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

Balázs Erdős1, Johan A Westerhuis2, Michiel E Adriaens1

  • 1Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.

Plos Computational Biology
|June 23, 2023
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Summary
This summary is machine-generated.

Analyzing complex biological "omics" data from longitudinal studies is challenging. This study introduces a quantitative longitudinal model within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to effectively quantify dynamics in multivariate outcomes.

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Systems Biology

Background:

  • Biological 'omics' data from longitudinal intervention studies present significant analytical challenges due to high dimensionality and complex inter-relationships.
  • Quantifying subtle dynamic differences, crucial in nutritional studies, is particularly difficult with traditional methods.
  • Existing methods struggle to capture the intricate dependency structures and multivariate outcomes common in repeated-measures data.

Purpose of the Study:

  • To demonstrate the utility of quantitative longitudinal models within the RM-ASCA+ framework for analyzing frequently sampled longitudinal 'omics' data.
  • To provide a method for systematically quantifying and summarizing multivariate outcomes in longitudinal studies.
  • To account for within-subject dependency structures in complex biological datasets.

Main Methods:

  • Application of the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework.
  • Integration of linear mixed models with polynomial and spline basis expansions of the time variable.
  • Utilized a simulation study and a real-world metabolomics dataset for validation.

Main Results:

  • The proposed approach effectively captures dynamics in frequently sampled longitudinal data with multivariate outcomes.
  • Non-linear dynamics were successfully quantified using linear mixed models with basis expansions within RM-ASCA+.
  • The method provides a convenient and interpretable way to analyze complex longitudinal 'omics' data.

Conclusions:

  • The RM-ASCA+ framework, enhanced with linear mixed models, offers a robust solution for analyzing longitudinal 'omics' data.
  • This approach facilitates the systematic quantification and summarization of multivariate outcomes while respecting data dependencies.
  • The findings enable more meaningful insights from complex biological intervention studies.