title: Statistical detection of boolean regulatory relationships.
abstract: A statistic tool for the detection of multivariate Boolean relationships is presented, with applications in the inference of gene regulatory mechanisms. A statistical test is developed for the detection of a nonzero discrete coefficient of determination (CoD) between predictor and target variables. This is done by framing the problem in the context of a stochastic logic model that naturally allows the inclusion of prior knowledge if available. The rejection region, p-value, statistical power, and confidence interval are derived and analyzed. Furthermore, the issue of multiplicity of tests due to presence of numerous candidate genes and logic relationships is addressed via FWER- and FDR-controlling approaches. The methodology is demonstrated by experiments using synthetic data and real data from a study on ionizing radiation (IR)-responsive genes. The results indicate that the proposed methodology is a promising tool for detection of gene regulatory relationships from gene-expression data. Software that implements the COD test is available online as an R package.
authors.affiliation: Texas A&MUniversity, College Station.
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
authors.affiliation: Texas A&MUniversity, College Station.
authors.forename: Ulisses M
authors.initials: UM
authors.lastname: Braga-Neto
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000235:genetics*; D016015:Logistic Models; D008957:Models, Genetic; D020543:Proteome / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics*
chemical_list: D020543:Proteome