{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:43:44Z","timestamp":1776123824681,"version":"3.50.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MC_UU_00002\/14"],"award-info":[{"award-number":["MC_UU_00002\/14"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Biometrika Trust"},{"DOI":"10.13039\/501100018956","name":"NIHR Cambridge Biomedical Research Centre","doi-asserted-by":"publisher","award":["BRC1215-20014"],"award-info":[{"award-number":["BRC1215-20014"]}],"id":[{"id":"10.13039\/501100018956","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NHS"},{"name":"National Institute for Health Research or the Department of Health and Social Care"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>While classical approaches for controlling the false discovery rate (FDR) of RNA sequencing (RNAseq) experiments have been well described, modern research workflows and growing databases enable a new paradigm of controlling the FDR globally across RNAseq experiments in the past, present and future. The simplest analysis strategy that analyses each RNAseq experiment separately and applies an FDR correction method can lead to inflation of the overall FDR. We propose applying recently developed methodology for online multiple hypothesis testing to control the global FDR in a principled way across multiple RNAseq experiments.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We show that repeated application of classical repeated offline approaches has variable control of global FDR of RNAseq experiments over time. We demonstrate that the online FDR algorithms are a principled way to control FDR. Furthermore, in certain simulation scenarios, we observe empirically that online approaches have comparable power to repeated offline approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The onlineFDR package is freely available at http:\/\/www.bioconductor.org\/packages\/onlineFDR. Additional code used for the simulation studies can be found at https:\/\/github.com\/latlio\/onlinefdr_rnaseq_simulation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac718","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T14:54:34Z","timestamp":1667487274000},"source":"Crossref","is-referenced-by-count":8,"title":["Global FDR control across multiple RNAseq experiments"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8066-5947","authenticated-orcid":false,"given":"Lathan","family":"Liou","sequence":"first","affiliation":[{"name":"Merck Research Laboratories, Merck & Co. , Kenilworth, NJ 07033, USA"}]},{"given":"Milena","family":"Hornburg","sequence":"additional","affiliation":[{"name":"Merck Research Laboratories, Merck & Co. , Kenilworth, NJ 07033, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6207-0416","authenticated-orcid":false,"given":"David S","family":"Robertson","sequence":"additional","affiliation":[{"name":"MRC Biostatistics Unit, University of Cambridge , Cambridge CB2 0SR, UK"}]}],"member":"286","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"2023010107545579200_btac718-B1","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/S0895-4356(00)00314-0","article-title":"Adjusting for multiple testing\u2014when and how?","volume":"54","author":"Bender","year":"2001","journal-title":"J. Clin. Epidemiol"},{"key":"2023010107545579200_btac718-B2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. B Methodol"},{"key":"2023010107545579200_btac718-B3","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1214\/aos\/1013699998","article-title":"The control of the false discovery rate in multiple testing under dependency","volume":"29","author":"Benjamini","year":"2001","journal-title":"Ann. Stat"},{"key":"2023010107545579200_btac718-B4","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/1471-2105-14-128","article-title":"Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool","volume":"14","author":"Chen","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2023010107545579200_btac718-B5","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1177\/107327481001700108","article-title":"The false discovery rate: a key concept in large-scale genetic studies","volume":"17","author":"Chen","year":"2010","journal-title":"Cancer Control"},{"key":"2023010107545579200_btac718-B6","first-page":"1438","article-title":"From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline","volume":"5","author":"Chen","year":"2016","journal-title":"F1000Res"},{"key":"2023010107545579200_btac718-B7","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1186\/s12864-017-3797-0","article-title":"Differentially expressed genes from RNA-Seq and functional enrichment results are affected by the choice of single-end versus paired-end reads and stranded versus non-stranded protocols","volume":"18","author":"Corley","year":"2017","journal-title":"BMC Genomics"},{"key":"2023010107545579200_btac718-B8","doi-asserted-by":"crossref","first-page":"eaar3593","DOI":"10.1126\/science.aar3593","article-title":"Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based Immunotherapy","volume":"362","author":"Cristescu","year":"2018","journal-title":"Science (New York, N.Y.)"},{"key":"2023010107545579200_btac718-B9","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1038\/nprot.2009.97","article-title":"Mapping identifiers for the integration of genomic datasets with the R\/bioconductor package biomaRt","volume":"4","author":"Durinck","year":"2009","journal-title":"Nat. Protoc"},{"key":"2023010107545579200_btac718-B10","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1093\/nar\/30.1.207","article-title":"Gene expression omnibus: NCBI gene expression and hybridization array data repository","volume":"30","author":"Edgar","year":"2002","journal-title":"Nucleic Acids Res"},{"key":"2023010107545579200_btac718-B11","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1214\/17-AOS1559","article-title":"Online rules for control of false discovery rate and false discovery exceedance","volume":"46","author":"Javanmard","year":"2018","journal-title":"Ann. Stat"},{"key":"2023010107545579200_btac718-B12","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1186\/s13059-019-1716-1","article-title":"A practical guide to methods controlling false discoveries in computational biology","volume":"20","author":"Korthauer","year":"2019","journal-title":"Genome Biol"},{"key":"2023010107545579200_btac718-B13","doi-asserted-by":"crossref","first-page":"R29","DOI":"10.1186\/gb-2014-15-2-r29","article-title":"Voom: precision weights unlock linear model analysis tools for RNA-seq read counts","volume":"15","author":"Law","year":"2014","journal-title":"Genome Biol"},{"key":"2023010107545579200_btac718-B14","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol"},{"key":"2023010107545579200_btac718-B15","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"Limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023010107545579200_btac718-B16","doi-asserted-by":"crossref","first-page":"4196","DOI":"10.1093\/bioinformatics\/btz191","article-title":"onlineFDR: an R package to control the false discovery rate for growing data repositories","volume":"35","author":"Robertson","year":"2019","journal-title":"Bioinformatics"},{"key":"2023010107545579200_btac718-B17","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1093\/bioinformatics\/btu324","article-title":"compcodeR\u2014an R package for benchmarking differential expression methods for RNA-seq data","volume":"30","author":"Soneson","year":"2014","journal-title":"Bioinformatics"},{"key":"2023010107545579200_btac718-B18","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1111\/1467-9868.00346","article-title":"A direct approach to false discovery rates","volume":"64","author":"Storey","year":"2002","journal-title":"J. R. Stat. Soc. B Stat. Methodol"},{"key":"2023010107545579200_btac718-B19","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023010107545579200_btac718-B20","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1093\/intimm\/dxs098","article-title":"PD-1 is a novel regulator of human B-cell activation","volume":"25","author":"Thibult","year":"2013","journal-title":"Int. Immunol"},{"key":"2023010107545579200_btac718-B21","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1038\/s41467-018-06500-x","article-title":"DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery","volume":"9","author":"Ye","year":"2018","journal-title":"Nat. Commun"},{"key":"2023010107545579200_btac718-B22","author":"Zrnic","year":"2020"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac718\/47220140\/btac718.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/1\/btac718\/48448999\/btac718.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/1\/btac718\/48448999\/btac718.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T02:53:27Z","timestamp":1728269607000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btac718\/6795009"}},"subtitle":[],"editor":[{"given":"Christina","family":"Kendziorski","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,11,3]]},"published-print":{"date-parts":[[2023,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac718","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1,1]]},"published":{"date-parts":[[2022,11,3]]},"article-number":"btac718"}}