ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2021
A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model.BMC bioinformatics · 2025Article
- Sparse Bayesian Group Factor Model for Feature Interactions in Multiple Count Tables Data.Journal of the American Statistical Association · 2025Article
- Article
- Bayesian clustering of multiple zero-inflated outcomes.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
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4 authors.
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Abstract
High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. We develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.
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Registered trials
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