How does ComBat work for batch effect adjustment?

Before conducting meta-analysis, an important step is to reduce study- or platform- specific effects (a.k.a. batch effects). Combat is a very popular method adjusting batch effects in microarray and RNA-seq data.

The method uses an empirical Bayes approach, and can be summarized in three main steps:

  1. Genes are standardized to have similar overall mean and variance;
  2. Information is pooled across genes from a batch to estimate batch effects (increased level of expression, high variability, etc.);
  3. The estimated batch effects are used to normalize the data to make them more comparable to each other.

For more technical details, please refer to the original publication: Adjusting batch effects in microarray expression data using empirical Bayes methods