Details

Autor(en) / Beteiligte

• Goodwin, Cody R
• Sherrod, Stacy D
• Marasco, Christina C
• Bachmann, Brian O
• Schramm-Sapyta, Nicole
• Wikswo, John P
• McLean, John A

Titel

Phenotypic Mapping of Metabolic Profiles Using Self-Organizing Maps of High-Dimensional Mass Spectrometry Data

Ist Teil von

• Analytical chemistry (Washington), 2014-07, Vol.86 (13), p.6563-6571

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2014

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.