A two-stage empirical Bayes method for identifying differentially expressed genes

Due to the high dimensionality of microarray gene expression data and complicated correlations in gene expression levels, statistical methods for analyzing these data often are computationally intensive, requiring special expertise for their implementation. Biologists without such expertise will benefit from a computationally efficient and easy-to-implement analytic method. In this article, we develop such a method: a two-stage empirical Bayes method for identifying differentially expressed genes. We use a special technique to reduce the dimension of the parameter space in the preliminary stage, and construct a Bayesian model in the second stage. The computation of our method is efficient and requires little calibration for real microarray gene expression data. Specifically, we employ statistical tools, including the empirical Bayes estimation and a distribution approximation approach, to speed up computation and at the same time to preserve precision. We develop a score for evaluating the magnitude of the overall differential gene expression levels based on our Bayesian model, and declare differential expression according to the posterior probabilities that their scores exceed some threshold value. The number of declarations is determined by a false discovery rate procedure.