Bayesian latent variable models for hierarchical clustered count outcomes with repeated measures in microbiome...

Area of Science:

• Microbiology
• Statistical Modeling
• Bioinformatics

Background:

• Microbiome data presents challenges: multivariate nature, hierarchical structures, skewed and zero-inflated counts, and repeated measures.
• Existing methods may not adequately address the complexity of jointly modeling multiple operational taxonomic units (OTUs) within taxonomic clusters.
• Accounting for serial and familial correlations is crucial for accurate microbiome analysis.

Purpose of the Study:

• To propose a novel Bayesian latent variable methodology for the joint modeling of multiple OTUs within a single taxonomic cluster.
• To develop a flexible statistical framework capable of handling negative binomial and zero-inflated negative binomial response distributions.
• To incorporate serial and familial correlations inherent in microbiome data.

Main Methods:

• Developed a Bayesian latent variable model for joint analysis of multiple OTUs.
• Implemented a Markov chain Monte Carlo (MCMC) algorithm utilizing a data augmentation scheme with Pólya-Gamma random variables.
• Employed hierarchical centering and parameter expansion techniques to enhance MCMC convergence.

Main Results:

• The proposed Bayesian method demonstrated robust performance in extensive simulation studies.
• The methodology successfully incorporated various count distributions (negative binomial, zero-inflated negative binomial) and correlation structures.
• The method was successfully applied to a real-world human microbiome study, yielding valuable insights.

Conclusions:

• The proposed Bayesian latent variable methodology offers a powerful and flexible approach for analyzing complex, multivariate microbiome data.
• The method effectively addresses challenges associated with count distributions and correlation structures, improving the analysis of microbial communities.
• This approach provides a valuable tool for researchers in microbiome studies, enabling more nuanced understanding of microbial ecology and host interactions.