Covariate balanced allocation of samples to batches to mitigate the impacts of technical variability.

Abstract

Performing assays on large numbers of samples requires their analysis in distinct batches, which commonly affects the measurements made in a systematic way. Analytical approaches can correct for such batch effects, however for this to be possible the batch should not be confounded with either the independent variable or any covariates relevant to the analysis. Thus, how samples are distributed across batches influences subsequent analytic conclusions. We present SampleAllocateR, an open source tool that employs optimisation methods from machine learning to optimally allocate a preselected set of samples to experimental batches in a way that statistically balances specified covariates between batches. This results in better statistical estimates of the effect of not only the technical batch effects but also the other specified covariates upon the results of the experiment.