AbstractMotivationBatch 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.ResultsWe 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.Availabilityand implementationOmixer is available from Bioconductor athttp://bioconductor.org/packages/release/bioc/html/Omixer.html. Scripts and data used to generate figures available upon request.Supplementary informationSupplementary dataare available atBioinformaticsonline.