Bayesian Graphical Compositional Regression for Microbiome Data

DOI10.1080/01621459.2019.1647212zbMath1445.62281arXiv1712.04723OpenAlexW2964678546MaRDI QIDQ5130601

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Publication date: 28 October 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1712.04723

zbMATH Keywords

phylogenygraphical modelscompositional datahierarchical modelsDirichlet-tree model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic graphical models (62H22) Microbiology (92C70)

Related Items (5)

A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data ⋮ Dirichlet-tree multinomial mixtures for clustering microbiome compositions ⋮ Microbiome Subcommunity Learning with Logistic-Tree Normal Latent Dirichlet Allocation ⋮ Bayesian modeling of interaction between features in sparse multivariate count data with application to microbiome study ⋮ A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes

Uses Software

• PhyloScan
• MIMIX

Cites Work

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• MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments
• Probabilistic Multi-Resolution Scanning for Two-Sample Differences
• A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis
• A Dirichlet-Tree Multinomial Regression Model for Associating Dietary Nutrients with Gut Microorganisms

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