Nonbiological experimental error commonly occurs in microarray data collected in different batches. It is often impossible to compare groups of samples from independent experiments because batch effects confound true gene expression differences. Existing methods for adjusting for batch effects require that samples from all biological groups are represented in every batch. In this chapter we review an experimental design along with a generalized empirical Bayes approach which adjusts for cross-experimental batch effects and therefore allows direct comparisons of gene expression values to be made between biological groups drawn from independent experiments. The necessary feature of this experimental design that enables such comparisons to be made is that identical replicate reference samples are included in each batch in every experiment. We discuss the clinical applications of our approach as well as the advantages of our approach in relation to meta-analysis approaches and other strategies for batch adjustment.
- Batch effects
- Empirical Bayes method
- Reference sample
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)