  Ongoing advances in microbiome profiling have allowed unprecedented insights
into the molecular activities of microbial communities. This has fueled a
strong scientific interest in understanding the critical role the microbiome
plays in governing human health, by identifying microbial features associated
with clinical outcomes of interest. Several aspects of microbiome data limit
the applicability of existing variable selection approaches. In particular,
microbiome data are high-dimensional, extremely sparse, and compositional.
Importantly, many of the observed features, although categorized as different
taxa, may play related functional roles. To address these challenges, we
propose a novel compositional regression approach that leverages the
data-adaptive clustering and variable selection properties of the spiked
Dirichlet process to identify taxa that exhibit similar functional roles. Our
proposed method, Bayesian Regression with Agglomerated Compositional Effects
using a dirichLET process (BRACElet), enables the identification of a sparse
set of features with shared impacts on the outcome, facilitating dimension
reduction and model interpretation. We demonstrate that BRACElet outperforms
existing approaches for microbiome variable selection through simulation
studies and an application elucidating the impact of oral microbiome
composition on insulin resistance.
