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Fed-PyDESeq2 documentation

This package is a federated python implementation of the DESeq2 method 1 for differential expression analysis (DEA) with bulk RNA-seq data, originally in R. This federated implementation is based on Substra, an open source federated learning software.

This package is based on (and benchmarked against) PyDESeq2 2, which is a python re-implementation of DESeq2.

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, without the LFC shrinkage step.

Citing this work

@article{muzellec2024fedpydeseq2,
  title={FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis},
  author={Muzellec, Boris and Marteau-Ferey, Ulysse and Marchand, Tanguy},
  journal={bioRxiv},
  pages={2024--12},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}

License

FedPyDESeq2 is released under an MIT license.

  1. Michael I Love, Wolfgang Huber, and Simon Anders. Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome biology, 15(12):1–21, 2014. doi:10.1186/s13059-014-0550-8. ↩

  2. Boris Muzellec, Maria Telenczuk, Vincent Cabeli, and Mathieu Andreux. Pydeseq2: a python package for bulk rna-seq differential expression analysis. Bioinformatics, 2023. doi:10.1093/bioinformatics/btad547. ↩