Uncovering circadian rhythms in metabolic longitudinal data: A Bayesian latent class modeling approach.

ABSTRACT: Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24-hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24-hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape-invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo samp

SUBMITTER: Kim S 

PROVIDER: S-EPMC10629474 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature