A Robust Empirical Bayesian Method for Detecting Differentially Expressed Genes

Abstract

With the increase in genome-wide experiments and sequenced genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genome-wide data set are tested against null hypotheses, where only a small number of features are expected to be significant. The empirical Bayesian method (EB) is one of the most powerful methods to address such an issue, which has attracted much attention in literature. Here we propose an altered EB method, which is more robust and gives a more reasonable statistical interpretation. Our method is applied on both simulated and real data, and it outperforms the EB method.