DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.118
Ting Chen , Texas A&M University, College Station
Ulisses M. Braga-Neto , Texas A&M University, College Station
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.
Testing, Irrigation, Bioinformatics, Stochastic processes, Noise, Logic functions, IEEE transactions, Mathematics and statistics, Statistical, General, Model Validation and Analysis, Modeling methodologies, Discrete event, Monte Carlo, Engineering
U. M. Braga-Neto and T. Chen, "Statistical Detection of Boolean Regulatory Relationships," in IEEE/ACM Transactions on Computational Biology and Bioinformatics.