A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing
Project description
NS-Forest
Getting Started
Download NSForest_v3dot9_1.py.
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.
Versioning and citations
This is version 3.9.1. Earlier releases 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 zhangy@jcvi.org
- Richard Scheuermann RScheuermann@jcvi.org
- Brian Aevermann baevermann@chanzuckerberg.com
License
This project is licensed under the MIT License.
Acknowledgments
- BICCN
- Allen Institute of Brain Science
- Chan Zuckerberg Initiative
- California Institute for Regenerative Medicine
Project details
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