Microarrays enable to measure the expression levels of tens of thousands of genes simultaneously. One important statistical question in such experiments is which of the several thousand genes are differentially expressed. Answering this question requires methods that can deal with multiple testing problems. One such approach is the control of the False Discovery Rate (FDR). Two recently developed methods for the identification of differentially expressed genes and the estimation of the FDR are the SAM (Significance Analysis of Microarrays) procedure and an empirical Bayes approach. In the two group case, both methods are based on a modified version of the standard t-statistic. However, it is also possible to use the Wilcoxon rank sum statistic. While there already exists a version of the empirical Bayes approach based on this rank statistic, we introduce in this paper a new version of SAM based on Wilcoxon rank sums. We furthermore compare these four procedures by applying them to simulated and real gene expression data.