A Biological Evaluation of Six Gene Set Analysis Methods for Identification of Differentially Expressed Pathways in Microarray Data

• Main Author: Dinu, Irina
• Other Authors: Irina Dinu, Qi Liu, John D. Potter, Adeniyi J. Adewale, Gian S. Jhangri, Thomas Mueller, Gunilla Einecke, Konrad Famulsky, Philip Halloran and Yutaka Yasui
• Language: English
• Published: Libertas Academica 2008
• Subjects: Cancer Informatics; Volume: 2008; Issue: 1; Canada; Fairview
• Online Access: http://www.la-press.com/redirect_file.php?fileId=1207&filename=CIN-6-Dinu-et-al&fileType=pdf

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.