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

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

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

Irina Dinu,John D Potter,Thomas Mueller,Qi Liu,Adeniyi J Adewale,Gian S Jhangri,Gunilla Einecke,Konrad S Famulski,Philip Halloran,Yutaka Yasui,

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 detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with p53 mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of p53. Of the 31 gene sets, 11 actually involve p53 directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of p53 signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with p53. CONCLUSION: We conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.

Improving gene set analysis of microarray data by SAM-GS. Publishing Authors By Initials

I Dinu,JD Potter,T Mueller,Q Liu,AJ Adewale,GS Jhangri,G Einecke,KS Famulski,P Halloran,Y Yasui,

For similar biological phenomena, cell phenomena, and immunity: cell physiology: cell communication: signal transduction research abstracts see: biological phenomena, cell phenomena, and immunity: cell physiology: cell communication: signal transduction research

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

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: BMC bioinformatics

VOLUME: 8

Page Numbers: 242

Journal Abbreviation: BMC Bioinformatics

ISSN: 1471-2105

DAY: 5

MONTH: 07

YEAR: 2007

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

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LANGUAGE: eng

NlmUniqueID: 100965194

Improving gene set analysis of microarray data by SAM-GS. Keywords Mesh Terms:

KEYWORDS: Signal Transduction

MESH TERMS: physiology

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Substance Name: Proteome

Registry Number: 0

Grant and Affiliation Information for Improving gene set analysis of microarray data by SAM-GS.

AFFILIATION: Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>

Country: England

AGENCY: United States NCI

GRANT: CA074794

ACRONYM: CA

MEDLINETA: BMC Bioinformatics

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