NS-Forest v4.0

Documentation: https://nsforest.readthedocs.io/en/latest/

BioArchive Link: TBD

Download and installation

In terminal:

git clone https://github.com/JCVenterInstitute/NSForest.git

cd NSForest

conda env create -f nsforest.yml

conda activate nsforest

cd tutorials

Tutorial

Follow the tutorials/tutorial.ipynb.

Pipeline

Dev version of NS-Forest v4.0

Follow the tutorial to get started.

Download 'NSForest_v4dot0_dev.py' and replace the version in the tutorial. Sample code below.

adata_median = preprocessing_medians(adata, cluster_header)
adata_median.varm["medians_" + cluster_header].stack().plot.hist(bins=30, title = 'cluster medians')

adata_median_binary = preprocessing_binary(adata_median, cluster_header, "medians_" + cluster_header)
adata_median_binary.varm["binary_scores_" + cluster_header].stack().plot.hist(bins=30, title='binary scores')

## make a copy of prepared adata
adata_prep = adata_median_binary.copy()

NSForest(adata_prep, cluster_header=cluster_header, n_trees=1000, n_genes_eval=6,
          medians_header = "medians_" + cluster_header, binary_scores_header = "binary_scores_" + cluster_header,
          gene_selection = "BinaryFirst_high", outputfilename="BinaryFirst_high")

NS-Forest v3.9

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]
• Angela Liu [email protected]
• Beverly Peng [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