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Updated: Jun 20, 2026
14:18
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
20.9K
Phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis
Camille Guilmineau1, Marie Tremblay-Franco2,3, Nathalie Vialaneix4
1INRAE, University of Montpellier, LBE, 102 Avenue des Etangs, 11100, Narbonne, France. camille.guilmineau@inrae.fr.
BMC Bioinformatics
|April 16, 2025
Summary
A new method, phoenics, analyzes longitudinal metabolomic data by integrating metabolic pathway information. This approach enhances the detection of differential metabolic pathways and biological functions in complex biological samples.
Area of Science:
- Biochemistry and Systems Biology
- Statistical Bioinformatics
- Metabolomics
Background:
- Metabolomics provides snapshots of an organism's metabolic state.
- Longitudinal metabolomic data reveals dynamic biological processes.
- Integrating metabolic pathways improves biological interpretation of metabolomic studies.
Purpose of the Study:
- To introduce phoenics, a novel method for pathway-level differential analysis of longitudinal metabolomic data.
- To enhance the biological interpretability of metabolomic data by incorporating pathway information.
Main Methods:
- A two-step approach involving dimension reduction informed by pathway data.
- Application of a mixed-effects linear model to transformed longitudinal metabolomic data.
Main Results:
- Phoenics effectively controls Type I error rates.
- Demonstrated superior ability in detecting differential metabolic pathways compared to existing methods.
- Successfully identified novel impacted biological functions in real-world datasets.
Conclusions:
- The phoenics method offers improved analysis of longitudinal metabolomic data.
- Pathway-level analysis significantly enhances the discovery of biological functions.
- The phoenics R package is available for broader scientific application.
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