Generalized Linear Models with Linear Constraints for Microbiome Compositional Data

TL;DR

This paper develops a generalized linear regression framework with linear constraints tailored for microbiome compositional data, enabling accurate inference and variable selection while respecting the data's compositional structure.

Contribution

It introduces a penalized likelihood estimation method with a de-biased procedure for valid inference under linear constraints in microbiome data analysis.

Findings

Confidence intervals have correct coverage probabilities.

Imposing linear constraints reduces estimate variance.

Method effectively identifies bacteria associated with IBD.

Abstract

Motivated by regression analysis for microbiome compositional data, this paper considers generalized linear regression analysis with compositional covariates, where a group of linear constraints on regression coefficients are imposed to account for the compositional nature of the data and to achieve subcompositional coherence. A penalized likelihood estimation procedure using a generalized accelerated proximal gradient method is developed to efficiently estimate the regression coefficients. A de-biased procedure is developed to obtain asymptotically unbiased and normally distributed estimates, which leads to valid confidence intervals of the regression coefficients. Simulations results show the correctness of the coverage probability of the confidence intervals and smaller variances of the estimates when the appropriate linear constraints are imposed. The methods are illustrated by a microbiome study in order to identify bacterial species that are associated with inflammatory bowel disease (IBD) and to predict IBD using fecal microbiome.