{
  "id": 9625974,
  "title": "FedEdgeR: federated and privacy-preserving edgeR for differential gene expression analysis",
  "url": "https://urgent.news/2026/09/24/fededger-federated-and-privacy-preserving-edger-for-differential-gene",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-24T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752480v1?rss=1"
  },
  "original_language": "en",
  "account": "Motivation for the development of Federated and Privacy-Preserving edgeR (FedEdgeR) stems from the need to improve statistical power in multi-center RNA-seq studies while respecting privacy regulations that restrict patient-level data sharing. Meta-analysis methods, which pool data from multiple studies, can bypass these restrictions but often sacrifice power, particularly when faced with imbalances in sample sizes across sites. Federated learning offers a solution by enabling sites to collaborate without sharing raw data, instead exchanging summary statistics.\n\nAmong the three leading differential expression (DE) frameworks, Flimma federates limma-voom and FedPyDESeq2 federates DESeq2. However, edgeR, a method frequently favored for its performance in small-sample and high-variability designs, has remained without a federated counterpart. This is due to edgeR's reliance on iteratively reweighted least squares (IRLS) and Cox-Reid dispersion estimation, which pose challenges for secure federation compared to limma-voom's more straightforward single-pass fitting approach.\n\nFedEdgeR, introduced in this work, addresses this gap by presenting a federated implementation of edgeR that employs secure multi-party computation (SMPC) to ensure data privacy. FedEdgeR incorporates IRLS GLM fitting, three-level dispersion estimation, and the likelihood-ratio test, allowing it to handle the unique aspects of edgeR's methodology. To assess its performance, FedEdgeR was evaluated on four diverse RNA-seq datasets, including two tumor/normal cohorts, a multi-cohort anti-PD-1 immunotherapy study, and a 6-sample paired stress-test.\n\nThe results demonstrate that FedEdgeR delivers comparable performance to pooled edgeR across various metrics. Specifically, it achieves Pearson's correlation coefficients (r) greater than or equal to 0.99999 on -log10 p-values, maintains 100% complete overlap of the top-100 DE genes, and attains an F1 score of 1.000 at a false discovery rate (FDR) of 0.05. Crucially, FedEdgeR consistently outperforms Fisher's, Stouffer's, random-effects, and RankProd meta-analysis methods when tested on every dataset, showcasing its effectiveness in maintaining both privacy and analytical robustness in federated settings.",
  "summary": "Motivation: Multi-center RNA-seq studies improve statistical power, but privacy regulations restrict patient-level data sharing. Meta-analysis methods avoid this restriction but lose power, especially under per-site imbalance. Federated learning allows sites to share only summary statistics. Among the three dominant differential expression (DE) frameworks, Flimma federates limma-voom and…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}