From 8f9979eca4f26b0e8b28eb22ec1f84e6ff01f6de Mon Sep 17 00:00:00 2001 From: William Fondrie Date: Fri, 19 Mar 2021 15:50:06 -0700 Subject: [PATCH 1/5] Removed unused import --- mokapot/__init__.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/mokapot/__init__.py b/mokapot/__init__.py index 36f6f112..66ee6bc7 100644 --- a/mokapot/__init__.py +++ b/mokapot/__init__.py @@ -1,8 +1,6 @@ """ Initialize the mokapot package. """ -import sys - try: from importlib.metadata import version, PackageNotFoundError From 75bace5956d1847e42fd42d1723d852dd8aeb291 Mon Sep 17 00:00:00 2001 From: William Fondrie Date: Fri, 19 Mar 2021 15:56:23 -0700 Subject: [PATCH 2/5] Ran vignettes --- .../basic_python_api.nbconvert.ipynb | 789 +++++++++++++++ .../vignettes/joint_models.nbconvert.ipynb | 557 ++++++++++ .../percolator_comparison.nbconvert.ipynb | 947 ++++++++++++++++++ 3 files changed, 2293 insertions(+) create mode 100644 docs/source/vignettes/basic_python_api.nbconvert.ipynb create mode 100644 docs/source/vignettes/joint_models.nbconvert.ipynb create mode 100644 docs/source/vignettes/percolator_comparison.nbconvert.ipynb diff --git a/docs/source/vignettes/basic_python_api.nbconvert.ipynb b/docs/source/vignettes/basic_python_api.nbconvert.ipynb new file mode 100644 index 00000000..93587ba8 --- /dev/null +++ b/docs/source/vignettes/basic_python_api.nbconvert.ipynb @@ -0,0 +1,789 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# First steps using mokapot in Python\n", + "\n", + "In this vignette, we'll look at the basics of how to use mokapot as a Python package. We've performed these analyses within a [Jupyter notebook](https://jupyter.org/), which is available using the link at the top of the page.\n", + "\n", + "## Following along locally\n", + "\n", + "To run this notebook, you'll need to have [mokapot](https://mokapot.readthedocs.io/en/latest/#installation) installed. Additionally, you'll need to have a file in the [Percolator tab-delimited format](https://github.com/percolator/percolator/wiki/Interface#tab-delimited-file-format) on hand. The example we'll be using comes from running [tide-search](http://crux.ms/tide-search) on a single phosphoproteomics experiment from: \n", + "\n", + "> Hogrebe, Alexander et al. “Benchmarking common quantification strategies for large-scale phosphoproteomics.” Nature communications vol. 9,1 1045. 13 Mar. 2018, doi:10.1038/s41467-018-03309-6\n", + "\n", + "If you need it, you can download it from the mokapot repository here ([phospho_rep1.pin](https://raw.githubusercontent.com/wfondrie/mokapot/master/data/phospho_rep1.pin)) and set the path to your input file:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:50:59.394184Z", + "iopub.status.busy": "2021-03-19T22:50:59.393330Z", + "iopub.status.idle": "2021-03-19T22:50:59.395466Z", + "shell.execute_reply": "2021-03-19T22:50:59.396356Z" + } + }, + "outputs": [], + "source": [ + "pin_file = \"../../../data/phospho_rep1.pin\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we'll need the FASTA file used for the database search to perform protein-level confidence estimates. Critically, this file must include both target and decoy protein sequences. The correct FASTA file for the above example can also be downloaded from the mokapot repository ([human_sp_td.fasta](https://raw.githubusercontent.com/wfondrie/mokapot/master/data/human_sp_td.fasta)). Once downloaded, you can define the path to your FASTA file to follow along:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:50:59.400329Z", + "iopub.status.busy": "2021-03-19T22:50:59.399701Z", + "iopub.status.idle": "2021-03-19T22:50:59.401358Z", + "shell.execute_reply": "2021-03-19T22:50:59.401936Z" + } + }, + "outputs": [], + "source": [ + "fasta_file = \"../../../data/human_sp_td.fasta\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Setup our Python environment\n", + "\n", + "Before we can perform an anlyses we need to import the Python packages that we'll be using. Additionally, it's a good idea to set the random seed for reproducibility." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:50:59.406095Z", + "iopub.status.busy": "2021-03-19T22:50:59.405372Z", + "iopub.status.idle": "2021-03-19T22:51:02.002010Z", + "shell.execute_reply": "2021-03-19T22:51:02.002680Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import mokapot\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Set the random seed:\n", + "np.random.seed(42)\n", + "\n", + "# Create an output directory\n", + "out_dir = \"basic_python_api_output\"\n", + "os.makedirs(out_dir, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want messages about the mokapot's progress throughout the analysis, then we need to enable it using the `logging` module: " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:02.007095Z", + "iopub.status.busy": "2021-03-19T22:51:02.006356Z", + "iopub.status.idle": "2021-03-19T22:51:02.008212Z", + "shell.execute_reply": "2021-03-19T22:51:02.008837Z" + } + }, + "outputs": [], + "source": [ + "import logging\n", + "\n", + "# Change to True enable messages and nicely format them:\n", + "log = False\n", + "if log:\n", + " logging.basicConfig(\n", + " level=logging.INFO,\n", + " format=\"%(levelname)s: %(message)s\", \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Read the PSMs\n", + "\n", + "We'll now use mokapot to read the PSMs from the provided input file. The [read_pin()](https://mokapot.readthedocs.io/en/latest/api/functions.html#mokapot.read_pin) function returns [LinearPsmDataset](https://mokapot.readthedocs.io/en/latest/api/dataset.html#mokapot.dataset.LinearPsmDataset) object, which stores the PSMs and their associated features for analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:02.012729Z", + "iopub.status.busy": "2021-03-19T22:51:02.012090Z", + "iopub.status.idle": "2021-03-19T22:51:03.181496Z", + "shell.execute_reply": "2021-03-19T22:51:03.182206Z" + } + }, + "outputs": [], + "source": [ + "psms = mokapot.read_pin(pin_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional: Add proteins\n", + "mokapot uses the [picked-protein approach](https://www.mcponline.org/content/14/9/2394.long) to assign accurate protein-level confidence estimates. To do this, you'll need to provide the FASTA file used for your database search (including decoy sequences) and supply the parameters that match the digestion conditions you searched." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:03.186008Z", + "iopub.status.busy": "2021-03-19T22:51:03.185471Z", + "iopub.status.idle": "2021-03-19T22:51:25.274956Z", + "shell.execute_reply": "2021-03-19T22:51:25.276067Z" + } + }, + "outputs": [], + "source": [ + "psms.add_proteins(fasta_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Analyze the PSMs\n", + "\n", + "After that the PSMs have been loaded, we can use the [brew()](https://mokapot.readthedocs.io/en/latest/api/functions.html#mokapot.brew) function to run the analysis and re-score the PSMs. This returns a [LinearConfidence](https://mokapot.readthedocs.io/en/latest/api/confidence.html#mokapot.confidence.LinearConfidence) object, which calculates confidences estimates and stores them." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:25.296676Z", + "iopub.status.busy": "2021-03-19T22:51:25.296127Z", + "iopub.status.idle": "2021-03-19T22:51:38.314190Z", + "shell.execute_reply": "2021-03-19T22:51:38.314883Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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3target_0_41715_3_-1True417154473.83594473.8286K.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGS[79.97]DDEEE...9.9646990.0000536.305117e-16sp|P24534|EF1B_HUMAN
4target_0_48913_5_-1True489135269.57375269.5728R.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT...9.8743740.0000536.305117e-16sp|P68104|EF1A1_HUMAN
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" + ], + "text/plain": [ + " SpecId Label ScanNr ExpMass CalcMass \\\n", + "0 target_0_48845_5_-1 True 48845 5269.5756 5269.5728 \n", + "1 target_0_45243_4_-1 True 45243 3945.8759 3945.8706 \n", + "2 target_0_51371_4_-1 True 51371 4051.1223 4051.1086 \n", + "3 target_0_41715_3_-1 True 41715 4473.8359 4473.8286 \n", + "4 target_0_48913_5_-1 True 48913 5269.5737 5269.5728 \n", + "\n", + " Peptide mokapot score \\\n", + "0 R.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT... 11.215498 \n", + "1 R.CSDAAGYPHATHDLEGPPLDAYSIQGQHTISPLDLAK.L 10.601063 \n", + "2 K.KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR.D 10.550855 \n", + "3 K.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGS[79.97]DDEEE... 9.964699 \n", + "4 R.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT... 9.874374 \n", + "\n", + " mokapot q-value mokapot PEP Proteins \n", + "0 0.000053 6.305117e-16 sp|P68104|EF1A1_HUMAN \n", + "1 0.000053 6.305117e-16 sp|Q15365|PCBP1_HUMAN \n", + "2 0.000053 6.305117e-16 sp|P08670|VIME_HUMAN \n", + "3 0.000053 6.305117e-16 sp|P24534|EF1B_HUMAN \n", + "4 0.000053 6.305117e-16 sp|P68104|EF1A1_HUMAN " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moka_conf, models = mokapot.brew(psms)\n", + "moka_conf.psms.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:38.328500Z", + "iopub.status.busy": "2021-03-19T22:51:38.327724Z", + "iopub.status.idle": "2021-03-19T22:51:38.331337Z", + "shell.execute_reply": "2021-03-19T22:51:38.330741Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SpecIdLabelScanNrExpMassCalcMassPeptidemokapot scoremokapot q-valuemokapot PEPProteins
0target_0_48845_5_-1True488455269.57565269.5728R.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT...11.2154980.0000736.305117e-16sp|P68104|EF1A1_HUMAN
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2target_0_51371_4_-1True513714051.12234051.1086K.KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR.D10.5508550.0000736.305117e-16sp|P08670|VIME_HUMAN
3target_0_41715_3_-1True417154473.83594473.8286K.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGS[79.97]DDEEE...9.9646990.0000736.305117e-16sp|P24534|EF1B_HUMAN
4target_0_31886_3_-1True318863450.76123450.7544R.AAAAVAAAASSCRPLGSGAGPGPTGAAPVSAPAPGPGPAGK.G9.7786720.0000736.305117e-16sp|Q9NRL3|STRN4_HUMAN
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" + ], + "text/plain": [ + " SpecId Label ScanNr ExpMass CalcMass \\\n", + "0 target_0_48845_5_-1 True 48845 5269.5756 5269.5728 \n", + "1 target_0_45243_4_-1 True 45243 3945.8759 3945.8706 \n", + "2 target_0_51371_4_-1 True 51371 4051.1223 4051.1086 \n", + "3 target_0_41715_3_-1 True 41715 4473.8359 4473.8286 \n", + "4 target_0_31886_3_-1 True 31886 3450.7612 3450.7544 \n", + "\n", + " Peptide mokapot score \\\n", + "0 R.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT... 11.215498 \n", + "1 R.CSDAAGYPHATHDLEGPPLDAYSIQGQHTISPLDLAK.L 10.601063 \n", + "2 K.KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR.D 10.550855 \n", + "3 K.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGS[79.97]DDEEE... 9.964699 \n", + "4 R.AAAAVAAAASSCRPLGSGAGPGPTGAAPVSAPAPGPGPAGK.G 9.778672 \n", + "\n", + " mokapot q-value mokapot PEP Proteins \n", + "0 0.000073 6.305117e-16 sp|P68104|EF1A1_HUMAN \n", + "1 0.000073 6.305117e-16 sp|Q15365|PCBP1_HUMAN \n", + "2 0.000073 6.305117e-16 sp|P08670|VIME_HUMAN \n", + "3 0.000073 6.305117e-16 sp|P24534|EF1B_HUMAN \n", + "4 0.000073 6.305117e-16 sp|Q9NRL3|STRN4_HUMAN " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moka_conf.peptides.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:38.342350Z", + "iopub.status.busy": "2021-03-19T22:51:38.341627Z", + "iopub.status.idle": "2021-03-19T22:51:38.344614Z", + "shell.execute_reply": "2021-03-19T22:51:38.345208Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mokapot protein groupbest peptidestripped sequencemokapot scoremokapot q-valuemokapot PEP
0sp|P68104|EF1A1_HUMANR.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT...RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHTAH...11.2154980.0002916.305117e-16
1sp|Q15365|PCBP1_HUMANR.CSDAAGYPHATHDLEGPPLDAYSIQGQHTISPLDLAK.LCSDAAGYPHATHDLEGPPLDAYSIQGQHTISPLDLAK10.6010630.0002916.305117e-16
2sp|P08670|VIME_HUMANK.KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR.DKLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR10.5508550.0002916.305117e-16
3sp|P24534|EF1B_HUMANK.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGS[79.97]DDEEE...ALGKYGPADVEDTTGSGATDSKDDDDIDLFGSDDEEESEEAK9.9646990.0002916.305117e-16
4sp|Q9NRL3|STRN4_HUMANR.AAAAVAAAASSCRPLGSGAGPGPTGAAPVSAPAPGPGPAGK.GAAAAVAAAASSCRPLGSGAGPGPTGAAPVSAPAPGPGPAGK9.7786720.0002916.305117e-16
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" + ], + "text/plain": [ + " mokapot protein group best peptide \\\n", + "0 sp|P68104|EF1A1_HUMAN R.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT... \n", + "1 sp|Q15365|PCBP1_HUMAN R.CSDAAGYPHATHDLEGPPLDAYSIQGQHTISPLDLAK.L \n", + "2 sp|P08670|VIME_HUMAN K.KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR.D \n", + "3 sp|P24534|EF1B_HUMAN K.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGS[79.97]DDEEE... \n", + "4 sp|Q9NRL3|STRN4_HUMAN R.AAAAVAAAASSCRPLGSGAGPGPTGAAPVSAPAPGPGPAGK.G \n", + "\n", + " stripped sequence mokapot score \\\n", + "0 RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHTAH... 11.215498 \n", + "1 CSDAAGYPHATHDLEGPPLDAYSIQGQHTISPLDLAK 10.601063 \n", + "2 KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR 10.550855 \n", + "3 ALGKYGPADVEDTTGSGATDSKDDDDIDLFGSDDEEESEEAK 9.964699 \n", + "4 AAAAVAAAASSCRPLGSGAGPGPTGAAPVSAPAPGPGPAGK 9.778672 \n", + "\n", + " mokapot q-value mokapot PEP \n", + "0 0.000291 6.305117e-16 \n", + "1 0.000291 6.305117e-16 \n", + "2 0.000291 6.305117e-16 \n", + "3 0.000291 6.305117e-16 \n", + "4 0.000291 6.305117e-16 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moka_conf.proteins.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use mokapot assign confidence estimates based on the best original feature---the Tide combined p-value in this case---instead of using the learned scores:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:38.354419Z", + "iopub.status.busy": "2021-03-19T22:51:38.353476Z", + "iopub.status.idle": "2021-03-19T22:51:43.848998Z", + "shell.execute_reply": "2021-03-19T22:51:43.849934Z" + } + }, + "outputs": [], + "source": [ + "tide_conf = psms.assign_confidence()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Plot and save the results\n", + "\n", + "We've included some simple plotting utilities to help assess mokapot's perfomance. Let's see if the mokapot model improves upon the best feature, which is Tide's combined p-value: " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:43.874602Z", + "iopub.status.busy": "2021-03-19T22:51:43.873798Z", + "iopub.status.idle": "2021-03-19T22:51:44.476682Z", + "shell.execute_reply": "2021-03-19T22:51:44.477336Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n", + "colors = (\"#343131\", \"#24B8A0\")\n", + "\n", + "# Plot the performance:\n", + "for ax, level in zip(axs, tide_conf.levels):\n", + " tide_conf.plot_qvalues(level=level, c=colors[0], ax=ax,\n", + " label=\"Tide combined p-value\")\n", + " moka_conf.plot_qvalues(level=level, c=colors[1], ax=ax,\n", + " label=\"mokapot\")\n", + " ax.legend(frameon=False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Excellent. It looks like mokapot increased our power to detect a few hundred more PSMs and peptides at 1% FDR. We figure out the exact improvement by looking at the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:44.483042Z", + "iopub.status.busy": "2021-03-19T22:51:44.482428Z", + "iopub.status.idle": "2021-03-19T22:51:44.487148Z", + "shell.execute_reply": "2021-03-19T22:51:44.487682Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PSMs gained by mokapot: 1149\n", + "Peptides gained by mokapot: 872\n", + "Proteins gained by mokapot: 89\n" + ] + } + ], + "source": [ + "# PSMs\n", + "moka_psms = (moka_conf.psms[\"mokapot q-value\"] <= 0.01).sum()\n", + "tide_psms = (tide_conf.psms[\"mokapot q-value\"] <= 0.01).sum()\n", + "print(f\"PSMs gained by mokapot: {moka_psms - tide_psms}\")\n", + "\n", + "# Peptides\n", + "moka_peps = (moka_conf.peptides[\"mokapot q-value\"] <= 0.01).sum()\n", + "tide_peps = (tide_conf.peptides[\"mokapot q-value\"] <= 0.01).sum()\n", + "print(f\"Peptides gained by mokapot: {moka_peps - tide_peps}\")\n", + "\n", + "# Proteins\n", + "moka_prots = (moka_conf.proteins[\"mokapot q-value\"] <= 0.01).sum()\n", + "tide_prots = (tide_conf.proteins[\"mokapot q-value\"] <= 0.01).sum()\n", + "print(f\"Proteins gained by mokapot: {moka_prots - tide_prots}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we will save the results as tab-delimited text files:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:51:44.492052Z", + "iopub.status.busy": "2021-03-19T22:51:44.491220Z", + "iopub.status.idle": "2021-03-19T22:51:45.204571Z", + "shell.execute_reply": "2021-03-19T22:51:45.205062Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['basic_python_api_output/mokapot.psms.txt',\n", + " 'basic_python_api_output/mokapot.peptides.txt',\n", + " 'basic_python_api_output/mokapot.proteins.txt']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_files = moka_conf.to_txt(dest_dir=out_dir)\n", + "result_files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up\n", + "\n", + "This vignette demonstrated the basic usage of mokapot's Python API. For more detail about any of the mokapot functions and classes that we used, see the [mokapot Python API documentation](https://mokapot.readthedocs.io/en/latest/api/index.html). Finally, check out the other vignettes for examples of advanced usage of mokapot's Python API." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/source/vignettes/joint_models.nbconvert.ipynb b/docs/source/vignettes/joint_models.nbconvert.ipynb new file mode 100644 index 00000000..aa92a040 --- /dev/null +++ b/docs/source/vignettes/joint_models.nbconvert.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Joint modeling of multiple experiments\n", + "\n", + "Often we are interested in comparing the peptides identified and quantified between multiple experimental conditions. In this case, we want to obtain valid confidence estimates for PSMs and peptides in each experiment, such that we can make a statement such as: \"we detected peptide x in sample y.\" To accomplish this using Percolator, we would have two options: 1) analyze each experiment with Percolator independently---that is, run Perolator once on each experiment---or 2) learn a static model from an external training set, then apply the static model to each experiment in separate Percolator runs. The former doesn't require any additional data, but the learned models can vary between experiments (particularly if they are small experiments) and result more missing values between them. Conversly, the latter can result in a more coehisive set of PSM or peptide detections, but requires an external dataset on which to train the model.\n", + "\n", + "In mokapot we've implemented a third strategy: when provided with multiple experiments, we learn a joint model from all of the PSMs then assign confidence estimates for each experiment individually. We'll explore this feature in this vignette using the PSMs assigned by [Tide-search](http://crux.ms/tide-search) for three single-cell proteomics experiments from: \n", + "\n", + "> Specht, Harrison, et al. \"Single-cell mass-spectrometry quantifies the emergence of macrophage heterogeneity.\" bioRxiv (2019): 665307.\n", + "\n", + "In what follows, we'll analyze these experiments individually and using the joint modeling strategy, then compare their performance. We've performed these analyses using mokapot's Python API within a [Jupyter notebook](https://jupyter.org/), which is available using the download link at the top of the page. In this vignette, we assume you're somewhat familiar with the Python API; if not, consider checking out the \"[First steps using mokapot in Python](basic_python_api.ipynb)\" vignette first.\n", + "\n", + "\n", + "## Following along locally\n", + "\n", + "To run this notebook, you'll need to have [mokapot](../index.rst#installation) installed and have the input files saved on your computer in the same directory. You can find these files here: [scope2_FP97AA.pin](https://github.com/wfondrie/mokapot/raw/master/data/scope2_FP97AA.pin), [scope2_FP97AB.pin](https://github.com/wfondrie/mokapot/raw/master/data/scope2_FP97AB.pin), [scope2_FP97AC.pin](https://github.com/wfondrie/mokapot/raw/master/data/scope2_FP97AC.pin)\n", + "\n", + "We can set the path to the input files here:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:52:02.781710Z", + "iopub.status.busy": "2021-03-19T22:52:02.780717Z", + "iopub.status.idle": "2021-03-19T22:52:02.783428Z", + "shell.execute_reply": "2021-03-19T22:52:02.784274Z" + } + }, + "outputs": [], + "source": [ + "pin_dir = \"../../../data\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup our Python environment\n", + "\n", + "Before we can perform the analyses, we need to import the Python packages that we'll be using. Additionally, it's a good idea to set the random seed for reproducibility." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:52:02.790166Z", + "iopub.status.busy": "2021-03-19T22:52:02.789597Z", + "iopub.status.idle": "2021-03-19T22:52:05.464223Z", + "shell.execute_reply": "2021-03-19T22:52:05.464753Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['../../../data/scope2_FP97AA.pin',\n", + " '../../../data/scope2_FP97AB.pin',\n", + " '../../../data/scope2_FP97AC.pin']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "import mokapot\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Set the random seed:\n", + "np.random.seed(42)\n", + "\n", + "# Colors for plotting:\n", + "colors = (\"#343131\", \"#24B8A0\")\n", + "\n", + "# Find the input files:\n", + "pin_files = [os.path.join(pin_dir, f) for f in os.listdir(pin_dir) \n", + " if f.startswith(\"scope2_FP97A\") and f.endswith(\".pin\")]\n", + "\n", + "pin_files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want messages about the mokapot's progress throughout the analyses, then we need to enable it using the `logging` module: " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:52:05.468693Z", + "iopub.status.busy": "2021-03-19T22:52:05.468119Z", + "iopub.status.idle": "2021-03-19T22:52:05.469850Z", + "shell.execute_reply": "2021-03-19T22:52:05.470521Z" + } + }, + "outputs": [], + "source": [ + "import logging\n", + "\n", + "# True enables messages and nicely formats them:\n", + "log = False\n", + "if log:\n", + " logging.basicConfig(\n", + " level=logging.INFO,\n", + " format=\"%(levelname)s: %(message)s\", \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the experiments individually\n", + "\n", + "We'll start by analyzing each of the three experiments individually with mokapot: " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:52:05.475195Z", + "iopub.status.busy": "2021-03-19T22:52:05.474615Z", + "iopub.status.idle": "2021-03-19T22:52:30.881090Z", + "shell.execute_reply": "2021-03-19T22:52:30.881771Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analyzing ../../../data/scope2_FP97AA.pin\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Learned model did not improve over the best feature. Now scoring by the best feature for each collection of PSMs.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analyzing ../../../data/scope2_FP97AB.pin\n", + "Analyzing ../../../data/scope2_FP97AC.pin\n" + ] + } + ], + "source": [ + "# A dictionary to store the results:\n", + "sep_results = {}\n", + "\n", + "# Loop through the input files, analyzing each with mokapot:\n", + "for pin in pin_files:\n", + " # Read PSMs and run mokapot\n", + " print(f\"Analyzing {pin}\")\n", + " psms = mokapot.read_pin(pin)\n", + " results, models = mokapot.brew(psms)\n", + " \n", + " # Add results to our result dictionary:\n", + " rep = os.path.split(pin)[-1].replace(\".pin\", \"\")\n", + " sep_results[rep] = results " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can access the results from the dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:52:30.886221Z", + "iopub.status.busy": "2021-03-19T22:52:30.885549Z", + "iopub.status.idle": "2021-03-19T22:52:30.898532Z", + "shell.execute_reply": "2021-03-19T22:52:30.899119Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SpecIdLabelScanNrExpMassCalcMassPeptidemokapot scoremokapot q-valuemokapot PEPProteins
0target_0_11040_3_-1True110402789.41792789.4084K.LVQDVANNTNEEAGDGTTTATVLAR.S23.5987090.000516.415823e-20sp|P10809|CH60_HUMAN
1target_0_8060_3_-1True80602154.19312154.1860K.GAEAANVTGPGGVPVQGSK.Y21.5337030.000516.705116e-18sp|P67809|YBOX1_HUMAN
2target_0_11114_3_-1True111142618.35542617.3450K.QTTVSNSQQAYQEAFEISK.K20.6239300.000515.199698e-17sp|P31946|1433B_HUMAN
3target_0_12043_3_-1True120432502.29462502.2815K.GVVPLAGTN[0.98]GETTTQGLDGLSER.C19.6260170.000514.917375e-16sp|P04075|ALDOA_HUMAN
4target_0_10221_3_-1True102212424.18622424.1812K.EQQEAIEHIDEVQNEIDR.L18.1784860.000511.279742e-14sp|P0DME0|SETLP_HUMAN\\tsp|Q01105|SET_HUMAN
\n", + "
" + ], + "text/plain": [ + " SpecId Label ScanNr ExpMass CalcMass \\\n", + "0 target_0_11040_3_-1 True 11040 2789.4179 2789.4084 \n", + "1 target_0_8060_3_-1 True 8060 2154.1931 2154.1860 \n", + "2 target_0_11114_3_-1 True 11114 2618.3554 2617.3450 \n", + "3 target_0_12043_3_-1 True 12043 2502.2946 2502.2815 \n", + "4 target_0_10221_3_-1 True 10221 2424.1862 2424.1812 \n", + "\n", + " Peptide mokapot score mokapot q-value \\\n", + "0 K.LVQDVANNTNEEAGDGTTTATVLAR.S 23.598709 0.00051 \n", + "1 K.GAEAANVTGPGGVPVQGSK.Y 21.533703 0.00051 \n", + "2 K.QTTVSNSQQAYQEAFEISK.K 20.623930 0.00051 \n", + "3 K.GVVPLAGTN[0.98]GETTTQGLDGLSER.C 19.626017 0.00051 \n", + "4 K.EQQEAIEHIDEVQNEIDR.L 18.178486 0.00051 \n", + "\n", + " mokapot PEP Proteins \n", + "0 6.415823e-20 sp|P10809|CH60_HUMAN \n", + "1 6.705116e-18 sp|P67809|YBOX1_HUMAN \n", + "2 5.199698e-17 sp|P31946|1433B_HUMAN \n", + "3 4.917375e-16 sp|P04075|ALDOA_HUMAN \n", + "4 1.279742e-14 sp|P0DME0|SETLP_HUMAN\\tsp|Q01105|SET_HUMAN " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sep_results[\"scope2_FP97AA\"].psms.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the experiments with a joint model\n", + "\n", + "Similarly, we'll use the joint modeling strategy to analyze these experiments. Instead of supplying one collection of PSMs (a [LinearPsmDataset](../api/dataset.html#mokapot.dataset.LinearPsmDataset)) to the [brew()](../api/functions.html#mokapot.brew) function, we supply a list of them:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:52:30.903619Z", + "iopub.status.busy": "2021-03-19T22:52:30.902892Z", + "iopub.status.idle": "2021-03-19T22:52:59.998984Z", + "shell.execute_reply": "2021-03-19T22:53:00.000023Z" + } + }, + "outputs": [], + "source": [ + "# A dictionary to store the results:\n", + "joint_results = {}\n", + "\n", + "# Read each input file:\n", + "psms_list = [mokapot.read_pin(f) for f in pin_files]\n", + "\n", + "# Run mokapot on all of the files:\n", + "results, brew = mokapot.brew(psms_list)\n", + " \n", + "# Add results to our result dictionary:\n", + "for pin, result in zip(pin_files, results):\n", + " rep = os.path.split(pin)[-1].replace(\".pin\", \"\")\n", + " joint_results[rep] = result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare Performance\n", + "\n", + "Now that we've finished running our mokapot analyses, we'll compare the results from both methods. First, let's investigate if there any differences in our power to detect PSMs and peptides:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:53:00.033476Z", + "iopub.status.busy": "2021-03-19T22:53:00.030196Z", + "iopub.status.idle": "2021-03-19T22:53:01.138228Z", + "shell.execute_reply": "2021-03-19T22:53:01.138857Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 3, figsize=(10, 6))\n", + "\n", + "# Put experiments in a logical order:\n", + "experiments = list(sep_results.keys())\n", + "experiments.sort()\n", + "\n", + "# Plot the perfomance of each:\n", + "gain = {}\n", + "for col, exp in zip(axs.T, experiments):\n", + " col[0].set_title(exp)\n", + " for ax, level in zip(col, [\"psms\", \"peptides\"]):\n", + " # Plot the number of PSMs accepted at each FDR threshold\n", + " sep_results[exp].plot_qvalues(level=level, ax=ax, c=colors[0],\n", + " label=\"Independent Models\")\n", + " joint_results[exp].plot_qvalues(level=level, ax=ax, c=colors[1],\n", + " label=\"Joint Model\")\n", + " ax.legend(frameon=False)\n", + " \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For these experiments, there appears to be a modest gain in power when the joint modeling strategy is used. This result was expected, because this is a subset of experiments that [benefited from using a static model with Percolator](https://doi.org/10.1021/acs.jproteome.9b00780). \n", + "\n", + "Let's also take a look at the detected peptides that are shared across the three experiments:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:53:01.182469Z", + "iopub.status.busy": "2021-03-19T22:53:01.181836Z", + "iopub.status.idle": "2021-03-19T22:53:01.360259Z", + "shell.execute_reply": "2021-03-19T22:53:01.360790Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def count_reps(res_dict, fdr=0.01):\n", + " \"\"\"\n", + " Count the number of replicates in which each peptide is detected.\n", + " \n", + " Parameters\n", + " ----------\n", + " res_dict : Dict\n", + " A result dictionary from above.\n", + " fdr : float\n", + " The FDR threshold below which to accept peptides.\n", + " \n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " A DataFrame with the counts for the number of peptides\n", + " detected in a number of replicates.\n", + " \"\"\"\n", + " peps = {k: set(p.peptides[\"Peptide\"][p.peptides[\"mokapot q-value\"] <= 0.01])\n", + " for k, p in res_dict.items()}\n", + " \n", + " all_peps = peps[list(peps.keys())[0]].union(*peps.values())\n", + " \n", + " reps = []\n", + " for peptide in all_peps:\n", + " reps.append(sum([peptide in r for r in peps.values()]))\n", + " \n", + " ret = pd.Series(reps).value_counts().reset_index().sort_values(\"index\")\n", + " ret.columns = [\"num_reps\", \"num_peptides\"]\n", + " return ret\n", + "\n", + "sep_reps = count_reps(sep_results)\n", + "joint_reps = count_reps(joint_results)\n", + "\n", + "plt.figure()\n", + "plt.plot(sep_reps[\"num_reps\"].values, sep_reps[\"num_peptides\"], c=colors[0])\n", + "plt.scatter(sep_reps[\"num_reps\"].values, sep_reps[\"num_peptides\"], \n", + " label=\"Independent models\", color=colors[0])\n", + "plt.plot(joint_reps[\"num_reps\"].values, joint_reps[\"num_peptides\"], c=colors[1])\n", + "plt.scatter(joint_reps[\"num_reps\"].values, joint_reps[\"num_peptides\"],\n", + " label=\"Joint model\", c=colors[1])\n", + "\n", + "plt.xticks([1, 2, 3])\n", + "plt.xlabel(\"Number of Replicates Dectected\")\n", + "plt.ylabel(\"Number of Peptides\")\n", + "plt.legend(frameon=False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can see that using the joint modeling approach increases the number of PSMs that are detected in across all three experiments. In effect, this leads to fewer missing values in downstream tasks and can have quite a large impact when many experiments are analyzed together." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up\n", + "\n", + "In this vignette we demonstrated how the joint modeling approach available in mokapot can be valuable when analyzing datasets that consist of multiple experiments. It's worth noting that this is particularly beneficial when the total number of PSMs or the number of high-quality PSMs is small, such as is the case with single-cell proteomics experiments we analyzed. For larger experiments, there is typically little to no difference between analyzing the experiments independently or with a joint model." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/source/vignettes/percolator_comparison.nbconvert.ipynb b/docs/source/vignettes/percolator_comparison.nbconvert.ipynb new file mode 100644 index 00000000..0ae329f3 --- /dev/null +++ b/docs/source/vignettes/percolator_comparison.nbconvert.ipynb @@ -0,0 +1,947 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing mokapot to Percolator from the command line\n", + "\n", + "In this vignette, we will run mokapot and Percolator on the same dataset. Because mokapot is a Python implementation of the Percolator algorithm, we expect them to yield similar results. We've performed these analyses within a [Jupyter notebook](https://jupyter.org/), which is available using the link at the top of the page.\n", + "\n", + "## Following along locally\n", + "\n", + "To run this notebook, you'll need to both have [mokapot](https://mokapot.readthedocs.io/en/latest/#installation) and [Percolator](https://github.com/percolator/percolator/wiki/Download-and-Install) installed. Additionally, you'll need to have a file in the [Percolator tab-delimited format](https://github.com/percolator/percolator/wiki/Interface#tab-delimited-file-format) on hand. The example we'll be using comes from running [tide-search](http://crux.ms/tide-search) on a single phosphoproteomics experiment from: \n", + "\n", + "> Hogrebe, Alexander et al. “Benchmarking common quantification strategies for large-scale phosphoproteomics.” Nature communications vol. 9,1 1045. 13 Mar. 2018, doi:10.1038/s41467-018-03309-6\n", + "\n", + "You can download this from the mokapot repository([phospho_rep1.pin](https://raw.githubusercontent.com/wfondrie/mokapot/master/data/phospho_rep1.pin)) and set the path to your input file:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:54:40.364254Z", + "iopub.status.busy": "2021-03-19T22:54:40.363284Z", + "iopub.status.idle": "2021-03-19T22:54:40.365497Z", + "shell.execute_reply": "2021-03-19T22:54:40.366243Z" + } + }, + "outputs": [], + "source": [ + "pin_file = \"../../../data/phospho_rep1.pin\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we'll need the FASTA file used for the database search to perform protein-level confidence estimates. For the best results, this file should contain both target and decoy protein sequences. The correct FASTA file for the above example can also be downloaded from the mokapot repository ([human_sp_td.fasta](https://raw.githubusercontent.com/wfondrie/mokapot/master/data/human_sp_td.fasta)). Once downloaded, you can define the path to your FASTA file to follow along:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:54:40.370284Z", + "iopub.status.busy": "2021-03-19T22:54:40.369678Z", + "iopub.status.idle": "2021-03-19T22:54:40.371482Z", + "shell.execute_reply": "2021-03-19T22:54:40.372170Z" + } + }, + "outputs": [], + "source": [ + "fasta_file = \"../../../data/human_sp_td.fasta\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing our environment\n", + "\n", + "Before we can proceed to our analyses, we'll need to load a few Python packages that will be used at various places throughout this vignette. All of these packages either come standard with Python or are installed with mokapot." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:54:40.375979Z", + "iopub.status.busy": "2021-03-19T22:54:40.375373Z", + "iopub.status.idle": "2021-03-19T22:54:43.047725Z", + "shell.execute_reply": "2021-03-19T22:54:43.048146Z" + } + }, + "outputs": [], + "source": [ + "import os # For file paths\n", + "import numpy as np # For math\n", + "import pandas as pd # To load and work with the results\n", + "import matplotlib.pyplot as plt # To plot the results\n", + "\n", + "import mokapot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to create output directories for our results:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:54:43.052111Z", + "iopub.status.busy": "2021-03-19T22:54:43.051557Z", + "iopub.status.idle": "2021-03-19T22:54:43.053446Z", + "shell.execute_reply": "2021-03-19T22:54:43.054011Z" + } + }, + "outputs": [], + "source": [ + "out_dir = \"percolator_comparison_output\"\n", + "os.makedirs(out_dir, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze PSMs with Percolator\n", + "\n", + "We'll start by performing a simple analysis with Percolator. This will result in two files, containing the confidence estimates for the PSMs and peptides. \n", + "\n", + "*(Note that a command starting with a `!` is just a way to run terminal commands within a Jupyter notebook. Likewise, {} is used to insert a Python variable into the command.)*" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:54:43.058729Z", + "iopub.status.busy": "2021-03-19T22:54:43.058233Z", + "iopub.status.idle": "2021-03-19T22:55:12.394529Z", + "shell.execute_reply": "2021-03-19T22:55:12.395805Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percolator version 3.05.0, Build Date May 18 2020 08:43:48\r\n", + "Copyright (c) 2006-9 University of Washington. All rights reserved.\r\n", + "Written by Lukas Käll (lukall@u.washington.edu) in the\r\n", + "Department of Genome Sciences at the University of Washington.\r\n", + "Issued command:\r\n", + "percolator ../../../data/phospho_rep1.pin --results-psms percolator_comparison_output/percolator.psms.txt --results-peptides percolator_comparison_output/percolator.peptides.txt --results-proteins percolator_comparison_output/percolator.proteins.txt --picked-protein ../../../data/human_sp_td.fasta --protein-decoy-pattern decoy_ --post-processing-tdc\r\n", + "Started Fri Mar 19 15:54:43 2021\r\n", + "Hyperparameters: selectionFdr=0.01, Cpos=0, Cneg=0, maxNiter=10\r\n", + "Reading tab-delimited input from datafile ../../../data/phospho_rep1.pin\r\n", + "Features:\r\n", + "lnrSp deltLCn deltCn Sp IonFrac RefactoredXCorr NegLog10PValue NegLog10ResEvPValue NegLog10CombinePValue PepLen Charge1 Charge2 Charge3 Charge4 Charge5 enzN enzC enzInt lnNumDSP dM absdM \r\n", + "Found 55398 PSMs\r\n", + "Concatenated search input detected and --post-processing-tdc flag set. Applying target-decoy competition on Percolator scores.\r\n", + "Train/test set contains 42330 positives and 13068 negatives, size ratio=3.23921 and pi0=1\r\n", + "Selecting Cpos by cross-validation.\r\n", + "Selecting Cneg by cross-validation.\r\n", + "Split 1:\tSelected feature 9 as initial direction. Could separate 17728 training set positives with q<0.01 in that direction.\r\n", + "Split 2:\tSelected feature 9 as initial direction. Could separate 17766 training set positives with q<0.01 in that direction.\r\n", + "Split 3:\tSelected feature 9 as initial direction. Could separate 17531 training set positives with q<0.01 in that direction.\r\n", + "Found 26521 test set positives with q<0.01 in initial direction\r\n", + "Reading in data and feature calculation took 0.680899 cpu seconds or 0 seconds wall clock time.\r\n", + "---Training with Cpos selected by cross validation, Cneg selected by cross validation, initial_fdr=0.01, fdr=0.01\r\n", + "Iteration 1:\tEstimated 27518 PSMs with q<0.01\r\n", + "Iteration 2:\tEstimated 27660 PSMs with q<0.01\r\n", + "Iteration 3:\tEstimated 27677 PSMs with q<0.01\r\n", + "Iteration 4:\tEstimated 27707 PSMs with q<0.01\r\n", + "Iteration 5:\tEstimated 27718 PSMs with q<0.01\r\n", + "Iteration 6:\tEstimated 27721 PSMs with q<0.01\r\n", + "Iteration 7:\tEstimated 27714 PSMs with q<0.01\r\n", + "Iteration 8:\tEstimated 27714 PSMs with q<0.01\r\n", + "Iteration 9:\tEstimated 27715 PSMs with q<0.01\r\n", + "Iteration 10:\tEstimated 27709 PSMs with q<0.01\r\n", + "Learned normalized SVM weights for the 3 cross-validation splits:\r\n", + " Split1\t Split2\t Split3\tFeatureName\r\n", + " 0.0230\t 0.0303\t 0.0285\tlnrSp\r\n", + " 0.0000\t 0.0000\t 0.0000\tdeltLCn\r\n", + " 0.2773\t 0.3682\t 0.4160\tdeltCn\r\n", + " 0.2671\t 0.2547\t 0.5801\tSp\r\n", + "-0.1505\t-0.1877\t-0.2711\tIonFrac\r\n", + "-0.3445\t-0.1024\t-0.4075\tRefactoredXCorr\r\n", + " 0.8339\t 0.5093\t 0.7080\tNegLog10PValue\r\n", + " 2.3228\t 2.2627\t 2.6138\tNegLog10ResEvPValue\r\n", + " 1.0476\t 1.5876\t 1.5223\tNegLog10CombinePValue\r\n", + " 0.0289\t-0.0185\t-0.1326\tPepLen\r\n", + " 0.0000\t 0.0000\t 0.0000\tCharge1\r\n", + " 0.1095\t 0.1721\t 0.1667\tCharge2\r\n", + " 0.0701\t 0.0386\t 0.0894\tCharge3\r\n", + "-0.1165\t-0.1162\t-0.1152\tCharge4\r\n", + "-0.1195\t-0.1644\t-0.2447\tCharge5\r\n", + " 0.2008\t 0.1115\t 0.2601\tenzN\r\n", + " 0.2416\t 0.2924\t 0.3273\tenzC\r\n", + "-0.2223\t-0.2130\t-0.2619\tenzInt\r\n", + "-0.0732\t-0.0530\t-0.0550\tlnNumDSP\r\n", + "-0.4213\t-0.4887\t-0.3853\tdM\r\n", + "-1.2402\t-1.5170\t-1.4263\tabsdM\r\n", + " 0.8314\t 1.2400\t 1.4099\tm0\r\n", + "Found 27605 test set PSMs with q<0.01.\r\n", + "Selected best-scoring PSM per scan+expMass (target-decoy competition): 42330 target PSMs and 13068 decoy PSMs.\r\n", + "Tossing out \"redundant\" PSMs keeping only the best scoring PSM for each unique peptide.\r\n", + "Calculating q values.\r\n", + "Final list yields 19731 target peptides with q<0.01.\r\n", + "Calculating posterior error probabilities (PEPs).\r\n", + "Processing took 30.06 cpu seconds or 13 seconds wall clock time.\r\n", + "\r\n", + "Calculating protein level probabilities.\r\n", + "Miscleavage detected: K.KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR.D\r\n", + "Miscleavage detected: K.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGSDDEEESEEAK.R\r\n", + "Non enzymatic flank detected: R.KGAGDGSDEEVDGKADGAEAK.P\r\n", + "Non enzymatic flank detected: R.PAAAAAPALAAAAAPFPSAGPAAQVAAAPPAAAASAPHGVAAAPK.P\r\n", + "Detected TrypsinP as protease instead of Trypsin, allowing (R|K).P cleavages.\r\n", + "Protein digestion parameters for duplicate/fragment detection (detected from PSM input):\r\n", + " enzyme=trypsinp, digestion=full, min-pept-length=6, max-pept-length=50, max-miscleavages=2\r\n", + "Detecting protein fragments/duplicates in target database\r\n", + "Decoy proteins detected in fasta database, no need to generate decoy database\r\n", + "Initialized protein inference engine.\r\n", + "Computing protein probabilities.\r\n", + "Performing picked protein strategy\r\n", + "Eliminated lower-scoring target-decoy protein: 7802 target proteins and 3991 decoy proteins remaining.\r\n", + "Computing protein statistics.\r\n", + "Number of protein groups identified at q-value = 0.01: 3645\r\n", + "Estimating protein probabilities took : 15.83 cpu seconds or 16 seconds wall clock time.\r\n" + ] + } + ], + "source": [ + "!percolator {pin_file} \\\n", + " --results-psms {out_dir}/percolator.psms.txt \\\n", + " --results-peptides {out_dir}/percolator.peptides.txt \\\n", + " --results-proteins {out_dir}/percolator.proteins.txt \\\n", + " --picked-protein {fasta_file} \\\n", + " --protein-decoy-pattern decoy_ \\\n", + " --post-processing-tdc # Use target decoy competition instead of mix-max." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze PSMs with mokapot\n", + "\n", + "Now let's analyze the same dataset using mokapot. When you use mokapot from the command line, the underlying models attempt to replicate the linear support vector machine (SVM) models that Percolator uses." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:55:12.405156Z", + "iopub.status.busy": "2021-03-19T22:55:12.404458Z", + "iopub.status.idle": "2021-03-19T22:55:55.729930Z", + "shell.execute_reply": "2021-03-19T22:55:55.731767Z" + }, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] mokapot version 0.6.2.dev11+g2b616fb.d20210317\r\n", + "[INFO] Written by William E. Fondrie (wfondrie@uw.edu) in the\r\n", + "[INFO] Department of Genome Sciences at the University of Washington.\r\n", + "[INFO] Command issued:\r\n", + "[INFO] /usr/local/anaconda3/bin/mokapot ../../../data/phospho_rep1.pin --dest_dir percolator_comparison_output --proteins ../../../data/human_sp_td.fasta\r\n", + "[INFO] \r\n", + "[INFO] Starting Analysis\r\n", + "[INFO] =================\r\n", + "[INFO] Parsing PSMs...\r\n", + "[INFO] Reading ../../../data/phospho_rep1.pin...\r\n", + "[INFO] NumExpr defaulting to 8 threads.\r\n", + "[INFO] Using 21 features:\r\n", + "[INFO] (1)\tlnrSp\r\n", + "[INFO] (2)\tdeltLCn\r\n", + "[INFO] (3)\tdeltCn\r\n", + "[INFO] (4)\tSp\r\n", + "[INFO] (5)\tIonFrac\r\n", + "[INFO] (6)\tRefactoredXCorr\r\n", + "[INFO] (7)\tNegLog10PValue\r\n", + "[INFO] (8)\tNegLog10ResEvPValue\r\n", + "[INFO] (9)\tNegLog10CombinePValue\r\n", + "[INFO] (10)\tPepLen\r\n", + "[INFO] (11)\tCharge1\r\n", + "[INFO] (12)\tCharge2\r\n", + "[INFO] (13)\tCharge3\r\n", + "[INFO] (14)\tCharge4\r\n", + "[INFO] (15)\tCharge5\r\n", + "[INFO] (16)\tenzN\r\n", + "[INFO] (17)\tenzC\r\n", + "[INFO] (18)\tenzInt\r\n", + "[INFO] (19)\tlnNumDSP\r\n", + "[INFO] (20)\tdM\r\n", + "[INFO] (21)\tabsdM\r\n", + "[INFO] Found 55398 PSMs.\r\n", + "[INFO] - 42330 target PSMs and 13068 decoy PSMs detected.\r\n", + "[INFO] Protein-level confidence estimates enabled.\r\n", + "[INFO] Parsing FASTA files and digesting proteins...\r\n", + "[INFO] - Parsed and digested 40832 proteins.\r\n", + "[INFO] - 22 had no peptides.\r\n", + "[INFO] - Retained 40810 proteins.\r\n", + "[INFO] Matching target to decoy proteins...\r\n", + "[INFO] Building protein groups...\r\n", + "[INFO] \t- Aggregated 40810 proteins into 40645 protein groups.\r\n", + "[INFO] Discarding shared peptides...\r\n", + "[INFO] - Discarded 202585 peptides and 168 proteins groups.\r\n", + "[INFO] - Retained 5091124 peptides from 40477 protein groups.\r\n", + "[INFO] Splitting PSMs into 3 folds...\r\n", + "[INFO] \r\n", + "[INFO] === Analyzing Fold 1 ===\r\n", + "[INFO] Finding initial direction...\r\n", + "[INFO] \t- Selected feature NegLog10CombinePValue with 17673 PSMs at q<=0.01.\r\n", + "[INFO] Selecting hyperparameters...\r\n", + "[INFO] \t- class_weight = {0: 1, 1: 1}\r\n", + "[INFO] Beginning training loop...\r\n", + "[INFO] \t- Iteration 0: 18132 training PSMs passed.\r\n", + "[INFO] \t- Iteration 1: 18351 training PSMs passed.\r\n", + "[INFO] \t- Iteration 2: 18396 training PSMs passed.\r\n", + "[INFO] \t- Iteration 3: 18447 training PSMs passed.\r\n", + "[INFO] \t- Iteration 4: 18429 training PSMs passed.\r\n", + "[INFO] \t- Iteration 5: 18443 training PSMs passed.\r\n", + "[INFO] \t- Iteration 6: 18436 training PSMs passed.\r\n", + "[INFO] \t- Iteration 7: 18451 training PSMs passed.\r\n", + "[INFO] \t- Iteration 8: 18438 training PSMs passed.\r\n", + "[INFO] \t- Iteration 9: 18436 training PSMs passed.\r\n", + "[INFO] Normalized feature weights in the learned model:\r\n", + "[INFO] Feature Weight\r\n", + "[INFO] lnrSp 0.026102189410187042\r\n", + "[INFO] deltLCn 0.0\r\n", + "[INFO] deltCn 0.4615984367352801\r\n", + "[INFO] Sp 0.43149511937023693\r\n", + "[INFO] IonFrac -0.2729756431733407\r\n", + "[INFO] RefactoredXCorr -0.30759469103483916\r\n", + "[INFO] NegLog10PValue 0.6177255196402924\r\n", + "[INFO] NegLog10ResEvPValue 2.9612131827339736\r\n", + "[INFO] NegLog10CombinePValue 2.0775269640351217\r\n", + "[INFO] PepLen -0.09018949332383376\r\n", + "[INFO] Charge1 0.0\r\n", + "[INFO] Charge2 0.2096066909859259\r\n", + "[INFO] Charge3 0.11111488281703338\r\n", + "[INFO] Charge4 -0.19831407324148603\r\n", + "[INFO] Charge5 -0.22598334079489468\r\n", + "[INFO] enzN 0.3103311846022322\r\n", + "[INFO] enzC 0.4078605926031055\r\n", + "[INFO] enzInt -0.25714857509810163\r\n", + "[INFO] lnNumDSP -0.07292075070788986\r\n", + "[INFO] dM -0.6359300207952723\r\n", + "[INFO] absdM -1.7916283491512663\r\n", + "[INFO] intercept 1.9398880003517747\r\n", + "[INFO] Done training.\r\n", + "[INFO] \r\n", + "[INFO] === Analyzing Fold 2 ===\r\n", + "[INFO] Finding initial direction...\r\n", + "[INFO] \t- Selected feature NegLog10CombinePValue with 17707 PSMs at q<=0.01.\r\n", + "[INFO] Selecting hyperparameters...\r\n", + "[INFO] \t- class_weight = {0: 1, 1: 1}\r\n", + "[INFO] Beginning training loop...\r\n", + "[INFO] \t- Iteration 0: 18165 training PSMs passed.\r\n", + "[INFO] \t- Iteration 1: 18370 training PSMs passed.\r\n", + "[INFO] \t- Iteration 2: 18446 training PSMs passed.\r\n", + "[INFO] \t- Iteration 3: 18476 training PSMs passed.\r\n", + "[INFO] \t- Iteration 4: 18471 training PSMs passed.\r\n", + "[INFO] \t- Iteration 5: 18496 training PSMs passed.\r\n", + "[INFO] \t- Iteration 6: 18502 training PSMs passed.\r\n", + "[INFO] \t- Iteration 7: 18511 training PSMs passed.\r\n", + "[INFO] \t- Iteration 8: 18523 training PSMs passed.\r\n", + "[INFO] \t- Iteration 9: 18532 training PSMs passed.\r\n", + "[INFO] Normalized feature weights in the learned model:\r\n", + "[INFO] Feature Weight\r\n", + "[INFO] lnrSp 0.007384349279428045\r\n", + "[INFO] deltLCn 0.0\r\n", + "[INFO] deltCn 0.24872162586281005\r\n", + "[INFO] Sp 0.37756464838637993\r\n", + "[INFO] IonFrac -0.14967204410811955\r\n", + "[INFO] RefactoredXCorr -0.5045470442001148\r\n", + "[INFO] NegLog10PValue 0.9108618736288966\r\n", + "[INFO] NegLog10ResEvPValue 2.6887393228215326\r\n", + "[INFO] NegLog10CombinePValue 2.0167415885508326\r\n", + "[INFO] PepLen -0.024070568525301175\r\n", + "[INFO] Charge1 0.0\r\n", + "[INFO] Charge2 0.13056572416734646\r\n", + "[INFO] Charge3 0.09651058142738499\r\n", + "[INFO] Charge4 -0.12875938491608133\r\n", + "[INFO] Charge5 -0.1789075735867103\r\n", + "[INFO] enzN 0.2909471215490557\r\n", + "[INFO] enzC 0.41598085797013623\r\n", + "[INFO] enzInt -0.28326866240161114\r\n", + "[INFO] lnNumDSP -0.07067476894046408\r\n", + "[INFO] dM -0.4931824784639919\r\n", + "[INFO] absdM -1.6527739725518216\r\n", + "[INFO] intercept 1.8727550327779863\r\n", + "[INFO] Done training.\r\n", + "[INFO] \r\n", + "[INFO] === Analyzing Fold 3 ===\r\n", + "[INFO] Finding initial direction...\r\n", + "[INFO] \t- Selected feature NegLog10CombinePValue with 17628 PSMs at q<=0.01.\r\n", + "[INFO] Selecting hyperparameters...\r\n", + "[INFO] \t- class_weight = {0: 1, 1: 1}\r\n", + "[INFO] Beginning training loop...\r\n", + "[INFO] \t- Iteration 0: 18126 training PSMs passed.\r\n", + "[INFO] \t- Iteration 1: 18338 training PSMs passed.\r\n", + "[INFO] \t- Iteration 2: 18431 training PSMs passed.\r\n", + "[INFO] \t- Iteration 3: 18471 training PSMs passed.\r\n", + "[INFO] \t- Iteration 4: 18454 training PSMs passed.\r\n", + "[INFO] \t- Iteration 5: 18452 training PSMs passed.\r\n", + "[INFO] \t- Iteration 6: 18438 training PSMs passed.\r\n", + "[INFO] \t- Iteration 7: 18433 training PSMs passed.\r\n", + "[INFO] \t- Iteration 8: 18429 training PSMs passed.\r\n", + "[INFO] \t- Iteration 9: 18427 training PSMs passed.\r\n", + "[INFO] Normalized feature weights in the learned model:\r\n", + "[INFO] Feature Weight\r\n", + "[INFO] lnrSp 0.07693186163379205\r\n", + "[INFO] deltLCn 0.0\r\n", + "[INFO] deltCn 0.46241238977005156\r\n", + "[INFO] Sp 0.5532094956546141\r\n", + "[INFO] IonFrac -0.30107155104415195\r\n", + "[INFO] RefactoredXCorr -0.44580226591561223\r\n", + "[INFO] NegLog10PValue 0.8132838471004196\r\n", + "[INFO] NegLog10ResEvPValue 2.7776618185164543\r\n", + "[INFO] NegLog10CombinePValue 2.0402250192827256\r\n", + "[INFO] PepLen -0.030481554940090305\r\n", + "[INFO] Charge1 0.0\r\n", + "[INFO] Charge2 0.22844421838895207\r\n", + "[INFO] Charge3 0.11503392863519651\r\n", + "[INFO] Charge4 -0.15327578600535563\r\n", + "[INFO] Charge5 -0.3310755441364631\r\n", + "[INFO] enzN 0.2994391497311681\r\n", + "[INFO] enzC 0.3494399489666935\r\n", + "[INFO] enzInt -0.2811971817493915\r\n", + "[INFO] lnNumDSP -0.059135455154673\r\n", + "[INFO] dM -0.6383259141223344\r\n", + "[INFO] absdM -1.80586955512272\r\n", + "[INFO] intercept 1.8686048209648667\r\n", + "[INFO] Done training.\r\n", + "[INFO] \r\n", + "[INFO] Assigning confidence...\r\n", + "[INFO] Performing target-decoy competition...\r\n", + "[INFO] Keeping the best match per ScanNr+ExpMass columns...\r\n", + "[INFO] \t- Found 55398 PSMs from unique spectra.\r\n", + "[INFO] \t- Found 46201 unique peptides.\r\n", + "[INFO] \t- Found 11794 unique protein groups.\r\n", + "[INFO] Assiging q-values to PSMs...\r\n", + "[INFO] \t- Found 27645 PSMs with q<=0.01\r\n", + "[INFO] Assiging PEPs to PSMs...\r\n", + "[INFO] Assiging q-values to peptides...\r\n", + "[INFO] \t- Found 19726 peptides with q<=0.01\r\n", + "[INFO] Assiging PEPs to peptides...\r\n", + "[INFO] Assiging q-values to proteins...\r\n", + "[INFO] \t- Found 3645 proteins with q<=0.01\r\n", + "[INFO] Assiging PEPs to proteins...\r\n", + "[INFO] Writing results...\r\n", + "[INFO] \r\n", + "[INFO] === DONE! ===\r\n", + "[INFO] mokapot analysis completed in 0:00:39\r\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!mokapot {pin_file} --dest_dir {out_dir} --proteins {fasta_file}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare the mokapot and Percolator results\n", + "\n", + "Now that we've analyzed the PSMs using both mokapot and Percolator, we can plot the scores and confidence estimates to see how well they agree.\n", + "\n", + "Let's start by comparing them at the PSM level. First we need to parse the result files for mokapot and Percolator, then combine them into one table:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:55:55.745496Z", + "iopub.status.busy": "2021-03-19T22:55:55.744205Z", + "iopub.status.idle": "2021-03-19T22:55:56.092620Z", + "shell.execute_reply": "2021-03-19T22:55:56.093147Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SpecIdLabelScanNrExpMassCalcMassPeptidemokapot scoremokapot q-valuemokapot PEPProteinspercolator scorepercolator q-valuepercolator PEP
0target_0_51371_4_-1True513714051.12234051.1086K.KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR.D11.2694860.0000566.305117e-16sp|P08670|VIME_HUMAN10.456700.0000526.305120e-16
1target_0_48845_5_-1True488455269.57565269.5728R.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT...11.1938310.0000566.305117e-16sp|P68104|EF1A1_HUMAN10.373900.0000526.305120e-16
2target_0_41715_3_-1True417154473.83594473.8286K.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGS[79.97]DDEEE...10.6284030.0000566.305117e-16sp|P24534|EF1B_HUMAN10.446100.0000526.305120e-16
3target_0_52110_3_-1True521103311.53443311.5339K.EAESCDCLQGFQLTHSLGGGTGSGMGTLLLSK.I10.1704870.0000566.305117e-16sp|Q3ZCM7|TBB8_HUMAN9.895940.0000526.305120e-16
4target_0_22781_3_-1True227813774.42963773.4241R.TPQRGDEEGLGGEEEEEEEEEEEDDS[79.97]AEEGGAAR.L9.9383600.0000566.305117e-16sp|Q9Y2K7|KDM2A_HUMAN9.752660.0000526.305120e-16
\n", + "
" + ], + "text/plain": [ + " SpecId Label ScanNr ExpMass CalcMass \\\n", + "0 target_0_51371_4_-1 True 51371 4051.1223 4051.1086 \n", + "1 target_0_48845_5_-1 True 48845 5269.5756 5269.5728 \n", + "2 target_0_41715_3_-1 True 41715 4473.8359 4473.8286 \n", + "3 target_0_52110_3_-1 True 52110 3311.5344 3311.5339 \n", + "4 target_0_22781_3_-1 True 22781 3774.4296 3773.4241 \n", + "\n", + " Peptide mokapot score \\\n", + "0 K.KLHEEEIQELQAQIQEQHVQIDVDVSKPDLTAALR.D 11.269486 \n", + "1 R.RGNVAGDSKNDPPMEAAGFTAQVIILNHPGQISAGYAPVLDCHT... 11.193831 \n", + "2 K.ALGKYGPADVEDTTGSGATDSKDDDDIDLFGS[79.97]DDEEE... 10.628403 \n", + "3 K.EAESCDCLQGFQLTHSLGGGTGSGMGTLLLSK.I 10.170487 \n", + "4 R.TPQRGDEEGLGGEEEEEEEEEEEDDS[79.97]AEEGGAAR.L 9.938360 \n", + "\n", + " mokapot q-value mokapot PEP Proteins percolator score \\\n", + "0 0.000056 6.305117e-16 sp|P08670|VIME_HUMAN 10.45670 \n", + "1 0.000056 6.305117e-16 sp|P68104|EF1A1_HUMAN 10.37390 \n", + "2 0.000056 6.305117e-16 sp|P24534|EF1B_HUMAN 10.44610 \n", + "3 0.000056 6.305117e-16 sp|Q3ZCM7|TBB8_HUMAN 9.89594 \n", + "4 0.000056 6.305117e-16 sp|Q9Y2K7|KDM2A_HUMAN 9.75266 \n", + "\n", + " percolator q-value percolator PEP \n", + "0 0.000052 6.305120e-16 \n", + "1 0.000052 6.305120e-16 \n", + "2 0.000052 6.305120e-16 \n", + "3 0.000052 6.305120e-16 \n", + "4 0.000052 6.305120e-16 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read the result files:\n", + "perc_psms = mokapot.read_percolator(os.path.join(out_dir, \"percolator.psms.txt\"))\n", + "moka_psms = pd.read_table(os.path.join(out_dir, \"mokapot.psms.txt\"))\n", + "\n", + "# Change column names so we can merge tables:\n", + "perc_psms = perc_psms.rename(\n", + " columns={\"PSMId\": \"SpecId\", \n", + " \"peptide\": \"Peptide\",\n", + " \"proteinIds\": \"Proteins\",\n", + " \"score\": \"percolator score\",\n", + " \"q-value\": \"percolator q-value\",\n", + " \"posterior_error_prob\": \"percolator PEP\"})\n", + "\n", + "# Merge the result files.\n", + "psms = pd.merge(moka_psms, perc_psms)\n", + "psms.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fortunately, we can see that a nearly identical number of PSMs are accepted at 1% FDR:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:55:56.097886Z", + "iopub.status.busy": "2021-03-19T22:55:56.097358Z", + "iopub.status.idle": "2021-03-19T22:55:56.117539Z", + "shell.execute_reply": "2021-03-19T22:55:56.118186Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percolator found 27608 PSMs at 1% FDR.\n", + "mokapot found 27645 PSMs at 1% FDR.\n" + ] + } + ], + "source": [ + "perc_passing = (psms[\"percolator q-value\"] <= 0.01).sum()\n", + "moka_passing = (psms[\"mokapot q-value\"] <= 0.01).sum()\n", + "print(f\"Percolator found {perc_passing} PSMs at 1% FDR.\")\n", + "print(f\"mokapot found {moka_passing} PSMs at 1% FDR.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can make some plots to see how the PSM scores and confidence estimates compare to one another. First, we'll define a simple plotting function for our comparisons:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:55:56.124975Z", + "iopub.status.busy": "2021-03-19T22:55:56.124358Z", + "iopub.status.idle": "2021-03-19T22:55:56.125994Z", + "shell.execute_reply": "2021-03-19T22:55:56.126652Z" + } + }, + "outputs": [], + "source": [ + "def comparison_plot(x, y, ax=None):\n", + " \"\"\"\n", + " Create a scatter plot with equal axis scales.\n", + " \n", + " Plot x against y, where the axis limits are scaled \n", + " to be equal. Additionally a y=x line is added.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : numpy.ndarray\n", + " The points to plot\n", + " ax : matplotlib.axes.Axes\n", + " The matplotlib axes to plot on.\n", + " \n", + " Returns\n", + " -------\n", + " matplotlib.axes.Axes\n", + " The axes with the plot.\n", + " \"\"\"\n", + " if ax is None:\n", + " ax = plt.gca()\n", + " \n", + " corr = np.corrcoef(x, y)[0, 1]\n", + " ax.text(0.05, 0.95, f\"Pearson's r = {corr:0.4f}\", \n", + " transform=ax.transAxes, va=\"top\")\n", + " ax.scatter(x, y, s=10, alpha=0.1, edgecolor=\"none\", c=\"#24B8A0\")\n", + " lims = [np.min([ax.get_xlim(), ax.get_ylim()]),\n", + " np.max([ax.get_xlim(), ax.get_ylim()])]\n", + " ax.plot(lims, lims, c=\"black\", zorder=0, linewidth=1)\n", + " ax.set_aspect('equal')\n", + " ax.set_xlim(lims)\n", + " ax.set_ylim(lims)\n", + " return ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can look at how well the scores, q-values, and posterior error probabilities (PEPs) correlate between mokapot and Percolator. Here are the PSMs:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:55:56.149564Z", + "iopub.status.busy": "2021-03-19T22:55:56.148915Z", + "iopub.status.idle": "2021-03-19T22:55:56.985261Z", + "shell.execute_reply": "2021-03-19T22:55:56.985747Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n", + "comparison_plot(psms[\"mokapot score\"], psms[\"percolator score\"], axs[0])\n", + "comparison_plot(psms[\"mokapot q-value\"], psms[\"percolator q-value\"], axs[1])\n", + "comparison_plot(psms[\"mokapot PEP\"], psms[\"percolator PEP\"], axs[2])\n", + "\n", + "for ax, lab in zip(axs, [\"score\", \"q-value\", \"PEP\"]):\n", + " ax.set_xlabel(f\"mokapot {lab}\")\n", + " ax.set_ylabel(f\"Percolator {lab}\")\n", + " \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also do the same for peptides:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:55:56.991650Z", + "iopub.status.busy": "2021-03-19T22:55:56.990982Z", + "iopub.status.idle": "2021-03-19T22:55:58.043152Z", + "shell.execute_reply": "2021-03-19T22:55:58.043650Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Read the result files:\n", + "perc_peps = mokapot.read_percolator(os.path.join(out_dir, \"percolator.peptides.txt\"))\n", + "moka_peps = pd.read_table(os.path.join(out_dir, \"mokapot.peptides.txt\"))\n", + "\n", + "# Change column names so we can merge tables:\n", + "perc_peps = perc_peps.rename(\n", + " columns={\"PSMId\": \"SpecId\", \n", + " \"peptide\": \"Peptide\",\n", + " \"proteinIds\": \"Proteins\",\n", + " \"score\": \"percolator score\",\n", + " \"q-value\": \"percolator q-value\",\n", + " \"posterior_error_prob\": \"percolator PEP\"})\n", + "\n", + "# Merge the result files:\n", + "peps = pd.merge(moka_peps, perc_peps)\n", + "\n", + "# Plot the results:\n", + "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n", + "comparison_plot(peps[\"mokapot score\"], peps[\"percolator score\"], axs[0])\n", + "comparison_plot(peps[\"mokapot q-value\"], peps[\"percolator q-value\"], axs[1])\n", + "comparison_plot(peps[\"mokapot PEP\"], peps[\"percolator PEP\"], axs[2])\n", + "\n", + "for ax, lab in zip(axs, [\"score\", \"q-value\", \"PEP\"]):\n", + " ax.set_xlabel(f\"mokapot {lab}\")\n", + " ax.set_ylabel(f\"Percolator {lab}\")\n", + " \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "or proteins:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2021-03-19T22:55:58.048703Z", + "iopub.status.busy": "2021-03-19T22:55:58.048100Z", + "iopub.status.idle": "2021-03-19T22:55:58.391387Z", + "shell.execute_reply": "2021-03-19T22:55:58.392005Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Read the result files:\n", + "perc_prots = mokapot.read_percolator(os.path.join(out_dir, \"percolator.proteins.txt\"))\n", + "moka_prots = pd.read_table(os.path.join(out_dir, \"mokapot.proteins.txt\"))\n", + "\n", + "# Use just the first protein from mokapot results:\n", + "moka_prots[\"ProteinId\"] = moka_prots[\"mokapot protein group\"].str.split(\", \", expand=True)[0]\n", + "\n", + "# Merge mokapot and Percolator results\n", + "prots = pd.merge(moka_prots, perc_prots)\n", + "\n", + "# Plot the results:\n", + "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n", + "comparison_plot(prots[\"mokapot q-value\"], prots[\"q-value\"], axs[0])\n", + "comparison_plot(prots[\"mokapot PEP\"], prots[\"posterior_error_prob\"], axs[1])\n", + "\n", + "for ax, lab in zip(axs, [\"q-value\", \"PEP\"]):\n", + " ax.set_xlabel(f\"mokapot {lab}\")\n", + " ax.set_ylabel(f\"Percolator {lab}\")\n", + " \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up\n", + "\n", + "As you can see from the plots above, the results from mokapot and Percolator were highly correlated. In fact, the variability that we observed is largely due to the stochastic step in the Percolator algorithm: PSMs are randomly assigned to cross-validation folds for analysis. Because of this, you can observe similar variability between Percolator results when run with different random seeds.\n", + "\n", + "In this vignette, we've run Percolator and mokapot from the command line then made plots to compare their results using Python. While mokapot can emulate much of Percolator's functionality from the command line, much more flexibility is unlocked when using it as a Python package. Be sure to check out our other vignettes to learn more." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 4dee6410339ce4a7654b71e2a591895b53591d5a Mon Sep 17 00:00:00 2001 From: William Fondrie Date: Fri, 19 Mar 2021 15:58:16 -0700 Subject: [PATCH 3/5] Updated changelog --- CHANGELOG.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 1f6d7a21..35d093d3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,7 +1,7 @@ # Changelog for mokapot -## [unreleased] -### Added +## [0.7.0] - 2021-03-19 +### Added - Support for downstream peptide and protein quantitation with [FlashLFQ](https://github.com/smith-chem-wisc/FlashLFQ). This is accomplished through the `mokapot.to_flashlfq()` function or the `to_flashlfq()` method of From 77f3c5ff930987cc4d81967e15bb0c373a46033f Mon Sep 17 00:00:00 2001 From: William Fondrie Date: Fri, 19 Mar 2021 16:28:28 -0700 Subject: [PATCH 4/5] Added test for setuptools startup --- mokapot/__init__.py | 4 +--- tests/unit_tests/test_version.py | 18 ++++++++++++++++++ 2 files changed, 19 insertions(+), 3 deletions(-) create mode 100644 tests/unit_tests/test_version.py diff --git a/mokapot/__init__.py b/mokapot/__init__.py index 66ee6bc7..0ca4b7f4 100644 --- a/mokapot/__init__.py +++ b/mokapot/__init__.py @@ -1,6 +1,4 @@ -""" -Initialize the mokapot package. -""" +"""Initialize the mokapot package.""" try: from importlib.metadata import version, PackageNotFoundError diff --git a/tests/unit_tests/test_version.py b/tests/unit_tests/test_version.py new file mode 100644 index 00000000..79bdbad7 --- /dev/null +++ b/tests/unit_tests/test_version.py @@ -0,0 +1,18 @@ +"""Test that getting the version works""" + + +def test_importlib(): + """This is the fast way for Python 3.8+""" + import mokapot + + assert mokapot.__version__ != "0.0.0" + + +def test_setuptools(): + """We use this for Python < 3.8""" + import sys + + sys.modules["importlib.metadata"] = None + import mokapot + + assert mokapot.__version__ != "0.0.0" From 3aa5865feeebc43ee3a5e0d92de0860af307fb90 Mon Sep 17 00:00:00 2001 From: William Fondrie Date: Fri, 19 Mar 2021 16:53:44 -0700 Subject: [PATCH 5/5] Added some qvalue error tests --- tests/unit_tests/test_qvalues.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/tests/unit_tests/test_qvalues.py b/tests/unit_tests/test_qvalues.py index 72050b0a..d3212c1e 100644 --- a/tests/unit_tests/test_qvalues.py +++ b/tests/unit_tests/test_qvalues.py @@ -85,3 +85,19 @@ def test_tdc_ascending(asc_scores): qvals = tdc(scores, target.astype(dtype), desc=False) np.testing.assert_array_equal(qvals, true_qvals) + + +def test_tdc_non_bool(): + """If targets is not boolean, should get a value errir""" + scores = np.array([1, 2, 3, 4, 5]) + targets = np.array(["1", "0", "1", "0", "blarg"]) + with pytest.raises(ValueError): + tdc(scores, targets) + + +def test_tdc_diff_len(): + """If the arrays are different lengths, should get a ValueError""" + scores = np.array([1, 2, 3, 4, 5]) + targets = np.array([True] * 3 + [False] * 3) + with pytest.raises(ValueError): + tdc(scores, targets)