A permutation test motivated by microarray data analysis

We introduce a nonparametric test intended for large-scale simultaneous inference in situations where the utility of distribution-free tests is limited because of their discrete nature. Such situations are frequently dealt with in microarray analysis where the number of tests is much larger than the sample size. The proposed test statistic is based on a certain distance between the distributions from which the samples under study are drawn. In a simulation study, the proposed permutation test is compared with permutation counterparts of the t-test and the Kolmogorov–Smirnov test. The usefulness of the proposed test is discussed in the context of microarray gene expression data and illustrated with an application to real datasets.