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

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.