ArticleBiostatistics (Oxford, England)2019
Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis.
Article in Biostatistics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Melody: meta-analysis of microbiome association studies for discovering generalizable microbial signatures.Genome biology · 2025Pooled it
- A joint mixture Tobit method with latent microbial abundance improves detection of microbiome-disease associations in zero-inflated data.PLoS computational biology · 2026Article
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- ABaCo: addressing heterogeneity challenges in metagenomic data integration with adversarial generative models.Nucleic acids research · 2026Article
- A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.Biostatistics (Oxford, England) · 2026Article
- Markov switching zero-inflated space-time multinomial models for comparing multiple infectious diseases.Biostatistics (Oxford, England) · 2025Article
- Article
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- A New Dirichlet-Multinomial Mixture Regression Model for the Analysis of Microbiome Data.Statistics in medicine · 2025Article
- Article
- Bayesian Modeling on Microbiome Data Analysis: Application to Subgingival Microbiome Study.Statistics in biosciences · 2024Article
- A LATENT VARIABLE MIXTURE MODEL FOR COMPOSITION-ON-COMPOSITION REGRESSION WITH APPLICATION TO CHEMICAL RECYCLING.The annals of applied statistics · 2024Article
- Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.eLife · 2024Review
- Identifying microbial drivers in biological phenotypes with a Bayesian network regression model.Ecology and evolution · 2024Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data.Frontiers in microbiology · 2024Article
- Bayesian Variable Shrinkage and Selection in Compositional Data Regression: Application to Oral Microbiome.Journal of the Indian Society for Probability and Statistics · 2024Article
- Microbiota DNA isolation, 16S rRNA amplicon sequencing, and bioinformatic analysis for bacterial microbiome profiling of rodent fecal samples.STAR protocols · 2022Article
- A Bayesian method for identifying associations between response variables and bacterial community composition.PLoS computational biology · 2022Article
- Article
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2 authors.
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Abstract
There is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts and in flexibly accommodating complex correlation structures and dispersion patterns among taxa. In this article, we develop a new probability distribution, zero-inflated generalized Dirichlet multinomial (ZIGDM), that overcomes these limitations in modeling multivariate taxon counts. Based on this distribution, we propose a ZIGDM regression model to link microbial abundances to covariates (e.g. disease status) and develop a fast expectation-maximization algorithm to efficiently estimate parameters in the model. The derived tests enable us to reveal rich patterns of variation in microbial compositions including differential mean and dispersion. The advantages of the proposed methods are demonstrated through simulation studies and an analysis of a gut microbiome dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.