Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks

Vinciotti Veronica, Augugliaro Luigi, Abbruzzo Antonino and Wit Ernst C. ()
Additional contact information
Vinciotti Veronica: Department of Mathematics, Brunel University London, Uxbridge UB8 3PH, UK
Augugliaro Luigi: Statistics, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Abbruzzo Antonino: Statistics, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Wit Ernst C.: Johann Bernoulli Institute, University of Groningen, 9747 AG Groningen, The Netherlands

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 3, 193-212

Abstract: Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.

Keywords: gene-regulatory systems; graphical models; penalized inference; sparse networks (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2014-0075 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:15:y:2016:i:3:p:193-212:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2014-0075

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().