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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 approachdoi:10.1021/ac5010794 pmid:24856386 pmcid:PMC4082383 fatcat:z3phqxxhc5ctjlkwkifdjavtjy