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Updated: Apr 29, 2026

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
Published on: January 7, 2019
Phenotypic mapping of metabolic profiles using self-organizing maps of high-dimensional mass spectrometry data
Cody R Goodwin1, Stacy D Sherrod, Christina C Marasco
1Department of Chemistry and Vanderbilt Institute of Chemical Biology, ‡Vanderbilt Institute for Integrative Biosystems Research and Education, §Department of Physics and Astronomy, ∇Department of Biomedical Engineering, and ⊥Department of Molecular Physiology and Biophysics, Vanderbilt University , Nashville, Tennessee 37235, United States.
Abstract:
A metabolic system is composed of inherently interconnected metabolic precursors, intermediates, and products. The analysis of untargeted metabolomics data has conventionally been performed through the use of comparative statistics or multivariate statistical analysis-based approaches; however, each falls short in representing the related nature of metabolic perturbations. Herein, we describe a complementary method for the analysis of large metabolite inventories using a data-driven approach based upon a self-organizing map algorithm. This workflow allows for the unsupervised clustering, and subsequent prioritization of, correlated features through Gestalt comparisons of metabolic heat maps. We describe this methodology in detail, including a comparison to conventional metabolomics approaches, and demonstrate the application of this method to the analysis of the metabolic repercussions of prolonged cocaine exposure in rat sera profiles.
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