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Bioinformatics Advance Access published online on January 31, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm019
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Gene Expression Network Analysis, and Applications to Immunology

Serban Nacu a,c,*, Rebecca Critchley-Thorne b, Peter Lee b and Susan Holmes a

aDepartment of Statistics, Stanford University, Stanford CA 94305, b Stanford School of Medicine, Stanford CA 94305 cEcole Normale Superieure, Paris, France

*to whom correspondence should be addressed.Serban Nacu, E-mail: serban{at}stat.stanford.edu


  Abstract

We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al. (2002), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and easier biological interpretation. We also assign significance levels to our results, adjusted for multiple testing. Our methods are succesfully applied to three human microarray data sets, related to cancer and the immune system, retrieving several known and potential pathways. The method, denoted by the acronym GXNA (Gene eXpression Network Analysis) is implemented in software that is publicly available and can be used on virtually any microarray data set.

Associate Editor: Martin Bishop

Received on August 5, 2006; revised on January 17, 2007; accepted on January 18, 2007

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