Bayesian nonlinear model selection for gene regulatory networks
#MMPMID25854759
Ni Y
; Stingo FC
; Baladandayuthapani V
Biometrics
2015[Sep]; 71
(3
): 585-95
PMID25854759
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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.
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