PyDESeq2 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.

As PyDESeq2 is a re-implementation of DESeq2 from scratch, you may experience some differences in terms of retrieved values or available features.

Currently, available features broadly correspond to the default settings of DESeq2 (v1.34.0) for single-factor and multi-factor analysis (with categorical or continuous factors) using Wald tests. We plan to implement more in the future. In case there is a feature you would particularly like to be implemented, feel free to open an issue.

• PyDESeq2
  • Table of Contents
  • Installation
    • Requirements
  • Getting started
    • Documentation
    • Data
  • Contributing
    • 1 - Download the repository
    • 2 - Create a conda environment
  • Development roadmap
  • Citing this work
  • References
  • License

PyDESeq2 can be installed from PyPI using pip:

pip install pydeseq2

We recommend installing within a conda environment:

conda create -n pydeseq2
conda activate pydeseq2
conda install pip
pip install pydeseq2

You can also add it to your projects through uv:

uv add pydeseq2

PyDESeq2 can also be installed from Bioconda with conda:

conda install -c bioconda pydeseq2

If you're interested in contributing or want access to the development version, please see the contributing section.

The list of package version requirements is available in pyproject.toml.

For reference, the code is being tested in a github workflow (CI) with python 3.11 to 3.13 and the latest versions of the following packages:

- anndata
- formulaic
- numpy
- pandas
- scikit-learn
- scipy
- formulaic-contrasts
- matplotlib

Please don't hesitate to open an issue in case you encounter any issue due to possible deprecations.

The Getting Started section of the documentation contains downloadable examples on how to use PyDESeq2.

The documentation is hosted here on ReadTheDocs. If you want to have the latest version of the documentation, you can build it from source. Please go to the dedicated README.md for information on how to do so.

The quick start examples use synthetic data, provided in this repo (see datasets.)

The experiments described in the PyDESeq2 article rely on data from The Cancer Genome Atlas, which may be obtained from this portal.

Please the Contributing section of the documentation to see how you can contribute to PyDESeq2.

git clone https://github.com/owkin/PyDESeq2.git

Run uv venv --python 3.13 (or higher python version) to create the pydeseq2 environment and then activate it: source .venv/bin/activate.

cd to the root of the repo and run uv sync --extra dev --extra doc to install in developer mode.

Then, run pre-commit install.

The pre-commit tool will automatically run ruff, black, and mypy.

PyDESeq2 is a living project and any contributions are welcome! Feel free to open new PRs or issues.

@article{muzellec2023pydeseq2,
  title={PyDESeq2: a python package for bulk RNA-seq differential expression analysis},
  author={Muzellec, Boris and Telenczuk, Maria and Cabeli, Vincent and Andreux, Mathieu},
  year={2023},
  doi = {10.1093/bioinformatics/btad547},
  journal={Bioinformatics},
}

[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.