Comparative evaluation of gene-set analysis methods.

ABSTRACT:

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 chi2 distribution of Global Te

SUBMITTER: Liu Q 

PROVIDER: S-EPMC2238724 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature