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

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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