Enhanced visualization of microbiome data in repeated measures designs.

ABSTRACT: Repeated measures microbiome studies, including longitudinal and clustered designs, offer valuable insights into the dynamics of microbial communities and their associations with various health outcomes. However, visualizing such multivariate data poses significant challenges, particularly in distinguishing meaningful biological patterns from noise introduced by covariates and the complexities of repeated measures. In this study, we propose a framework to enhance the visualization of repeated measures microbiome data using Principal Coordinates Analysis (PCoA) adjusted for covariates through linear mixed models (LMM). Our method adjusts for confounding variables and accounts for the repeated measures structure of the data, enabling clearer identification of microbial community variations a

SUBMITTER: Little A 

PROVIDER: S-EPMC11604720 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature