Metadata-Version: 2.4
Name: pySingleCellNet
Version: 0.1.3
Summary: Single-cell classification and analysis, optimized for embryonic and fetal development
Author-email: Patrick Cahan <patrick.cahan@gmail.com>, Yuqi Tan <ytan19@jhmi.edu>
License-Expression: MIT
Project-URL: Documentation, https://cahanlab-pysinglecellnet.readthedocs-hosted.com/
Project-URL: Source, https://github.com/CahanLab/PySingleCellNet
Project-URL: Repository, https://github.com/CahanLab/PySingleCellNet.git
Project-URL: Issues, https://github.com/CahanLab/PySingleCellNet/issues
Project-URL: Changelog, https://github.com/CahanLab/PySingleCellNet/tree/master/docs/CHANGELOG.md
Keywords: single cell,cell typing,classification,embryo,embryoid,gastruloid
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: scikit-misc
Requires-Dist: scanpy
Requires-Dist: statsmodels
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: umap-learn
Requires-Dist: mygene
Requires-Dist: palettable
Requires-Dist: gseapy
Requires-Dist: alive_progress
Requires-Dist: python-igraph
Requires-Dist: marsilea
Dynamic: license-file

# pySingleCellNet

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**pySingleCellNet** helps you classify and analyze single-cell RNA-Seq data, …

# PySingleCellNet
### A computational toolkit for the single cell analysis and comparison of embryos and embryo models
PySingleCellNet (PySCN) predicts the 'cell type' of query scRNA-seq data by Random forest multi-class classification. See [Tan & Cahan 2019] for more details. PySCN includes functionality to aid in the analysis of engineered cell populations (i.e. cells derived via directed differentiation of pluripotent stem cells or via direct conversion).

[Tan & Cahan 2019]: https://doi.org/10.1016/j.cels.2019.06.004
[github]: https://github.com/pcahan1/PySingleCellNet
[original version]: https://github.com/pcahan1/PySingleCellNet

