NS-Forest v3.9

[Release Note:] Major code optimizations based on algorithm v3.0. No algorithmic change to v3.0.

Changes of parameter name from v3.0
[old name] = [new name]
threads = n_jobs
howManyInformativeGenes2test = n_top_genes
InformativeGenes = n_binary_genes
clusterLabelcolumnHeader = cluster_header
rfTrees = n_trees
Median_Expression_Level = median_cutoff = 0 #set to 0
Genes_to_testing = n_genes_eval
dataDummy = df_dummies
column = cl

Download and installation

NS-Forest can be installed using pip:
sudo pip install nsforest

If you are using a machine on which you lack administrative access, NS-Forest can be installed locally using pip:
pip install --user nsforest

NS-Forest can also be installed using conda:
conda install -c ttl074 nsforest

Will be uploaded to official conda channel soon.

Prerequisites:

• This is a python script written and tested in python 3.8, scanpy 1.8.2, anndata 0.8.0.
• Other required libraries: numpy, pandas, sklearn, itertools, time, tqdm.

Tutorial

Follow the tutorial to get started.

If you download 'NSForest_v3dot9_2.py' directly, replace the version to the most updated one in the tutorial.

If you download the pip or conda package, use the following in the tutorial.

import nsforest as ns
ns.NSForest()

Versions and citations

Earlier versions are managed in Releases.

Version 2 and beyond:

Aevermann BD, Zhang Y, Novotny M, Keshk M, Bakken TE, Miller JA, Hodge RD, Lelieveldt B, Lein ES, Scheuermann RH. A machine learning method for the discovery of minimum marker gene combinations for cell-type identification from single-cell RNA sequencing. Genome Res. 2021 Jun 4:gr.275569.121. doi: 10.1101/gr.275569.121.

Version 1.3/1.0:

Aevermann BD, Novotny M, Bakken T, Miller JA, Diehl AD, Osumi-Sutherland D, Lasken RS, Lein ES, Scheuermann RH. Cell type discovery using single-cell transcriptomics: implications for ontological representation. Hum Mol Genet. 2018 May 1;27(R1):R40-R47. doi: 10.1093/hmg/ddy100.

Authors

• Yun (Renee) Zhang [email protected]
• Richard Scheuermann [email protected]
• Brian Aevermann [email protected]

License

This project is licensed under the MIT License.

Acknowledgments

• BICCN
• Allen Institute of Brain Science
• Chan Zuckerberg Initiative
• California Institute for Regenerative Medicine

What's Changed

• Adding pip instructions and packaging files by @ttl074 in #13

New Contributors

• @ttl074 made their first contribution in #13

Full Changelog: v3.0...v3.9