Bioinformatics Advance Access published online on October 5, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm482
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Extracting Three-way Gene Interactions from Microarray Data
Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 237, Houston, TX 77030-4009, USA.
*To whom correspondence should be addressed. Dr. Li Zhang, E-mail: lzhangli{at}mdanderson.org
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Abstract |
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Motivation: It is an important and difficult task to extract gene network information from high throughput genomic data. A common approach is to cluster genes using pair-wise correlation as a distance metric. However, pair-wise correlation is clearly too simplistic to describe the complex relationships among real genes since co-expression relationships are often restricted to a specific set of biological conditions/processes. In this study, we described a three-way gene interaction model that captures the dynamic nature of co-expression relationship between a gene pair through the introduction of a controller gene.
Results: We surveyed 0.4 billion possible three-way interactions among 1000 genes in a microarray dataset containing 678 human cancer samples. To test the reproducibility and statistical significance of our results, we randomly split the samples into a training set and a testing set. We found that the gene triplets with the strongest interactions (i.e., with the smallest p-values from appropriate statistical tests) in the training set also had the strongest interactions in the testing set. A distinctive pattern of three-way interaction emerged from these gene triplets: depending on the third gene being expressed or not, the remaining two genes can be either co-expressed or mutually exclusive (i.e., expression of either one of them would repress the other). Such three-way interactions can exist without apparent pair-wise correlations. The identified three-way interactions may constitute candidates for further experimentation using techniques such as RNA interference, so that novel gene network or pathways could be identified.
Contact: lzhangli{at}mdanderson.org
Associate Editor: Prof. David Rocke
Received on April 28, 2007; revised on September 16, 2007; accepted on September 23, 2007
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