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We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.

Original publication

DOI

10.1093/biostatistics/kxr009

Type

Journal article

Journal

Biostatistics

Publication Date

10/2011

Volume

12

Pages

682 - 694

Keywords

Algorithms, Arabidopsis, Bayes Theorem, Biostatistics, Circadian Rhythm, Gene Regulatory Networks, Genome, Plant, Linear Models, Markov Chains, Models, Genetic, Models, Statistical, Nonlinear Dynamics, Oligonucleotide Array Sequence Analysis