Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA.

ABSTRACT: The intricate dependency structure of biological "omics" data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes.

SUBMITTER: Erdos B 

PROVIDER: S-EPMC10325080 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature