Batch effects can have a major impact on the results of omics studies (Leek et al, 2010). Randomization is the first, and arguably most influential, step in handling them. However, its implementation suffers from a few key issues:
To combat these problems, we developed Omixer - an R package for multivariate randomization and reproducible generation of intuitive sample layouts.
Omixer randomizes input sample lists multiple times (default: 1,000) and then combines these randomized lists with plate layouts, which can be selected from commonly used setups or custom-made. It can handle paired samples, keeping these adjacent but shuffling their order, and allows explicit masking of specific wells if, for example, plates are shared between different studies.
After performing robust tests of correlation between technical covariates and selected randomization factors, a layout is chosen using these criteria:
The optimal randomized list can then be processed by
omixerSheet, returning intuitive sample layouts for the wet lab.
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