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Bioinformatics Advance Access published online on July 15, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth405
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received March 4, 2004
Revised May 25, 2004
Accepted July 6, 2004

Article

A graph theoretic approach to testing associations between disparate sources of functional genomics data

Raji Balasubramanian 1, Thomas LaFramboise 1*, Denise Scholtens 1, Robert Gentleman 1

1 Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115

* To whom correspondence should be addressed. E-mail: tlaframb{at}hsph.harvard.edu.


  Abstract

Motivation: The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology.

Results: We present a graph theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived predictome and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph theoretic framework to recast a surprising discrepancy presented in Giaever et al, (2002) between gene expression (Causton et al., 2001) and knockout phenotype, using expression data from a different set of experiments (Cho et al., 1998).

Availability: An R software package, GraphAT, containing the data and statistical procedures is available from Bioconductor: http://www.bioconductor.org.


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