TY - JOUR
T1 - Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks
AU - Vinciotti, Veronica
AU - Augugliaro, Luigi
AU - Abbruzzo, Antonino
AU - Wit, Ernst C
PY - 2016/6/1
Y1 - 2016/6/1
N2 - 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.
AB - 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.
U2 - 10.1515/sagmb-2014-0075
DO - 10.1515/sagmb-2014-0075
M3 - Article
C2 - 27023322
VL - 15
SP - 193
EP - 212
JO - Statistical applications in genetics and molecular biology
JF - Statistical applications in genetics and molecular biology
SN - 1544-6115
IS - 3
ER -