +Pip is nowadays mostly superseded by environment manager such as [hatch][]. +However, for the sake of completeness, and since it’s ubiquitously available, +we describe how you can manage environments manually using `pip`: ```bash -cd scirpy python3 -m venv .venv source .venv/bin/activate pip install -e ".[dev,test,doc]" ``` +The `.venv` directory is typically automatically discovered by IDEs such as VS Code. + :::: ::::: [hatch environments]: https://hatch.pypa.io/latest/tutorials/environment/basic-usage/ +[uv]: https://docs.astral.sh/uv/ ## Code-style @@ -55,7 +148,7 @@ in the root of the repository. Pre-commit will automatically download all dependencies when it is run for the first time. Alternatively, you can rely on the [pre-commit.ci][] service enabled on GitHub. -If you didn't run `pre-commit` before pushing changes to GitHub it will automatically commit fixes to your pull request, or show an error message. +If you didn’t run `pre-commit` before pushing changes to GitHub it will automatically commit fixes to your pull request, or show an error message. If pre-commit.ci added a commit on a branch you still have been working on locally, simply use @@ -102,6 +195,14 @@ hatch test --all # test with all supported Python versions :::: +::::{group-tab} uv + +```bash +uv run pytest +``` + +:::: + ::::{group-tab} Pip ```bash @@ -118,12 +219,17 @@ in the root of the repository. ### Continuous integration -Continuous integration will automatically run the tests on all pull requests and test +Continuous integration via GitHub actions will automatically run the tests on all pull requests and test against the minimum and maximum supported Python version. -Additionally, there's a CI job that tests against pre-releases of all dependencies (if there are any). +Additionally, there’s a CI job that tests against pre-releases of all dependencies (if there are any). The purpose of this check is to detect incompatibilities of new package versions early on and -gives you time to fix the issue or reach out to the developers of the dependency before the package is released to a wider audience. +gives you time to fix the issue or reach out to the developers of the dependency before the package +is released to a wider audience. + +The CI job is defined in `.github/workflows/test.yaml`, +however the single point of truth for CI jobs is the Hatch test matrix defined in `pyproject.toml`. +This means that local testing via hatch and remote testing on CI tests against the same python versions and uses the same environments. ## Publishing a release @@ -189,7 +295,7 @@ please check out [this feature request][issue-render-notebooks] in the `cookiecu (docs-building)= -#### Building the docs locally +### Building the docs locally :::::{tabs} ::::{group-tab} Hatch @@ -201,6 +307,16 @@ hatch run docs:open :::: +::::{group-tab} uv + +```bash +cd docs +uv run sphinx-build -M html . _build -W +(xdg-)open _build/html/index.html +``` + +:::: + ::::{group-tab} Pip ```bash diff --git a/pyproject.toml b/pyproject.toml index 0adc87a40..1c4a331d5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,7 +39,7 @@ optional-dependencies.doc = [ "pandas", # Until pybtex >0.24.0 releases: https://bitbucket.org/pybtex-devs/pybtex/issues/169/ "setuptools", - "sphinx>=4", + "sphinx>=8.1", "sphinx-autodoc-typehints", "sphinx-book-theme>=1", "sphinx-copybutton", @@ -48,8 +48,9 @@ optional-dependencies.doc = [ "sphinxext-opengraph", ] optional-dependencies.test = [ - "coverage", + "coverage>=7.10", "pytest", + "pytest-cov", # For VS Code’s coverage functionality ] # https://docs.pypi.org/project_metadata/#project-urls urls.Documentation = "https://scirpy.readthedocs.io/" @@ -62,7 +63,7 @@ features = [ "dev" ] [tool.hatch.envs.docs] features = [ "doc" ] -scripts.build = "sphinx-build -M html docs docs/_build {args}" +scripts.build = "sphinx-build -M html docs docs/_build -W {args}" scripts.open = "python -m webbrowser -t docs/_build/html/index.html" scripts.clean = "git clean -fdX -- {args:docs}" @@ -77,7 +78,7 @@ deps = [ "pre" ] python = [ "3.13" ] [tool.hatch.envs.hatch-test] -features = [ "test" ] +features = [ "dev", "test" ] [tool.hatch.envs.hatch-test.overrides] # If the matrix variable `deps` is set to "pre", @@ -135,6 +136,7 @@ addopts = [ [tool.coverage.run] source = [ "scirpy" ] +patch = [ "subprocess" ] omit = [ "**/test_*.py", ] From f5fdd241c9f5566030c3490d45ef1422a9d20168 Mon Sep 17 00:00:00 2001 From: Gregor Sturm Date: Fri, 17 Oct 2025 19:17:33 +0200 Subject: [PATCH 4/9] Pre-commit autoupdate --- .pre-commit-config.yaml | 6 +++--- pyproject.toml | 1 + 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b9de3fe04..1d4aee5b7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,16 +7,16 @@ default_stages: minimum_pre_commit_version: 2.16.0 repos: - repo: https://github.com/biomejs/pre-commit - rev: v2.2.4 + rev: v2.2.6 hooks: - id: biome-format exclude: ^\.cruft\.json$ # inconsistent indentation with cruft - file never to be modified manually. - repo: https://github.com/tox-dev/pyproject-fmt - rev: v2.6.0 + rev: v2.11.0 hooks: - id: pyproject-fmt - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.13.2 + rev: v0.14.1 hooks: - id: ruff-check types_or: [python, pyi, jupyter] diff --git a/pyproject.toml b/pyproject.toml index ccd236d3e..ea2fcb685 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,7 @@ classifiers = [ "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", ] dynamic = [ "version" ] dependencies = [ From cc426005cd742b438e8075ea3be79a828432d590 Mon Sep 17 00:00:00 2001 From: Gregor Sturm Date: Fri, 17 Oct 2025 19:28:48 +0200 Subject: [PATCH 5/9] Add sphinx tabs --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index ea2fcb685..09af8b70c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -67,6 +67,7 @@ optional-dependencies.doc = [ "sphinx-autodoc-typehints", "sphinx-book-theme>=1", "sphinx-copybutton", + "sphinx-tabs", "sphinxcontrib-bibtex>=1", "sphinxext-opengraph", ] From ca2894b10e45c5ec0627ae48ea791263195d53c8 Mon Sep 17 00:00:00 2001 From: Gregor Sturm Date: Fri, 17 Oct 2025 19:30:12 +0200 Subject: [PATCH 6/9] Python 3.14 not compatible with Numba yet --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 09af8b70c..4cb1fe864 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -105,12 +105,12 @@ scripts.clean = "git clean -fdX -- {args:docs}" # Test the lowest and highest supported Python versions with normal deps [[tool.hatch.envs.hatch-test.matrix]] deps = [ "stable" ] -python = [ "3.10", "3.14" ] +python = [ "3.10", "3.13" ] # Test the newest supported Python version also with pre-release deps [[tool.hatch.envs.hatch-test.matrix]] deps = [ "pre" ] -python = [ "3.14" ] +python = [ "3.13" ] [tool.hatch.envs.hatch-test] features = [ "dev", "test" ] From 0d6f67cca4b734d247a3f04d5f8c92201b830e6e Mon Sep 17 00:00:00 2001 From: Gregor Sturm Date: Fri, 17 Oct 2025 19:33:20 +0200 Subject: [PATCH 7/9] Fix docs --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 4cb1fe864..72422cb1e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,6 +70,8 @@ optional-dependencies.doc = [ "sphinx-tabs", "sphinxcontrib-bibtex>=1", "sphinxext-opengraph", + # needed for AnnData type hints + "zarr", ] optional-dependencies.parasail = [ # parasail 1.2.1 fails to be installd on MacOS From 60beeecd05030be80ef905eef896416efe7e55d0 Mon Sep 17 00:00:00 2001 From: Gregor Sturm Date: Sat, 18 Oct 2025 20:19:55 +0200 Subject: [PATCH 8/9] Clean up typing --- docs/conf.py | 1 + pyproject.toml | 4 ++-- src/scirpy/get/__init__.py | 3 +-- src/scirpy/ir_dist/__init__.py | 5 +---- src/scirpy/util/__init__.py | 5 ++--- 5 files changed, 7 insertions(+), 11 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index b2a3cbee5..fddf73a84 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -186,4 +186,5 @@ ("py:meth", "mudata.MuData.update"), ("py:class", "awkward.highlevel.Array"), ("py:class", "logomaker.src.Logo.Logo"), + ("py:data", "typing.Union"), ] diff --git a/pyproject.toml b/pyproject.toml index 72422cb1e..f835f0c01 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,8 +70,8 @@ optional-dependencies.doc = [ "sphinx-tabs", "sphinxcontrib-bibtex>=1", "sphinxext-opengraph", - # needed for AnnData type hints - "zarr", + # TODO needed for AnnData type hints + "zarr<3", ] optional-dependencies.parasail = [ # parasail 1.2.1 fails to be installd on MacOS diff --git a/src/scirpy/get/__init__.py b/src/scirpy/get/__init__.py index 6235ce176..160098587 100644 --- a/src/scirpy/get/__init__.py +++ b/src/scirpy/get/__init__.py @@ -1,8 +1,7 @@ import itertools from collections.abc import Mapping, Sequence from contextlib import contextmanager -from enum import Enum, auto -from typing import Any, Literal, Union, cast, overload +from typing import Any, Literal, cast import awkward as ak import numpy as np diff --git a/src/scirpy/ir_dist/__init__.py b/src/scirpy/ir_dist/__init__.py index 90d22c06b..d5e68fc37 100644 --- a/src/scirpy/ir_dist/__init__.py +++ b/src/scirpy/ir_dist/__init__.py @@ -1,12 +1,9 @@ """Compute distances between immune receptor sequences""" -import itertools from collections.abc import Sequence -from typing import Literal, Optional, Union +from typing import Literal import numpy as np -from anndata import AnnData -from mudata import MuData from scanpy import logging from scipy.sparse import csr_matrix diff --git a/src/scirpy/util/__init__.py b/src/scirpy/util/__init__.py index d9924c60f..3f947bedd 100644 --- a/src/scirpy/util/__init__.py +++ b/src/scirpy/util/__init__.py @@ -1,10 +1,9 @@ -import contextlib import json import os import warnings -from collections.abc import Callable, Mapping, Sequence +from collections.abc import Callable, Sequence from textwrap import dedent -from typing import Any, Literal, Optional, Union, cast, overload +from typing import Any, Union, cast, overload import awkward as ak import numpy as np From 410c01d41ea7c5b85917598a8f86638ac929be60 Mon Sep 17 00:00:00 2001 From: Gregor Sturm Date: Sat, 18 Oct 2025 20:28:51 +0200 Subject: [PATCH 9/9] Update changelog --- CHANGELOG.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 90623fd27..c16a35ae9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,12 @@ and this project adheres to [Semantic Versioning][]. [keep a changelog]: https://keepachangelog.com/en/1.0.0/ [semantic versioning]: https://semver.org/spec/v2.0.0.html +## Unreleased + +### Chore + +- Template update to v0.6.0 + ## v0.22.3 ### Fixes