Gene Set Enrichment Analysis Using Non-parametric Scores

Gene Set Enrichment Analysis (GSEA) is a well-known technique used for studying groups of functionally related genes and their correlation with phenotype. This method creates a ranked list of genes, which is used to calculate an enrichment score. In this work, we introduce two different metrics for gene ranking in GSEA, namely the Wilcoxon and the Baumgartner-Weiß-Schindler tests. The advantage of these metrics is that they do not assume any particular distribution on the data. We compared them with the signal-to-noise ratio metric originally proposed by the developers of GSEA on a type 2 diabetes mellitus (DM2) database. Statistical significance is evaluated by means of false discovery rate and p-value calculations. Results show that the Baumgartner-WeißSchindler test detects more pathways with statistical significance. One of them could be related to DM2, according to the literature, but further research is needed.