A Simultaneous Feature Selection and Compositional Association Test for Detecting Sparse Associations in High-Dimensional Metagenomic Data.

ABSTRACT: Numerous metagenomic studies aim to discover associations between the microbial composition of an environment (e.g., gut, skin, oral) and a phenotype of interest. Multivariate analysis is often performed in these studies without critical a priori knowledge of which taxa are associated with the phenotype being studied. This approach typically reduces statistical power in settings where the true associations among only a few taxa are obscured by high dimensionality (i.e., sparse association signals). At the same time, low sample size and compositional sample space constraints may reduce beyond-study generalizability if not properly accounted for. To address these difficulties, we developed the Selection-Energy-Permutation (SelEnergyPerm) method, a nonparametric group association test

SUBMITTER: Hinton AL 

PROVIDER: S-EPMC8978828 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature