Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies

Bioinformatics, vol. 37, pp. 3051–3052

Abstract

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. AVAILABILITYAND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors 3

1. Lucy Sinke corresponding Aachen Molecular Epidemiology Department of Biomedical Data Science

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Centre , Leiden 2333 ZC, The Netherlands

2. Davy Cats Aachen Molecular Epidemiology Department of Biomedical Data Science

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Centre , Leiden 2333 ZC, The Netherlands

3. Bastiaan T. Heijmans Aachen Molecular Epidemiology Department of Biomedical Data Science

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Centre , Leiden 2333 ZC, The Netherlands