title: Extracting three-way gene interactions from microarray data.
abstract: BACKGROUND
  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.
  
  
  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 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.
authors.affiliation: 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.
authors.forename: Jiexin
authors.initials: J
authors.lastname: Zhang
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction
chemical_list: D009363:Neoplasm Proteins