title: Bayesian nonlinear model selection for gene regulatory networks.
abstract: Gene regulatory networks represent the regulatory relationships between genes and their products and are important for exploring and defining the underlying biological processes of cellular systems. We develop a novel framework to recover the structure of nonlinear gene regulatory networks using semiparametric spline-based directed acyclic graphical models. Our use of splines allows the model to have both flexibility in capturing nonlinear dependencies as well as control of overfitting via shrinkage, using mixed model representations of penalized splines. We propose a novel discrete mixture prior on the smoothing parameter of the splines that allows for simultaneous selection of both linear and nonlinear functional relationships as well as inducing sparsity in the edge selection. Using simulation studies, we demonstrate the superior performance of our methods in comparison with several existing approaches in terms of network reconstruction and functional selection. We apply our methods to a gene expression dataset in glioblastoma multiforme, which reveals several interesting and biologically relevant nonlinear relationships.
authors: Department of Statistics, Rice University, Houston, Texas, U.S.A.
authors: Yang
authors: Y
authors: Ni
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A.
authors: Francesco C
authors: FC
authors: Stingo
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A.
authors: Veerabhadran
authors: V
authors: Baladandayuthapani
mesh_terms: D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000235:genetics / Q000378:metabolism*; D003198:Computer Simulation; D015972:Gene Expression Regulation, Neoplastic / Q000502:physiology*; D053263:Gene Regulatory Networks / Q000502:physiology; D005909:Glioblastoma / Q000235:genetics / Q000378:metabolism*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000378:metabolism*; D017711:Nonlinear Dynamics; D015398:Signal Transduction / Q000235:genetics
chemical_list: D009363:Neoplasm Proteins
keywords: Directed acyclic graph; Gene regulatory network; Hierarchical model; MCMC; Model and functional selection; P-splines