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

Luigi Augugliaro, Antonino Abbruzzo, Ernst C. Wit, Veronica Vinciotti

Risultato della ricerca: Article

5 Citazioni (Scopus)

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.
Lingua originaleEnglish
pagine (da-a)193-212
Numero di pagine20
RivistaStatistical Applications in Genetics and Molecular Biology
Volume15
Stato di pubblicazionePublished - 2016

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Regulatory Networks
Dynamic Networks
Gaussian Model
Graphical Models
Factorial
Model Selection
Biological Phenomena
Neisseria meningitidis
Gene Regulatory Networks
Meningitis
Patient Selection
Penalized Maximum Likelihood
Model Selection Criteria
Time Lag
Gene Regulatory Network
Microarray Data
Bacteria
Sparsity
Absolute value
Network Model

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Genetics
  • Molecular Biology

Cita questo

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AU - Abbruzzo, Antonino

AU - Wit, Ernst C.

AU - Vinciotti, Veronica

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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.

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