Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis

DOI10.1214/19-AOAS1295zbMath1439.62225arXiv1711.01241OpenAlexW3017376615MaRDI QIDQ2179984

Boyu Ren, Tommi Vatanen, Stefano Favaro, Sergio Bacallado, Lorenzo Trippa, Curtis Huttenhower

Publication date: 13 May 2020

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.01241

zbMATH Keywords

latent factor modeltype 1 diabetestruncated dependent Dirichlet processes

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20) Causal inference from observational studies (62D20)

Related Items (4)

Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis ⋮ Microbiome Subcommunity Learning with Logistic-Tree Normal Latent Dirichlet Allocation ⋮ Subject-specific Dirichlet-multinomial regression for multi-district microbiota data analysis ⋮ Bayesian sparse multivariate regression with asymmetric nonlocal priors for microbiome data analysis

Uses Software

• Bioconductor
• edgeR
• DEseq
• MIMIX

Cites Work

• Unnamed Item
• Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
• Bayesian inference with dependent normalized completely random measures
• Bayesian clustering of animal abundance trends for inference and dimension reduction
• Probability in Banach spaces. Isoperimetry and processes
• Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis
• Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis
• Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity
• A Bayesian analysis of some nonparametric problems
• Modern multidimensional scaling. Theory and applications.
• Nonparametric Bayesian models through probit stick-breaking processes
• Sparse Bayesian infinite factor models
• Hierarchical Dirichlet Processes
• Posterior Analysis for Normalized Random Measures with Independent Increments
• Exact and approximate sum representations for the Dirichlet process
• MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments
• Bayesian Analysis of Binary and Polychotomous Response Data
• A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis
• Comparing Distributions by using Dependent Normalized Random-Measure Mixtures

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