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Bioinformatics Advance Access published online on July 28, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl401
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received February 9, 2006
Revised June 27, 2006
Accepted July 18, 2006

Article

A multivariate approach for integrating genome-wide expression data and biological knowledge

Sek Won Kong 1, William T. Pu 2, and Peter J. Park 3 *

1 Department of Cardiology, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA; Informatics Program, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA
2 Department of Cardiology, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA
3 Informatics Program, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA; Harvard-Partners Center for Genetics and Genomics, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

* To whom correspondence should be addressed.
Peter J. Park, E-mail: peter_park{at}harvard.edu


  Abstract

Motivation: Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions.

Results: We present a simple yet effective multivariate statistical procedure for assessing the correlation between a subspace defined by a group of genes and a binary phenotype. A subspace is deemed significant if the samples corresponding to different phenotypes are well separated in that subspace. The separation is measured using Hotelling's T2 statistic, which captures the covariance structure of the subspace. When the dimension of the subspace is larger than that of the sample space, we project the original data to a smaller orthonormal subspace. We use this method to search through functional pathway subspaces defined by Reactome, KEGG, BioCarta, and Gene Ontology. To demonstrate its performance, we apply this method to the data from two published studies, and we visualize the results in the principal component space.

Associate Editor: Alvis Brazma
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