The null hypothesis of GSEA, and a novel statistical model for competitive gene set analysis

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Competitive gene set analysis intends to assess whether a specific set of genes is more associated with a trait than the remaining genes. However, the statistical models assumed to date to underly these methods do not enable a clear cut formulation of the competitive null hypothesis. This is a major handicap to the interpretation of results obtained from a gene set analysis.


This work presents a hierarchical statistical model based on the notion of dependence measures, which overcomes this problem. The two levels of the model naturally reflect the modular structure of many gene set analysis methods. We apply the model to show that the popular GSEA method, which recently has been claimed to test the self-contained null hypothesis, actually tests the competitive null if the weight parameter is zero. However, for this result to hold strictly, the choice of the dependence measures underlying GSEA and the estimators used for it is crucial.


Supplementary information:

Supplementary material is available at Bioinformatics online.

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