{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T02:40:37Z","timestamp":1774579237990,"version":"3.50.1"},"reference-count":10,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"DIMENSION project","award":["529051021"],"award-info":[{"award-number":["529051021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,29]]},"abstract":"Abstract<\/jats:title>\n \n Motivation<\/jats:title>\n Batch effects heavily impact results in omics studies, causing bias and false positive results, but software to control them preemptively is lacking. Sample randomization prior to measurement is vital for minimizing these effects, but current approaches are often ad hoc, poorly documented and ill-equipped to handle multiple batches and outcomes.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We developed Omixer\u2014a Bioconductor package implementing multivariate and reproducible sample randomization for omics studies. It proactively counters correlations between technical factors and biological variables of interest by optimizing sample distribution across batches.<\/jats:p>\n <\/jats:sec>\n \n Availabilityand implementation<\/jats:title>\n Omixer is available from Bioconductor at http:\/\/bioconductor.org\/packages\/release\/bioc\/html\/Omixer.html. Scripts and data used to generate figures available upon request.<\/jats:p>\n <\/jats:sec>\n \n Supplementary information<\/jats:title>\n Supplementary data are available at Bioinformatics online.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab159","type":"journal-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T20:12:44Z","timestamp":1614888764000},"page":"3051-3052","source":"Crossref","is-referenced-by-count":19,"title":["Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9209-1266","authenticated-orcid":false,"given":"Lucy","family":"Sinke","sequence":"first","affiliation":[{"name":"Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Centre , Leiden 2333 ZC, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9684-220X","authenticated-orcid":false,"given":"Davy","family":"Cats","sequence":"additional","affiliation":[{"name":"Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Centre , Leiden 2333 ZC, The Netherlands"}]},{"given":"Bastiaan T","family":"Heijmans","sequence":"additional","affiliation":[{"name":"Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Centre , Leiden 2333 ZC, The Netherlands"}]}],"member":"286","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"2023061310554537500_btab159-B1","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1200\/JCO.2007.15.1951","article-title":"Run batch effects potentially compromise the usefulness of genomic signatures for ovarian cancer","volume":"26","author":"Baggerly","year":"2008","journal-title":"J. Clin. Oncol"},{"key":"2023061310554537500_btab159-B2","doi-asserted-by":"crossref","first-page":"354","DOI":"10.3389\/fgene.2014.00354","article-title":"Stratified randomization controls better for batch effects in 450K methylation analysis: a cautionary tale","volume":"5","author":"Buhule","year":"2014","journal-title":"Front. Genet"},{"key":"2023061310554537500_btab159-B3","doi-asserted-by":"crossref","first-page":"e0202947","DOI":"10.1371\/journal.pone.0202947","article-title":"Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptomic data","volume":"13","author":"Espin-Perez","year":"2018","journal-title":"PLoS One"},{"key":"2023061310554537500_btab159-B4","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.tibtech.2017.02.012","article-title":"Why batch effects matter in omics data, and how to avoid them","volume":"35","author":"Goh","year":"2017","journal-title":"Trends Biotechnol"},{"key":"2023061310554537500_btab159-B5","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1158\/1055-9965.EPI-13-0114","article-title":"Batch effects and pathway analysis: two potential perils in cancer studies involving DNA methylation array analysis","volume":"22","author":"Harper","year":"2013","journal-title":"Cancer Epidemiol. Biomarkers Prev"},{"key":"2023061310554537500_btab159-B6","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","article-title":"Adjusting batch effects in microarray expression data using empirical Bayes methods","volume":"8","author":"Johnson","year":"2007","journal-title":"Biostatistics"},{"key":"2023061310554537500_btab159-B7","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1093\/biostatistics\/kxr055","article-title":"Learning from our GWAS mistakes: from experimental design to scientific method","volume":"13","author":"Lambert","year":"2012","journal-title":"Biostatistics"},{"key":"2023061310554537500_btab159-B8","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1038\/nrg2825","article-title":"Tackling the widespread and critical impact of batch effects in high-throughput data","volume":"11","author":"Leek","year":"2010","journal-title":"Nat. Rev. Genet"},{"key":"2023061310554537500_btab159-B9","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s13059-016-1131-9","article-title":"Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution","volume":"18","author":"van Iterson","year":"2017","journal-title":"Genome Biol"},{"key":"2023061310554537500_btab159-B10","doi-asserted-by":"crossref","first-page":"e3724","DOI":"10.1371\/journal.pone.0003724","article-title":"Randomization in laboratory procedure is key to obtaining reproducible microarray results","volume":"3","author":"Yang","year":"2008","journal-title":"PLoS One"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab159\/36622578\/btab159.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/18\/3051\/50578908\/btab159.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/18\/3051\/50578908\/btab159.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T10:57:10Z","timestamp":1686653830000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/18\/3051\/6162882"}},"subtitle":[],"editor":[{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":10,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2021,9,29]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab159","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,9,15]]},"published":{"date-parts":[[2021,3,8]]}}}