Comparative evaluation of gene-set analysis methods

Background: Multiple data-analytic methods have been proposed for evaluating gene-expression levels in specific biological pathways, assessing differential expression associated with a binary phenotype. Following Goeman and Bühlmann's recent review, we compared statistical performance of three methods, namely Global Test, ANCOVA Global Test, and SAM-GS, that test "self-contained null hypotheses" Via. subject sampling. The three methods were compared based on a simulation experiment and analyses of three real-world microarray datasets. Results: In the simulation experiment, we found that the use of the asymptotic distribution in the two Global Tests leads to a statistical test with an incorrect size. Specifically, p-values calculated by the scaled χ2 distribution of Global Test and the asymptotic distribution of ANCOVA Global Test are too liberal, while the asymptotic distribution with a quadratic form of the Global Test results in p-values that are too conservative. T...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Qi Liu, Irina Dinu, Adeniyi J. Adewale, John D. Potter, Yutaka Yasui
• Keywords: Asymptotic Distribution, BMCBI 2007, Gene Expression, Global Tests