FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis

Muzellec, B.; Marteau-Ferey, U.; Marchand, T.

doi:10.1101/2024.12.06.627138

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bioRxiv · 10.1101/2024.12.06.627138

FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis

Muzellec, B.·Marteau-Ferey, U.·Marchand, T.

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Abstract

Large-scale transcriptomic studies are often limited by data silos and risks of privacy leakage, which may lead to missed clinical insights. Meta-analysis methods may be used to aggregate local results, but they induce lower statistical power and are particularly sensitive to heterogeneous settings. A recent paradigm in distributed computing, federated learning (FL) is a means of fitting models from siloed data, while ensuring that private data does not leave its storage facilities. Here, we introduce FedPyDESeq2, a software for differential expression analysis (DEA) on siloed bulk RNA-seq. Building on FL tools, FedPyDESeq2 implements the DESeq2 pipeline for DEA on siloed datasets in a privacy-enhancing manner. We benchmark FedPyDESeq2 on datasets from The Cancer Genome Atlas corresponding to 8 different indications, split by geographical origin. FedPyDESeq2 achieves near-identical results on siloed data compared with PyDESeq2 on pooled data, and significantly outperforms meta-analysis baselines.

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Muzellec, B., Marteau-Ferey, U., Marchand, T.. 2024-12-10. FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis. https://doi.org/10.1101/2024.12.06.627138

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