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

Author:

Dinu Irina1,Liu Qi1,Potter John D.2,Adewale Adeniyi J.1,Jhangri Gian S.1,Mueller Thomas3,Einecke Gunilla3,Famulsky Konrad3,Halloran Philip3,Yasui Yutaka1

Affiliation:

1. School of Public Health, University of Alberta, 13-106 Clinical Sciences Building, Edmonton, AB, Canada T6G 2G3.

2. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, WA, U.S.A. 98109.

3. Division of Nephrology and Transplantation 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.

Publisher

SAGE Publications

Subject

Cancer Research,Oncology

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