Adaptive and powerful microbiome multivariate association analysis via feature selection

Abstract

The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods’ susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT.

Gut microbiome co-abundance networks varies with age, sex, smoking status and body mass index

“…To address this limitation, there has been an increasing interest in multivariate approaches. This includes the application of the alpha and beta diversity indexes, which measure intra-and inter-sample distances between microbiome samples 19,22,28 , and various multivariate mean-based approaches to conduct a joint test of the abundance of multiple taxa with a predictor [29][30][31] . Conversely, the study of features associated with variability in the co-abundance of taxa remains puzzling [32][33][34] .…”