PyDESeq2 documentation

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.

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 analysis, with an optional apeGLM LFC shrinkage 2. 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 on GitHub.

Usage:

Contents:

PyDESeq2

Documentation

The documentation is automatically build using Sphinx, and hosted here on ReadTheDoc.

Citing this work

@article{muzellec2022pydeseq2,
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={2022},
doi = {10.1101/2022.12.14.520412},
journal={bioRxiv},
}

References

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>

License

PyDESeq2 is released under an MIT license.