Bayesian inference of networks across multiple sample groups and data types.
In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple...
| Publicado en: | Biostatistics Vol. 21; no. 3; pp. 561 - 577 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144206957&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144206957 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Jul2020 vid: 21 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 144206957 144206957 NLM30590505 144206957 10.1093/biostatistics/kxy078 NLM30590505 144206957 ppf: 561 ppct: 16 formats: tig: atl: Bayesian inference of networks across multiple sample groups and data types. aug: au: Shaddox, Elin Peterson, Christine B Stingo, Francesco C Hanania, Nicola A Cruickshank-Quinn, Charmion Kechris, Katerina Bowler, Russell Vannucci, Marina affil: Department of Statistics, Rice University , Houston, TX, USA sug: subj: Research, Medical Methods Statistics Methods Data Analysis, Statistical Models, Statistical Pulmonary Disease, Chronic Obstructive Metabolism Physiology Pulmonary Disease, Chronic Obstructive Metabolism Severity of Illness Indices Computer Simulation Probability Gene Expression Physiology Data Collection Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of chronic obstructive pulmonary disease. Bayesian inference; Chronic obstructive pulmonary disease (COPD); Data integration; Gaussian graphical model; Markov random field prior; Spike and slab prior. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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