title: Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors.
abstract: Metabolic processes are essential for cellular function and survival. We are interested in inferring a metabolic network in activated microglia, a major neuroimmune cell in the brain responsible for the neuroinflammation associated with neurological diseases, based on a set of quantified metabolites. To achieve this, we apply the Bayesian adaptive graphical lasso with informative priors that incorporate known relationships between covariates. To encourage sparsity, the Bayesian graphical lasso places double exponential priors on the off-diagonal entries of the precision matrix. The Bayesian adaptive graphical lasso allows each double exponential prior to have a unique shrinkage parameter. These shrinkage parameters share a common gamma hyperprior. We extend this model to create an informative prior structure by formulating tailored hyperpriors on the shrinkage parameters. By choosing parameter values for each hyperprior that shift probability mass toward zero for nodes that are close together in a reference network, we encourage edges between covariates with known relationships. This approach can improve the reliability of network inference when the sample size is small relative to the number of parameters to be estimated. When applied to the data on activated microglia, the inferred network includes both known relationships and associations of potential interest for further investigation.
authors.affiliation: Department of Statistics, MS 138 Rice University 6100 Main St. Houston, TX 77251-1892 USA.
authors.forename: Christine
authors.initials: C
authors.lastname: Peterson
authors.affiliation: Department of Statistics, MS 138 Rice University 6100 Main St. Houston, TX 77251-1892 USA.
authors.forename: Marina
authors.initials: M
authors.lastname: Vannucci
authors.affiliation: Departments of Pediatrics, Neuroscience, and Program in Developmental Biology Baylor College of Medicine Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital 1250 Moursund St., Suite N1250.16 Houston, TX 77030 USA.
authors.forename: Cemal
authors.initials: C
authors.lastname: Karakas
authors.affiliation: Departments of Pediatrics, Neuroscience, and Program in Developmental Biology Baylor College of Medicine Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital 1250 Moursund St., Suite N1250.16 Houston, TX 77030 USA.
authors.forename: William
authors.initials: W
authors.lastname: Choi
authors.affiliation: Departments of Pediatrics, Neuroscience, and Program in Developmental Biology Baylor College of Medicine Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital 1250 Moursund St., Suite N1250.16 Houston, TX 77030 USA.
authors.forename: Lihua
authors.initials: L
authors.lastname: Ma
authors.affiliation: Departments of Pediatrics, Neuroscience, and Program in Developmental Biology Baylor College of Medicine Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital 1250 Moursund St., Suite N1250.16 Houston, TX 77030 USA.
authors.forename: Mirjana
authors.initials: M
authors.lastname: Maletić-Savatić
keywords: Bayesian adaptive graphical lasso; Graphical models; Informative prior; Metabolic network; Neuroinflammation