Phenotypic mapping of metabolic profiles using self-organizing maps of high-dimensional mass spectrometry data.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Goodwin, CR; Sherrod, SD; Marasco, CC; Bachmann, BO; Schramm-Sapyta, N; Wikswo, JP; McLean, JA

Published Date

  • July 2014

Published In

Volume / Issue

  • 86 / 13

Start / End Page

  • 6563 - 6571

PubMed ID

  • 24856386

Pubmed Central ID

  • 24856386

Electronic International Standard Serial Number (EISSN)

  • 1520-6882

International Standard Serial Number (ISSN)

  • 0003-2700

Digital Object Identifier (DOI)

  • 10.1021/ac5010794

Language

  • eng