Bayesian Group Shrinkage model to Identify the Key Genera in Microbiome-Metabolite Relation Dynamics

Music City Center 

The gut microbiome influences cancer therapy responses, particularly immunotherapies, by shaping the metabolome. While some studies examine specific microbial genera and metabolites, little work identifies key genera driving overall metabolome profiles. To address this, we introduce B-MASTER (Bayesian Multivariate Analysis for Selecting Targeted Essential Regressors), a fully Bayesian framework with L1 and L2 penalties for sparsity and shrinkage, paired with a scalable Gibbs sampler. B-MASTER enables full posterior inference for models with up to four million parameters efficiently. Using this approach, we identify key microbial genera shaping metabolite profiles and analyze their relevance to colorectal cancer.

Bayesian penalized regression,

Gibbs sampling

Scalable high-dimensional models

Microbiome-metabolites dynamics

Colorectal cancer. 

Biometrics Section