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PyDESeq2 documentation

This package is a python implementation of the DESeq2 method :cite:p:`love2014moderated` 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, with an optional apeGLM LFC shrinkage step :cite:p:`zhu2019heavy`.

PyDESeq2 also supports pytximport-derived normalization factors :cite:p:`kuehlGeneCountEstimation2024b`, enabling accurate differential expression analysis with transcript-level quantification data from tools like Salmon, Kallisto, or RSEM. When explicitly enabled, this feature accounts for gene length differences between samples due to differential isoform usage.

We plan to implement more features 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.

Citing this work

@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},
}

License

PyDESeq2 is released under an MIT license.

.. toctree::
   :hidden:
   :maxdepth: 2
   :caption: General

   usage/installation
   usage/requirements
   usage/contributing
   usage/references


.. toctree::
   :hidden:
   :maxdepth: 2
   :caption: API

   api/index


.. toctree::
   :hidden:
   :maxdepth: 2
   :caption: Tutorials

   auto_examples/index.rst