Data-driven approach for metabolite relationship recovery in biological 1H NMR data sets using iterative statistical total correlation spectroscopy.

TitleData-driven approach for metabolite relationship recovery in biological 1H NMR data sets using iterative statistical total correlation spectroscopy.
Publication TypeJournal Article
Year of Publication2011
AuthorsSands CJ, Coen M, Ebbels TMD, Holmes E, Lindon JC, Nicholson JK
JournalAnal Chem
Volume83
Issue6
Pagination2075-82
Date Published2011 Mar 15
ISSN1520-6882
KeywordsAnimals, Galactosamine, Liver, Male, Metabolomics, Nuclear Magnetic Resonance, Biomolecular, Rats, Rats, Sprague-Dawley, Statistics as Topic, Uridine Diphosphate
Abstract

Statistical total correlation spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite correlations in NMR metabonomic data sets. Here, the STOCSY approach is extended in a novel Iterative-STOCSY (I-STOCSY) tool in which correlations are calculated initially from a driver peak of interest and subsequently for all peaks identified as correlating with a correlation coefficient greater than a set threshold. Consequently, in a single automated run, the majority of information contained in multiple STOCSY calculations from all peaks recursively correlated to the original user defined driver peak of interest are recovered. In addition, highly correlating peaks are clustered into putative structurally related sets, and the results are presented in a fully interactive plot where each set is represented by a node; node-to-node connections are plotted alongside corresponding spectral data colored by the strength of connection, thus allowing the intuitive exploration of both inter- and intrametabolite connections. The I-STOCSY approach has been here applied to a (1)H NMR data set of 24 h postdose aqueous liver extracts from rats treated with the model hepatotoxin galactosamine and has been shown both to recover the previously deduced major metabolic effects of treatment and to generate new hypotheses even on this well-studied model system. I-STOCSY, thus, represents a significant advance in correlation based analysis and visualization, providing insight into inter- and intrametabolite relationships following metabolic perturbations.

DOI10.1021/ac102870u
Alternate JournalAnal. Chem.
PubMed ID21323345