Analysis of metabolomic profiling data acquired on GC-MS

Imhoi Koo, Xiaoli Wei, Xiang Zhang

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Scopus citations


Gas chromatography-mass spectrometry (GC-MS) is one of the three most popular analytical platforms for metabolomics and is largely employed for the study of oncometabolism. Large volumes of data are usually generated in a GC-MS experiment, and many analytical steps are required to extract biologically relevant information from GC-MS data. These steps include (1) spectrum deconvolution, to convert raw data into a peak list; (2) metabolite identification, to recognize metabolites associated to chromatographic peaks; (3) quantification, to compare the abundance of a specific metabolite in different samples; (4) association network analysis, to reveal correlations among the changes in the abundance of multiple metabolites; and (5) pathway analysis, to understand the biochemical interrelationship between several metabolites that vary in a coordinated or differential manner. Here, we describe in detail the analytical steps that are necessary to interpret a GC-MS dataset.

Original languageEnglish (US)
Title of host publicationCell-wide Metabolic Alterations Associated with Malignancy
PublisherAcademic Press Inc.
Number of pages10
ISBN (Print)9780128013298
StatePublished - 2014

Publication series

NameMethods in Enzymology
ISSN (Print)0076-6879
ISSN (Electronic)1557-7988

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology


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