An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.

Wadsworth, W Duncan; Argiento, Raffaele; Guindani, Michele; Galloway-Pena, Jessica; Shelburne, Samuel A; Vannucci, Marina

Wadsworth, W Duncan; Argiento, Raffaele; Guindani, Michele; Galloway-Pena, Jessica; Shelburne, Samuel A; Vannucci, Marina.

Afiliação

Wadsworth WD; Department of Statistics, Rice University, Houston, TX, USA.
Argiento R; ESOMAS Department, University of Torino and Collegio Carlo Alberto, Torino, Italy.
Guindani M; Department of Statistics, University of California, Irvine, CA, USA.
Galloway-Pena J; Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA.
Shelburne SA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA.
Vannucci M; Department of Statistics, Rice University, Houston, TX, USA. marina@rice.edu.

BMC Bioinformatics ; 18(1): 94, 2017 02 08.

Article em En | MEDLINE | ID: mdl-28178947

Assuntos

Bactérias/classificação; Modelos Lineares; Microbiota; Algoritmos; Teorema de Bayes; Simulação por Computador; Humanos; Cadeias de Markov; Método de Monte Carlo

Palavras-chave

Bayesian hierarchical model; Data integration; Dirichlet-multinomial; Microbiome data; Variable selection

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bactérias / Modelos Lineares / Microbiota Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

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