Identifying Differentially Expressed Genes in Meta-Analysis via Bayesian Model-Based Clustering

A Bayesian model‐based clustering approach is proposed for identifying differentially expressed genes in meta‐analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non‐differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration‐driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model‐based approach is also compared with commonly used permutation methods, and it is shown that the model‐based approach is superior to the permutation methods when there are excessive under‐expressed genes compared to over‐expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)