This package is a python implementation of the DESeq2 method [1] for differential expression analysis (DEA) with bulk RNA-seq data, originally in R. It aims to facilitate DEA experiments for python users.
Currently, available features broadly correspond to the default settings of DESeq2 (v1.34.0), but we plan to implement more in the near future. In case there is a feature you would particularly like to be implemented, feel free to open an issue.
For now, the only way to use PyDESeq2 is to install it from source.
PyPI and conda versions will soon be released.
git clone https://github.com/owkin/PyDESeq2.git
Run conda create -n pydeseq2 python=3.8 (or higher python version) to create the environment and then activate it:
conda activate pydeseq2.
cd inside the root of the repo and run pip install . to install.
The list of package version requirements is available in setup.py.
For reference, the code was tested with python 3.8 and the following package versions:
- numpy 1.23.0
- pandas 1.4.3
- scikit-learn 1.1.1
- scipy 1.8.1
- statsmodels 0.13.2
Please don't hesitate to open an issue in case you encounter any issue due to possible deprecations.
The notebooks directory contains minimal examples on how to use PyDESeq2, in the form of jupyter notebooks.
The quick start notebooks use data from The Cancer Genome Atlas.
For more information on how to obtain and organize TCGA data, see datasets.
Run pip install -e ."[dev]" to install in developer mode.
Then, run pre-commit install.
The pre-commit tool will automatically run black
and isort, and check flake8 compatibility
PyDESeq2 is a living project and any contributions are welcome! Feel free to open new PRs or issues.
TBD
[1] Love, M. I., Huber, W., & Anders, S. (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome biology, 15(12), 1-21. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8
[2] Zhu, A., Ibrahim, J. G., & Love, M. I. (2019). "Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences." Bioinformatics, 35(12), 2084-2092. https://academic.oup.com/bioinformatics/article/35/12/2084/5159452
PyDESeq2 is released under an MIT license.