ArticleBiostatistics (Oxford, England)2021
A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data.
Article in Biostatistics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 37 citations in OpenAlex.
- A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.Biostatistics (Oxford, England) · 2026Article
- Persistent gut microbiota dysbiosis and metabolic remodeling after ceftriaxone exposure in mice: a cross-study re-analysis.Frontiers in cellular and infection microbiology · 2026Article
- Artificial Intelligence-Driven Food Safety: Decoding Gut Microbiota-Mediated Health Effects of Non-Microbial Contaminants.Foods (Basel, Switzerland) · 2025Review
- Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model.BMC bioinformatics · 2025Article
- A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.Statistics in medicine · 2025Article
- A regularized Bayesian Dirichlet-multinomial regression model for integrating single-cell-level omics and patient-level clinical study data.Biometrics · 2025Article
- Article
- Bayesian Modeling on Microbiome Data Analysis: Application to Subgingival Microbiome Study.Statistics in biosciences · 2024Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- A Regularized Bayesian Dirichlet-multinomial Regression Model for Integrating Single-cell-level Omics and Patient-level Clinical Study Data.bioRxiv : the preprint server for biology · 2024Article
- fastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data.Bioinformatics (Oxford, England) · 2024Article
- A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data.Frontiers in microbiology · 2024Article
- Structure-adaptive canonical correlation analysis for microbiome multi-omics data.Frontiers in genetics · 2024Article
- Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep.bioRxiv : the preprint server for biology · 2023Article
- Overview of data preprocessing for machine learning applications in human microbiome research.Frontiers in microbiology · 2023Review
- Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.Methods in molecular biology (Clifton, N.J.) · 2023Review
- A Survey of Statistical Methods for Microbiome Data Analysis.Frontiers in applied mathematics and statistics · 2022Article
- Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data.Cancers · 2021Review
- MB-GAN: Microbiome Simulation via Generative Adversarial Network.GigaScience · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Microbiome omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics, and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Hence, we present an integrative Bayesian zero-inflated negative binomial regression model that can both distinguish differentially abundant taxa with distinct phenotypes and quantify covariate-taxa effects. Our model demonstrates good performance using simulated data. Furthermore, we successfully integrated microbiome taxonomies and metabolomics in two real microbiome datasets to provide biologically interpretable findings. In all, we proposed a novel integrative Bayesian regression model that features bacterial differential abundance analysis and microbiome-covariate effects quantifications, which makes it suitable for general microbiome studies.
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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.