Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression
Morrissey, Edward R., Juárez, Miguel A., Denby, Katherine J. and Burroughs, Nigel John. (2011) Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression. Biostatistics, Vol.12 (No.4). pp. 682-694. ISSN 1465-4644Full text not available from this repository.
Official URL: http://dx.doi.org/10.1093/biostatistics/kxr009
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.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Science > Life Sciences (2010- )
Faculty of Science > Mathematics
Faculty of Science > Centre for Systems Biology
|Journal or Publication Title:||Biostatistics|
|Publisher:||Oxford University Press|
|Number of Pages:||13|
|Page Range:||pp. 682-694|
|Access rights to Published version:||Restricted or Subscription Access|
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