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Bayesian Sparse Regression for the Association of Microbiome Profiles with Metabolite Abundance

Abstract Number:

1680 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Kai Jiang (1), Christine Peterson (2)

Institutions:

(1) The University of Texas MD Anderson Cancer Center, N/A, (2) University of Texas MD Anderson Cancer Center, N/A

Co-Author:

Christine Peterson  
University of Texas MD Anderson Cancer Center

First Author:

Kai Jiang  
The University of Texas MD Anderson Cancer Center

Presenting Author:

Kai Jiang  
The University of Texas MD Anderson Cancer Center

Abstract Text:

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 are often recorded as zeros. These missing values 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 method to address challenges in both metabolite and microbiome data. Key features of our proposed model include adopting a z-prior to address the compositional characteristics of microbiome data and modeling the two different mechanisms of missing metabolite data. We demonstrate on simulated data that our proposed model can impute the unobserved true metabolite values and correctly select the relevant microbiome predictors. We illustrate our method on real data from a study on the interplay between the microbiome and metabolome in colorectal cancer.

Keywords:

Bayesian variable selection |Compositional covariates|Metabolome outcome|Microbiome data analysis|Missing value imputation|

Sponsors:

Biometrics Section

Tracks:

Model/Variable Selection

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