Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach

High-dimensional case-control analysis is encountered in many different settings in genomics. In order to rank genes accordingly, many different scores have been proposed, ranging from ad hoc modifications of the ordinary t statistic to complicated hierarchical Bayesian models. Here, we introduce the shrinkage t statistic that is based on a novel and model-free shrinkage estimate of the variance vector across genes. This is derived in a quasi-empirical Bayes setting. The new rank score is fully automatic and requires no specification of parameters or distributions. It is computationally inexpensive and can be written analytically in closed form. Using a series of synthetic and three real expression data we studied the quality of gene rankings produced by the shrinkage t statistic. The new score consistently leads to highly accurate rankings for the complete range of investigated data sets and all considered scenarios for across-gene variance structures.