Metadata-Version: 2.3
Name: Ramin123455
Version: 1.0.0
Summary: Python package for cell identity recognition at individual cell level from single-cell RNA-seq data. Cell types for unknown cells are predicted, through a statistical approach, using raw gene expresssion data among a list of cells, and a gene marker dataset consisting of a list of genes found in known cell types/gene sets.
Project-URL: Homepage, https://github.com/RockLee117/sc-pred
Project-URL: Issues, https://github.com/RockLee117/sc-pred/issues
Author-email: Ramin Mohammadi <rammoh5346@gmail.com>
License-File: LICENSE.txt
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.11
Requires-Dist: anndata>=0.9.1
Requires-Dist: numpy>=1.24.4
Requires-Dist: pandas>=2.1.4
Requires-Dist: plotly>=5.16.1
Requires-Dist: scanpy>=1.9.3
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: scipy>=1.11.1
Requires-Dist: umap-learn>=0.5.3
Description-Content-Type: text/markdown

# sc-pred

## Description
Python package for cell identity recognition at individual cell level from single-cell RNA-seq data.<br/><br/>
Cell types for unknown cells are predicted, through a statistical approach, using raw gene expresssion <br/>
data among a list of cells, and a gene marker dataset consisting of a list of genes found in known cell types/gene sets.<br/><br/>
sc-pred is based off of the
- R software: https://github.com/RausellLab/CelliD
- CelliD statistical method presented in the article: <a href="https://www.nature.com/articles/s41587-021-00896-6.epdf?sharing_token=cb8TdGrz0o3PXjXn_wZGCdRgN0jAjWel9jnR3ZoTv0Oa3WzvJLtg4J6wv_eRGblv7pCmV-VB-3abW6uWDvAeOER7rbNPidd1IsRjFITIK8SJ_d0RrfACjtlZFkN4l3DDZLXWnaDW2XZDF1uZ-2DWCHQNkva9vqKjz708F5zU2FU%3D">Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID, Nature Biotechnology 2021</a>

Please take a look at the <a href="https://github.com/RockLee117/sc-pred">README.md</a> in the repository for further understanding of the implementation.

## Author
- Ramin Mohammadi, rammoh5346@gmail.com

## License

This package is under the GNU General Public License v3.0