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PeerJ. 2013 Dec 19;1:e229. doi: 10.7717/peerj.229. eCollection 2013.

Pathway-GPS and SIGORA: identifying relevant pathways based on the over-representation of their gene-pair signatures.

Author information

1
Animal & Bioscience Research Department, AGRIC, Teagasc , Grange, Dunsany, Co. Meath , Ireland ; Department of Molecular Biology and Biochemistry, Simon Fraser University , Burnaby, British Columbia , Canada.
2
Department of Molecular Biology and Biochemistry, Simon Fraser University , Burnaby, British Columbia , Canada.
3
Animal & Bioscience Research Department, AGRIC, Teagasc , Grange, Dunsany, Co. Meath , Ireland.

Abstract

Motivation. Predominant pathway analysis approaches treat pathways as collections of individual genes and consider all pathway members as equally informative. As a result, at times spurious and misleading pathways are inappropriately identified as statistically significant, solely due to components that they share with the more relevant pathways. Results. We introduce the concept of Pathway Gene-Pair Signatures (Pathway-GPS) as pairs of genes that, as a combination, are specific to a single pathway. We devised and implemented a novel approach to pathway analysis, Signature Over-representation Analysis (SIGORA), which focuses on the statistically significant enrichment of Pathway-GPS in a user-specified gene list of interest. In a comparative evaluation of several published datasets, SIGORA outperformed traditional methods by delivering biologically more plausible and relevant results. Availability. An efficient implementation of SIGORA, as an R package with precompiled GPS data for several human and mouse pathway repositories is available for download from http://sigora.googlecode.com/svn/.

KEYWORDS:

Functional analysis; High-throughput data; Over-representation analysis; Pathway analysis; Shared components; Systems biology

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