Bayesian Nonparametric Ordination for the Analysis of Microbial Communities

Area of Science:

• Microbial Ecology
• Computational Biology
• Statistical Modeling

Background:

• Human microbiome studies analyze bacterial abundance using sequencing data, often in contingency tables.
• Ordination methods are crucial for identifying patterns and clusters in microbial community structures.
• Quantifying uncertainty in microbial distribution estimates is vital for reliable ordination analysis.

Purpose of the Study:

• To develop a Bayesian method for incorporating uncertainty estimates into microbiome ordination analyses.
• To improve the visualization of microbial sample variations and clusters.
• To provide a robust framework for analyzing complex microbiome datasets.

Main Methods:

• A Bayesian nonparametric prior for dependent normalized random measures was constructed.
• Latent factors representing microbial distribution similarity were incorporated.
• A shrinkage prior was used to optimize the dimensionality of latent factors.
• Posterior samples were used to evaluate uncertainty in ordination plots.

Main Results:

• The proposed Bayesian approach successfully quantifies uncertainty in microbiome ordination.
• Credible regions were visualized in ecological ordination plots, enhancing interpretation.
• The method demonstrated effectiveness in a simulation study and two real microbiome datasets.

Conclusions:

• The Bayesian analysis for dependent distributions provides a powerful tool for microbiome research.
• This method enhances the reliability and interpretability of ordination results.
• It offers a novel way to visualize uncertainty in microbial community analyses.