Bayesian Inference of Multiple Gaussian Graphical Models

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dc.citation.firstpage159
dc.citation.issueNumber509
dc.citation.journalTitleJournal of the American Statistical Association
dc.citation.lastpage174
dc.citation.volumeNumber110
dc.contributor.authorPeterson, Christine B.
dc.contributor.authorStingo, Francesco C.
dc.contributor.authorVannucci, Marina
dc.date.accessioned2017-05-22T17:16:48Z
dc.date.available2017-05-22T17:16:48Z
dc.date.issued2015
dc.description.abstractIn this article, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior, which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the parameters that measure network relatedness. This approach allows us to share information between sample groups, when appropriate, as well as to obtain a measure of relative network similarity across groups. Our modeling framework incorporates relevant prior knowledge through an edge-specific informative prior and can encourage similarity to an established network. Through simulations, we demonstrate the utility of our method in summarizing relative network similarity and compare its performance against related methods. We find improved accuracy of network estimation, particularly when the sample sizes within each subgroup are moderate. We also illustrate the application of our model to infer protein networks for various cancer subtypes and under different experimental conditions.
dc.identifier.citationPeterson, Christine B., Stingo, Francesco C. and Vannucci, Marina. "Bayesian Inference of Multiple Gaussian Graphical Models." <i>Journal of the American Statistical Association,</i> 110, no. 509 (2015) Taylor & Francis: 159-174. http://dx.doi.org/10.1080/01621459.2014.896806.
dc.identifier.doihttp://dx.doi.org/10.1080/01621459.2014.896806
dc.identifier.urihttps://hdl.handle.net/1911/94322
dc.language.isoeng
dc.publisherTaylor & Francis
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Taylor & Francis.
dc.subject.keywordBayesian inference
dc.subject.keywordG-Wishart prior
dc.subject.keywordGaussian graphical model
dc.subject.keywordMarkov random field
dc.subject.keywordprotein network
dc.titleBayesian Inference of Multiple Gaussian Graphical Models
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpost-print
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