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Bioinformatics Advance Access originally published online on September 21, 2007
Bioinformatics 2007 23(22):3100-3102; doi:10.1093/bioinformatics/btm460
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

TAPPA: topological analysis of pathway phenotype association

Shouguo Gao * and Xujing Wang

The Max McGee National Research Center for Juvenile Diabetes & The Human and Molecular Genetics Center, The Medical College of Wisconsin and Children's Hospital of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA

*To whom correspondence should be addressed.


  Abstract

Summary: Extracting biological insight from microarray data is important but challenging. Here we describe TAPPA, a java-based tool, for identification of phenotype-associated genetic pathways utilizing the pathway topological measures. This is achieved by first calculating a Pathway Connectivity Index (PCI) for each pathway, followed by evaluating its correlation to the phenotypic variation. Our PCI definition not only efficiently captures the contributions from genes that show subtle but consistent changes in expression, but also naturally overweighs the hub genes that interact with a large number of other genes in the pathway. TAPPA also allows evaluation of sub-modules within a pathway and their association to phenotypes.

Availability: TAPPA and data for Figure 1 are freely available from http://watson.mcgee.mcw.edu:8080/~sgao

Contact: sgao{at}mcw.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Olga Troyanskaya

Received on January 25, 2007; revised on September 1, 2007; accepted on September 5, 2007

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