Extracting three-way gene interactions from microarray data.

Abstract

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 pairwise correlation as a distance metric. However, pairwise 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. 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 pairwise 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.