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Protocol for constructing correlation-based molecular networks from large-scale untargeted metabolomics data

Year: 2025
Type: preprint
Abstract: 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.
DOI: 10.1101/2025.04.26.649581
Authors: Huang Lin, Lijun Zhang, Ali Lotfi, Alan Jarmusch, Iris Lee, Adam Kim, James T. Morton, Alexander Aksenov
Institutions: University of Maryland, College Park, University of Connecticut, National Institute of Environmental Health Sciences, Washington University in St. Louis, UConn Health, University of Colorado Boulder, ProNAi Therapeutics (United States)
Language: English
Open Access status: green
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
OpenAlex ID: W4409851299
Cites: 69
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Related to: 10