@inbook{b5c30f54b21248f0b4312fa2f251efa7,
title = "Analysis of metabolomic profiling data acquired on GC-MS",
abstract = "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.",
author = "Imhoi Koo and Xiaoli Wei and Xiang Zhang",
note = "Funding Information: This work was supported by NIH grant RO1GM087735 through the National Institute of General Medical Sciences.",
year = "2014",
doi = "10.1016/B978-0-12-801329-8.00016-7",
language = "English (US)",
isbn = "9780128013298",
series = "Methods in Enzymology",
publisher = "Academic Press Inc.",
pages = "315--324",
booktitle = "Cell-wide Metabolic Alterations Associated with Malignancy",
address = "United States",
}