{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:55:22Z","timestamp":1790596522889,"version":"4.1.0"},"posted":{"date-parts":[[2026,9,24]]},"group-title":"Bioinformatics","reference-count":41,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health","award":["1R35GM158529"],"award-info":[{"award-number":["1R35GM158529"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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\u2019s single-pass fit.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    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\n                    <jats:italic>r<\/jats:italic>\n                    \u2265 0.99999 on \u2212 log\n                    <jats:sub>10<\/jats:sub>\n                    <jats:italic>p<\/jats:italic>\n                    -values, 100% complete top-100 DE gene overlap, and\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    = 1.000 at the nominal FDR level 0.05.\n                  <\/jats:p>\n                  <jats:p>It consistently outperforms Fisher, Stouffer, random-effects, and RankProd meta-analysis on every tested dataset.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>\n                    Source code is archived at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/doi.org\/10.5281\/zenodo.20450301\">https:\/\/doi.org\/10.5281\/zenodo.20450301<\/jats:ext-link>\n                    .\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>\n                    <jats:email>zhiwei@njit.edu<\/jats:email>\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.09.17.752480","type":"posted-content","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T21:00:22Z","timestamp":1790283622000},"source":"Crossref","is-referenced-by-count":0,"title":["FedEdgeR: federated and privacy-preserving edgeR for differential gene expression analysis"],"prefix":"10.64898","author":[{"given":"Xun","family":"Song","sequence":"first","affiliation":[{"name":"Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026092804000446000_2026.09.17.752480v1.1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-025-03684-6"},{"key":"2026092804000446000_2026.09.17.752480v1.2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"2026092804000446000_2026.09.17.752480v1.3","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"2026092804000446000_2026.09.17.752480v1.4","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-024-03296-6"},{"key":"2026092804000446000_2026.09.17.752480v1.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.febslet.2004.07.055"},{"key":"2026092804000446000_2026.09.17.752480v1.6","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-11-94"},{"key":"2026092804000446000_2026.09.17.752480v1.7","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18186-1"},{"key":"2026092804000446000_2026.09.17.752480v1.8","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkaf018"},{"key":"2026092804000446000_2026.09.17.752480v1.9","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1979.10481038"},{"key":"2026092804000446000_2026.09.17.752480v1.10","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1987.tb01422.x"},{"key":"2026092804000446000_2026.09.17.752480v1.11","doi-asserted-by":"crossref","unstructured":"R. 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