GeneClust: cofunctional grouping-based feature gene selection for unsupervised scRNA-seq clustering
GeneClust is a computational feature selection method for scRNA-seq cell clustering. GeneClust groups genes into clusters from which genes are evaluated and selected with the aim of maximizing relevance, minimizing redundancy and preserving complementarity.
Dependencies
- numpy>=1.21.5
- pandas>=1.4.2
- anndata>=0.8.0
- setuptools>=59.5.0
- loguru>=0.6.0
- sklearn>=0.0
- scikit-learn>=1.1.1
- scanpy>=1.9.1
- scipy>=1.7.3
- leidenalg>=0.8.9
Installation
- PyPI
You can directly install the package from PyPI.
pip3 install GeneClust
- Github
Also, You can download the package from Github and install it locally:
git clone https://github.com/ToryDeng/scGeneClust.git
cd scGeneClust/
python3 setup.py install --user
Two Versions of GeneClust
| Version | Usage Scenarios |
|---|---|
| GeneClust-ps | 1. Number of cells is small (e.g., several thousand) 2. Cell clustering performance is more important |
| GeneClust-fast | 1. Number of cells is large (e.g., over 50,000) 2. Computational efficiency is more important |
Tutorial
For the step-by-step tutorial, please refer to the notebook:
https://github.com/ToryDeng/scGeneClust/blob/main/notebooks/tutorial.ipynb
Reproducibility
To reproduce the results presented in our paper, please go to https://github.com/ToryDeng/scGeneClust/tree/main/figures to download the outputs of GeneClust and code to generate the figures.
