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:
- Genes are standardized to have similar overall mean and variance;
- Information is pooled across genes from a batch to estimate batch effects (increased level of expression, high variability, etc.);
- 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