Issue No. 05 - Sept.-Oct. (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.118
Ting Chen , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
Ulisses M. Braga-Neto , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
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.
Statistical analysis, Bioinformatics, Stochastic processes, Boolean functions, Logic functions,Engineering, 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
Ting Chen, Ulisses M. Braga-Neto, "Statistical Detection of Boolean Regulatory Relationships", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. , pp. 1, Sept.-Oct. 2013, doi:10.1109/TCBB.2013.118