Bayesian Sparse Regression for Microbiome-Metabolite Data Integration

TL;DR

This paper introduces a Bayesian regression model designed to integrate microbiome and metabolite data, effectively handling missing values and compositional constraints, with demonstrated accuracy on simulated and real colorectal cancer data.

Contribution

The paper presents a novel Bayesian approach that models missingness and compositionality in microbiome-metabolite data integration, improving variable selection and imputation.

Findings

Accurately imputes missing metabolite values in simulated data.

Correctly identifies relevant microbiome predictors.

Effectively applied to colorectal cancer study data.

Abstract

Numerous studies have shown that microbial metabolites, which represent the products of bacteria in the human gut, play a key role in shaping cancer risk and response to treatment. However, metabolite data typically contain a large proportion of missing values, which may result from either low abundance or technical challenges in data processing. Moreover, given the compositionality of microbiome data, where the observed abundances can only be interpreted on a relative scale, standard variable selection methods are not applicable. In this project, we propose a novel Bayesian regression method to address these challenges in the integration of metabolite and microbiome data. Key features of our proposed model include modeling the two different mechanisms of missingness for the metabolite data and adopting a Bayesian prior designed to address the compositional characteristics of microbiome data. We demonstrate on simulated data that our proposed model can accurately impute the unobserved true metabolite values and correctly select the relevant microbiome predictors. We further illustrate our method using real data from a study focused on understanding the interplay between the microbiome and metabolome in colorectal cancer.