Bayesian Sparse Multivariate Regression with Asymmetric Nonlocal Priors for Microbiome Data Analysis

Shuler, Kurtis; Sison-Mangus, Marilou; Lee, Juhee

doi:10.1214/19-BA1164

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Bayesian Sparse Multivariate Regression with Asymmetric Nonlocal Priors for Microbiome Data Analysis

2020
By Shuler, Kurtis ; Sison-Mangus, Marilou ; Lee, Juhee
Source: Bayesian Analysis, 15 (2) 1-20
Series: Contribution no. ECOHAB #940

[PDF-2.81 MB]

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Details:

Journal Title:

Bayesian Analysis
Personal Author:

Shuler, Kurtis ; Sison-Mangus, Marilou ; Lee, Juhee

Shuler, Kurtis ; Sison-Mangus, Marilou ; Lee, Juhee Less -
NOAA Program & Office:

NOS (National Ocean Service)
Description:

We propose a Bayesian sparse multivariate regression method to model the relationship between microbe abundance and environmental factors for microbiome data. We model abundance counts of operational taxonomic units (OTUs) with a negative binomial distribution and relate covariates to the counts through regression. Extending conventional nonlocal priors, we construct asymmetric non-local priors for regression coefficients to efficiently identify relevant covariates andtheir effect directions. We build a hierarchical model to facilitate pooling of information across OTUs that produces parsimonious results with improved accuracy. We present simulation studies that compare variable selection performance under the proposed model to those under Bayesian sparse regression models with asymmetric and symmetric local priors and two frequentist models. The simulations show the proposed model identifies important covariates and yields coefficient estimates with favorable accuracy compared with the alternatives. The proposed model is applied to analyze an ocean microbiome dataset collected over time to study the association of harmful algal bloom conditions with microbial communities.
Keywords:

[+]

Mathematical Models Population Genetics Red Grouper Risk Assessment count data, harmful algal bloom, microbiome, negative binomial, next-generation sequencing, nonlocal prior, stochastic search variable selection
Source:

Bayesian Analysis, 15 (2) 1-20
Series:

Contribution no. ECOHAB #940 ; Contribution no. MERHAB #206

Contribution no. ECOHAB #940 ; Contribution no. MERHAB #206 Less -
DOI:

https://doi.org/10.1214/19-BA1164
Document Type:

Journal Article
Funding:

Grant no. NA17NOS4780183
Place as Subject:

North Atlantic Ocean ; United States

North Atlantic Ocean ; United States Less -
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:4fafcd24f8f95abc688bb2911224b0d5a8e62f96439347524bb18ba18b0972f4
Download URL:

https://repository.library.noaa.gov/view/noaa/28811/noaa_28811_DS1.pdf
File Type:

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