Correlating High-dimensional longitudinal microbial features with time-varying outcomes with FLORAL.

ABSTRACT: Correlating time-dependent patient characteristics and matched microbiome samples can be helpful to identify biomarkers in longitudinal microbiome studies. Existing approaches typically repeat a pre-specified modeling approach for all taxonomic features, followed by a multiple testing adjustment step for false discovery rate (FDR) control. In this work, we develop an alternative strategy of using log-ratio penalized generalized estimating equations, which directly models the longitudinal patient characteristic of interest as the outcome variable and treats microbial features as high-dimensional compositional covariates. A cross validation procedure is developed for variable selection and model selection among different working correlation structures. In extensive simulations, the proposed

SUBMITTER: Fei T 

PROVIDER: S-EPMC11870566 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature