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

Balázs Erdős*, Johan A Westerhuis, Michiel E Adriaens, Shauna D O'Donovan, Ren Xie, Cécile M Singh-Povel, Age K Smilde, Ilja C W Arts

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.

Original languageEnglish
Article numbere1011221
Number of pages18
JournalPLoS Computational Biology
Volume19
Issue number6
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Metabolomics
  • Computer Simulation
  • Algorithms
  • Linear Models

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