Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis

TL;DR

This paper introduces a sparse Dirichlet-multinomial regression model with variable selection for microbiome data, effectively identifying environmental covariates associated with microbiome composition while accounting for overdispersion.

Contribution

The authors develop a penalized likelihood approach with a sparse group l1 penalty and an efficient algorithm for variable selection in high-dimensional microbiome data analysis.

Findings

Sparse DM regression outperforms traditional models in identifying relevant covariates.

The method successfully detects strong associations between nutrient intake and gut microbiome.

Simulations confirm improved variable selection accuracy.

Abstract

With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for each microbiome sample. One goal of microbiome study is to associate the microbiome composition with environmental covariates. We propose to model the taxa counts using a Dirichlet-multinomial (DM) regression model in order to account for overdispersion of observed counts. The DM regression model can be used for testing the association between taxa composition and covariates using the likelihood ratio test. However, when the number of covariates is large, multiple testing can lead to loss of power. To address the high dimensionality of the problem, we develop a penalized likelihood approach to estimate the regression parameters and to select the variables by imposing a sparse group $\ell_1$ penalty to encourage both group-level and within-group sparsity. Such a variable selection procedure can lead to selection of the relevant covariates and their associated bacterial taxa. An efficient block-coordinate descent algorithm is developed to solve the optimization problem. We present extensive simulations to demonstrate that the sparse DM regression can result in better identification of the microbiome-associated covariates than models that ignore overdispersion or only consider the proportions. We demonstrate the power of our method in an analysis of a data set evaluating the effects of nutrient intake on human gut microbiome composition. Our results have clearly shown that the nutrient intake is strongly associated with the human gut microbiome.