{
  "abstract": "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 FedPyDESeq2 federates DESeq2. edgeR, still preferred for small-sample and high-variability designs, lacks a federated counterpart. Its iteratively reweighted least squares (IRLS) and Cox-Reid dispersion estimation make federation harder than limma-voom's single-pass fit. Results: We present FedEdgeR, a federated edgeR implementation protected by secure multi-party computation (SMPC), covering IRLS GLM fitting, three-level dispersion estimation, and the likelihood-ratio test. We evaluate it on four representative RNA-seq datasets: two tumor/normal cohorts, a multi-cohort anti-PD-1 immunotherapy study, and a 6-sample paired stress-test. FedEdgeR matches pooled edgeR under various metrics, such as Pearson r [≥] 0.99999 on -log10 p-values, 100% complete top-100 DE gene overlap, and F1 = 1.000 at the nominal FDR level 0.05. It consistently outperforms Fisher, Stouffer, random-effects, and RankProd meta-analysis on every tested dataset.",
  "abstract_shown": true,
  "affiliations": [
    "New Jersey Institute of Technology"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "X. Song"
    },
    {
      "affs": [
        0
      ],
      "name": "Z. Wei"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2cv2qwr10faqe.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.17.752480",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752480v1.full",
  "id": "2cv2qwr10faqe",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752480v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7214159687",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752480v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2cv2qwr10faqe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:22Z",
      "native_id": "10.64898/2026.09.17.752480",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752480v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "FedEdgeR: federated and privacy-preserving edgeR for differential gene expression analysis",
  "title_text": "FedEdgeR: federated and privacy-preserving edgeR for differential gene expression analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:22Z",
  "url": "https://research.draconitic.com/paper/2cv2qwr10faqe",
  "venue": "bioRxiv",
  "versions": []
}