Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential.

ABSTRACT: In this article, we focus on the analysis of competitive gene set methods for detecting the statistical significance of pathways from gene expression data. Our main result is to demonstrate that some of the most frequently used gene set methods, GSEA, GSEArot and GAGE, are severely influenced by the filtering of the data in a way that such an analysis is no longer reconcilable with the principles of statistical inference, rendering the obtained results in the worst case inexpressive. A possible consequence of this is that these methods can increase their power by the addition of unrelated data and noise. Our results are obtained within a bootstrapping framework that allows a rigorous assessment of the robustness of results and enables power estimates. Our results indicate that when using c

SUBMITTER: Tripathi S 

PROVIDER: S-EPMC3627569 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature