Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors

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dc.citation.firstpage547en_US
dc.citation.journalTitleStatistics and Its Interfaceen_US
dc.citation.lastpage558en_US
dc.citation.volumeNumber6en_US
dc.contributor.authorPeterson, Christineen_US
dc.contributor.authorVannucci, Marinaen_US
dc.contributor.authorKarakas, Cemalen_US
dc.contributor.authorChoi, Williamen_US
dc.contributor.authorMa, Lihuaen_US
dc.contributor.authorMaletic-Savatic, Mirjanaen_US
dc.date.accessioned2013-12-20T19:56:51Zen_US
dc.date.available2013-12-20T19:56:51Zen_US
dc.date.issued2013en_US
dc.description.abstractMetabolic 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.en_US
dc.identifier.citationPeterson, Christine, Vannucci, Marina, Karakas, Cemal, et al.. "Inferring metabolic networks using the Bayesian adaptive graphical lasso with informative priors." <i>Statistics and Its Interface,</i> 6, (2013) International Press: 547-558. http://dx.doi.org/10.4310/SII.2013.v6.n4.a12.en_US
dc.identifier.doihttp://dx.doi.org/10.4310/SII.2013.v6.n4.a12en_US
dc.identifier.urihttps://hdl.handle.net/1911/75306en_US
dc.language.isoengen_US
dc.publisherInternational Pressen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.subject.keywordgraphical modelsen_US
dc.subject.keywordBayesian adaptive graphical lassoen_US
dc.subject.keywordinformative prioren_US
dc.subject.keywordmetabolic networken_US
dc.subject.keywordneuroinflammationen_US
dc.titleInferring metabolic networks using the Bayesian adaptive graphical lasso with informative priorsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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