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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

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Metabolomics data exploration guided by prior knowledge.

Robert A van den Berg1, Carina M Rubingh, Johan A Westerhuis

  • 1TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands. robert.vandenberg@psy.kuleuven.be

Analytica Chimica Acta
|September 29, 2009
PubMed
Summary
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Consensus principal component analysis (CPCA) and canonical correlation analysis (CCA) help focus metabolomics data on specific metabolite groups. These methods improve biological interpretation, especially for complex datasets like Escherichia coli.

Area of Science:

  • Metabolomics
  • Bioinformatics
  • Systems Biology

Background:

  • Metabolomics data analysis often requires focusing on specific metabolite subsets.
  • Existing methods may not effectively relate targeted metabolites to the broader metabolome.
  • Understanding biochemical pathways and their regulation is crucial in microbial systems.

Purpose of the Study:

  • To apply and compare Consensus Principal Component Analysis (CPCA) and Canonical Correlation Analysis (CCA) for focused metabolomics data exploration.
  • To investigate the relationship between selected metabolite groups (e.g., amino acid biosynthesis pathways) and the remainder of the metabolome.
  • To evaluate the utility of CPCA and CCA in enhancing biological interpretation of complex metabolomics data.

Main Methods:

  • Application of CPCA to identify common trends between metabolite subsets and the rest of the metabolome.

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

  • Application of CCA to identify correlations between metabolite subsets and the rest of the metabolome.
  • Analysis of two microbial metabolomics datasets: Pseudomonas putida S12 (simple) and Escherichia coli (complex).

    • Main Results:

    • CPCA and CCA yielded similar results for the simpler P. putida dataset.
    • For the complex E. coli dataset, CPCA highlighted trends related to phenylalanine biosynthesis, while CCA revealed differences between strains and growth conditions.
    • CPCA and CCA proved complementary, with focused analysis improving biological interpretation compared to ordinary PCA, especially for complex data.

    Conclusions:

    • CPCA and CCA are valuable, complementary tools for targeted analysis in metabolomics.
    • These methods facilitate the exploration of relationships between specific biochemical pathways and the overall metabolic state.
    • Focused data analysis using CPCA and CCA enhances biological insights from complex microbial metabolomics datasets.