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B-MASTER: Scalable Bayesian Multivariate Regression for M...
[Submitted on 8 Dec 2024 (v1), last revised 1 Jun 2026 (this ver · 2026-06-02 · via stat updates on arXiv.org

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Abstract: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: The B-MASTER code, including demonstration scripts, is available at this https URL. An archived snapshot of the code corresponding to this manuscript is available on Zenodo with DOI: https://doi.org/10.5281/zenodo.20484958.

Submission history

From: Priyam Das [view email]
[v1] Sun, 8 Dec 2024 17:13:06 UTC (9,885 KB)
[v2] Tue, 27 May 2025 20:32:05 UTC (21,707 KB)
[v3] Fri, 12 Sep 2025 17:57:31 UTC (10,527 KB)
[v4] Mon, 1 Jun 2026 07:48:56 UTC (16,804 KB)