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  3. Longitudinal Metabolomics Data Analysis Informed By Mechanistic Models.
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  3. Longitudinal Metabolomics Data Analysis Informed By Mechanistic Models.

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Longitudinal Metabolomics Data Analysis Informed by Mechanistic Models.

Lu Li1,2, Huub Hoefsloot3, Barbara M Bakker4

  • 1School of Mathematics (Zhuhai), Sun Yat-sen University, Zhuhai 519000, China.

Metabolites
|January 24, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Integrating mechanistic models with metabolomics data analysis improves pattern discovery, especially for males, and enhances robustness against missing data. This novel approach aids in understanding complex biological systems.

Keywords:
(coupled) tensor factorizationschallenge testsknowledge-guided machine learninglongitudinal metabolomics datametabolic model

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Area of Science:

  • Metabolomics
  • Systems Biology
  • Computational Biology

Background:

  • Metabolomics data often suffers from noise, small sample sizes, and missing values, hindering accurate analysis.
  • While data-driven methods are useful, incorporating prior knowledge of metabolic pathways can significantly improve insights.
  • Existing methods may not fully leverage mechanistic understanding for metabolomics data interpretation.

Purpose of the Study:

  • To introduce a novel data analysis approach for metabolomics that integrates mechanistic models.
  • To enhance the analysis of noisy and incomplete metabolomics data by combining real measurements with simulated data.
  • To improve the discovery of biologically relevant patterns and biomarkers.

Main Methods:

  • Time-resolved metabolomics data from plasma samples (COPSAC2000 cohort) were structured as a third-order tensor (subjects x metabolites x time).
  • Simulated data from a human whole-body metabolic model were also structured as a tensor (virtual subjects x metabolites x time).
  • Coupled tensor factorizations were employed to jointly analyze real and simulated data, coupled in the metabolite mode.

    • Main Results:

    • Joint analysis of real and simulated data showed improved pattern discovery and higher correlation with BMI-related phenotypes in males compared to analyzing real data alone.
    • Performance was comparable between joint and real-data-only analysis in females.
    • The approach demonstrated robustness in handling incomplete measurements but highlighted limitations with incorrect prior information.

    Conclusions:

    • Joint analysis using coupled tensor factorizations effectively integrates mechanistic prior information into metabolomics data analysis.
    • This hybrid approach guides the interpretation of real data and reveals more interpretable patterns.
    • The method offers a promising strategy for robust metabolomics analysis, particularly when dealing with data imperfections.