Protocol for constructing correlation-based molecular networks from large-scale untargeted metabolomics data
This protocol describes a computational approach for constructing correlation-based molecular networks from untargeted metabolomics data using MetVAE, a variational autoencoder-based framework. Complementing spectral similarity networks, it captures functional relationships reflected in cross-sample correlations. The workflow imports metabolomics features and sample metadata, adjusts for compositionality, missingness, confounding, and high-dimensionality, estimates sparse metabolite correlations, and exports GraphML files for network visualization. In a hepatocellular carcinoma mouse model, it links lipid classes in high-fat-diet animals, suggesting an endogenous "auto-brewery" route to lipotoxic metabolites.
Authors
- Alexander A. Aksenov (ORCID: https://orcid.org/0000-0002-9445-2248)
- James T. Morton (ORCID: https://orcid.org/0000-0003-3189-2681)
- Alan K. Jarmusch (ORCID: https://orcid.org/0000-0002-2228-6308)
- Lin Huang (ORCID: https://orcid.org/0000-0002-4892-7871)
- Lijun Zhang (ORCID: https://orcid.org/0000-0002-9372-1004)
- Iris Lee (ORCID: https://orcid.org/0000-0003-0433-1707)
Institutions
- University of Connecticut (US)
- University of Colorado Boulder (US)
- Washington University in St. Louis (US)
- National Institute of Environmental Health Sciences (US)
- ProNAi Therapeutics (United States) (US)
- University of Maryland, College Park (US)
- UConn Health (US)
Publication Details
- Journal
- STAR Protocols
- Published
- 2026-11-11
- DOI
- https://doi.org/10.1016/j.xpro.2026.104956
- Citations
- 2
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.68
Funders
- U.S. Department of Energy
- U.S. Department of Health and Human Services
- U.S. Department of Agriculture
- National Institutes of Health
- National Institute of Food and Agriculture
- National Institute of Environmental Health Sciences