Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Centre, Leiden 2333 ZC, The Netherlands.
Bioinformatics. 2021 Sep 29;37(18):3051-3052. doi: 10.1093/bioinformatics/btab159.
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.
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.
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 data are available at Bioinformatics online.
批次效应在组学研究中会严重影响结果，导致偏差和假阳性结果，但缺乏预先控制这些效应的软件。在测量之前对样本进行随机化对于最小化这些效应至关重要，但目前的方法往往是特定的，记录不佳，并且无法处理多个批次和结果。
我们开发了 Omixer-一个用于组学研究的 Bioconductor 软件包，实现了多元和可重复的样本随机化。它通过优化样本在批次之间的分布，主动对抗技术因素与感兴趣的生物学变量之间的相关性。
Omixer 可从 Bioconductor 获得，网址为 http://bioconductor.org/packages/release/bioc/html/Omixer.html。生成图的脚本和数据可根据要求提供。
补充数据可在 Bioinformatics 在线获得。
