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Updated: Jul 17, 2026
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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
Network-based integration of metabolomics data from large-scale repositories
Cecilia Wieder1, Eloisa Rocha Liedl1, Thomas Payne2
1Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Imperial College London, Hammersmith Hospital, Du Cane Road, London, W12 0NN, UK.
Summary
This study introduces a network-based framework to integrate public metabolomics data, enabling robust cross-study analysis of metabolite signatures and pathways. The approach enhances data reuse and reproducibility for accelerated biological discovery.
Area of Science:
- Bioinformatics
- Computational Biology
- Metabolomics
Background:
- Public metabolomics data repositories are growing rapidly.
- There's a need for tools to integrate and reanalyze datasets for reuse and reproducibility.
Purpose of the Study:
- Enable large-scale integrative meta-analysis of public metabolomics data.
- Identify robust multi-study metabolite and pathway signatures.
- Provide global visual overviews of repository content.
Main Methods:
- Developed a network-based integration framework for study and metabolite/pathway levels.
- Used metabolite co-occurrences in bipartite graphs for meta-networks.
- Created study-level networks for repository exploration.
- Implemented an interactive Python Dash app for network visualization.
Main Results:
- Applied the framework to six COVID-19 plasma datasets, identifying ten differential metabolites across studies.
- Found pyroglutamic acid consistently up-regulated in COVID-19 studies.
- Pathway-level networks revealed shared biological processes.
- A global network of 1,181 studies showed clustering by assay and metadata.
Conclusions:
- Network-based integration of harmonized metabolomics data enables robust cross-study analysis.
- Standardized annotation pipelines are crucial for data reuse and reproducibility.
- This approach accelerates biological discovery by enhancing the impact of public metabolomics datasets.
