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B-MASTER: scalable Bayesian multivariate regression for master predictor discovery in colorectal cancer
Priyam Das1, Tanujit Dey2, Christine B Peterson3
1Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23219, United States.
Motivation:
The gut microbiome shapes cancer therapy response through its influence on host metabolism. While prior studies examine pairwise associations between individual genera and metabolites, there is limited methodology for identifying microbial genera that systematically regulate the overall metabolome. Scalable statistical tools are needed to uncover such system-level "master predictors" in high-dimensional microbiome-metabolome data.
Results:
We introduce B-MASTER, a scalable Bayesian multivariate regression framework combining ℓ1 sparsity and ℓ2 group shrinkage to identify essential cross-metabolite regulators. A Gibbs sampler enables near-linear computational scaling, supporting models with millions of parameters. The method is supported by theoretical guarantees, including posterior contraction and selection consistency. Analysis of colorectal cancer microbiome-metabolome data reveals key microbial genera that govern global and cancer-associated metabolite patterns, highlighting system-level regulatory structure.
Availability And Implementation:
The B-MASTER code, including demonstration scripts, is available at https://github.com/priyamdas2/B-MASTER. An archived snapshot of the code corresponding to this manuscript is available on Zenodo with DOI: 10.5281/zenodo.20484958.
