Adjusting batch effects in microarray expression data using empirical Bayes methods

TLDR

Batch effects in microarray experiments hinder data integration, yet combining datasets is essential for statistical power, and current correction methods typically require large batch sizes that many studies cannot meet. This study proposes parametric and non‑parametric empirical Bayes frameworks that adjust for batch effects robustly in small samples while matching performance in larger datasets. The authors demonstrate the methods on two example datasets, showing they are straightforward to apply and practically useful. The resulting software is freely available at http://biosun1.harvard.edu/complab/batch/.

Abstract

Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (⁠>25⁠) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.