An empirical Bayes' approach to joint analysis of multiple microarray gene expression studies

With the prevalence of gene expression studies and the relatively low reproducibility caused by insufficient sample sizes, it is natural to consider joint analysis that could combine data from different experiments effectively to achieve improved accuracy. We present in this article a model-based approach for better identification of differentially expressed genes by incorporating data from different studies. The model can accommodate in a seamless fashion a wide range of studies including those performed at different platforms by fitting each data with different set of parameters, and/or under different but overlapping biological conditions. Model-based inferences can be done in an empirical Bayes' fashion. Because of the information sharing among studies, the joint analysis dramatically improves inferences based on individual analysis. Simulation studies and real data examples are presented to demonstrate the effectiveness of the proposed approach under a variety of complications that often arise in practice.