Publications

A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses

Shankar J, Szpakowski S, Solis NV, Mounaud S, Liu H, Losada L, Nierman WC, Filler SG

PMID: 25638274

Abstract

Microbiome studies incorporate next-generation sequencing to obtain profiles of microbial communities. Data generated from these experiments are high-dimensional with a rich correlation structure but modest sample sizes. A statistical model that utilizes these microbiome profiles to explain a clinical or biological endpoint needs to tackle high-dimensionality resulting from the very large space of variable configurations. Ensemble models are a class of approaches that can address high-dimensionality by aggregating information across large model spaces. Although such models are popular in fields as diverse as economics and genetics, their performance on microbiome data has been largely unexplored.