Compositional knockoff filter for high-dimensional regression analysis of microbiome data.

ABSTRACT: A critical task in microbiome data analysis is to explore the association between a scalar response of interest and a large number of microbial taxa that are summarized as compositional data at different taxonomic levels. Motivated by fine-mapping of the microbiome, we propose a two-step compositional knockoff filter to provide the effective finite-sample false discovery rate (FDR) control in high-dimensional linear log-contrast regression analysis of microbiome compositional data. In the first step, we propose a new compositional screening procedure to remove insignificant microbial taxa while retaining the essential sum-to-zero constraint. In the second step, we extend the knockoff filter to identify the significant microbial taxa in the sparse regression model for compositional data. Th

SUBMITTER: Srinivasan A 

PROVIDER: S-EPMC7831267 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature