Motivation: 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.
Results: We developed Omixer-a 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.
Availability: Omixer is available from Bioconductor at http://bioconductor.org/packages/release/bioc/html/Omixer.html.
Supplementary information: Supplementary data are available at Bioinformatics online.
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