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Bayesian Modeling on Microbiome Data Analysis: Application to Subgingival Microbiome Study.

Yeongjin Gwon1,2, Fang Yu1, Jeffrey B Payne3,4

  • 1Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, 984375 Nebraska Medical Center, Omaha, NE 68198‑4375, USA.

Statistics in Biosciences
|April 22, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model for microbiome data analysis, improving the understanding of links between microbes and diseases. The refined model enhances accuracy in analyzing complex microbiome data, aiding in diagnostics and therapeutics.

Keywords:
Bayesian ZINBMCMCMarginalizationMicrobiomePólya-gamma mixture

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Area of Science:

  • Microbiome Research
  • Statistical Modeling
  • Bioinformatics

Background:

  • Microbiome data analysis is crucial for understanding human diseases.
  • Microbiome data presents unique challenges: compositional nature, excess zeros, and overdispersion.
  • Existing models struggle to fully capture subject-level variations in microbiome studies.

Purpose of the Study:

  • To develop an advanced statistical model for microbiome data analysis.
  • To quantify covariate-taxa effects in subgingival microbiome studies.
  • To address challenges like zero-inflation and overdispersion while accounting for subject heterogeneity.

Main Methods:

  • Proposed a refined Bayesian zero-inflated negative binomial (ZINB) regression model with random subject effects.
  • Developed an efficient Markov chain Monte Carlo (MCMC) sampling algorithm for Bayesian computation.
  • Applied the model to a real subgingival microbiome dataset.

Main Results:

  • The proposed model effectively handles zero-inflation and overdispersion.
  • Incorporation of random subject effects accounts for subject-level heterogeneity.
  • Simulation studies confirmed the model's superior performance in power and type I error control compared to existing methods.

Conclusions:

  • The refined Bayesian ZINB model offers a robust approach for microbiome data analysis.
  • This method improves the identification of associations between microbial taxa and health outcomes.
  • The model has significant potential for developing new diagnostics and therapeutics.