Comments on selected fundamental aspects of microarray analysis
Computational Biology and Chemistry
Gene set enrichment analysis using non-parametric scores
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
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Motivation: An important application of microarray experiments is to identify differentially expressed genes. Because microarray data are often not distributed according to a normal distribution nonparametric methods were suggested for their statistical analysis. Here, the Baumgartner-Weiß-Schindler test, a novel and powerful test based on ranks, is investigated and compared with the parametric t-test as well as with two other nonparametric tests (Wilcoxon rank sum test, Fisher-Pitman permutation test) recently recommended for the analysis of gene expression data. Results: Simulation studies show that an exact permutation test based on the Baumgartner-Weiß-Schindler statistic B is preferable to the other three tests. It is less conservative than the Wilcoxon test and more powerful, in particular in case of asymmetric or heavily tailed distributions. When the underlying distribution is symmetric the differences in power between the tests are relatively small. Thus, the Baumgartner-Weiß-Schindler is recommended for the usual situation that the underlying distribution is a priori unknown. Availability: SAS code available on request from the authors.