Irina Dinu¹, Qi Liu¹, John D. Potter², Adeniyi J. Adewale¹, Gian S. Jhangri¹, Thomas Mueller³, Gunilla Einecke³, Konrad Famulsky³, Philip Halloran³ and Yutaka Yasui¹

¹School of Public Health, University of Alberta, 13-106 Clinical Sciences Building, Edmonton, AB, Canada T6G 2G3.  ²Division of Public Health Sciences, Fred Hutchinson Cancer Research Center,1100 Fairview Ave. N., Seattle, WA, U.S.A. 98109.  ³Division of Nephrology and Transplan- tation Immunology, University of Alberta, 250 Heritage Medical Research Center, Edmonton, AB Canada T6G 2S2.

Abstract

Gene-set analysis of microarray data evaluates biological pathways, or gene sets, for their differential expression by a phenotype of interest. In contrast to the analysis of individual genes, gene-set analysis utilizes existing biological knowledge of genes and their pathways in assessing differential expression. This paper evaluates the biological performance of five gene-set analysis methods testing “self-contained null hypotheses” via subject sampling, along with the most popular gene-set analysis method, Gene Set Enrichment Analysis (GSEA). We use three real microarray analyses in which differentially expressed gene sets are predictable biologically from the phenotype. Two types of gene sets are considered for this empirical evaluation: one type contains “truly positive” sets that should be identified as differentially expressed; and the other type contains “truly negative” sets that should not be identified as differentially expressed. Our evaluation suggests advantages of SAM-GS, Global, and ANCOVA Global methods over GSEA and the other two methods.