A Bayesian multiple comparison approach for gene expression data analysis

Abstract

Methods used to detect differentially expressed genes in situations with one control and one treatment are t-tests. These methods do not per- form well when control and treatment variances are different. In situations with a control and more than one treatment, it is common to apply analysis of variance followed by a Tukey and/or Duncan test to identify which treat- ment caused the difference. We propose a Bayesian approach for multiple comparison analysis which is very useful in the context of DNA microarray experiments. It uses a priori Dirichlet process and Polya urn scheme. It is a unified procedure (for cases with one or more treatments) which detects differentially expressed genes and identify treatments causing the difference. We use simulations to verify the performance of the proposed method and compare it with usual methods. In cases with control and one treatment and control and more than one treatment followed by Tukey and Duncan tests, the method presents better performance when variances are different. The method is applied to two real data sets. In these cases, genes not detected by usual methods are identified by the proposed method.