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Bioinformatics Advance Access originally published online on November 5, 2008
Bioinformatics 2009 25(1):75-82; doi:10.1093/bioinformatics/btn577
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A novel signaling pathway impact analysis

Adi Laurentiu Tarca 1,2, Sorin Draghici 1,*, Purvesh Khatri 1, Sonia S. Hassan 2, Pooja Mittal 2, Jung-sun Kim 2, Chong Jai Kim 2, Juan Pedro Kusanovic 2 and Roberto Romero 2

1Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202 and 2Perinatology Research Branch-NIH/NICHD, 4 Brush, 3990 John R, Detroit, MI 48201, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe.

Results: We describe a novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance P-value, which combines the enrichment and perturbation P-values. We illustrate the capabilities of the novel method on four real datasets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods.

Availability: SPIA was implemented as an R package available at http://vortex.cs.wayne.edu/ontoexpress/

Contact: sorin{at}wayne.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: John Quackenbush

Received on July 28, 2008; revised on October 29, 2008; accepted on November 4, 2008

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