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

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.