Improving gene set analysis of microarray data by SAM-GS.

ABSTRACT:

Background

Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).

Results

Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to dete

SUBMITTER: Dinu I 

PROVIDER: S-EPMC1931607 | biostudies-literature | 2007 Jul

REPOSITORIES: biostudies-literature