Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets.
We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.
Cloarec
Olivier
O
Biological Chemistry Section, Faculty of Medicine, Biomedical Sciences Division, Imperial College London, South Kensington, London, UK.
Dumas
Marc-Emmanuel
ME
Craig
Andrew
A
Barton
Richard H
RH
Trygg
Johan
J
Hudson
Jane
J
Blancher
Christine
C
Gauguier
Dominique
D
Lindon
John C
JC
Holmes
Elaine
E
Nicholson
Jeremy
J
D000588:Amines / Q000032:analysis / Q000378:metabolism; D000818:Animals; D015415:Biomarkers / Q000032:analysis* / Q000378:metabolism / Q000652:urine; D019343:Citric Acid / Q000032:analysis / Q000378:metabolism; D003401:Creatine / Q000032:analysis / Q000378:metabolism; D003404:Creatinine / Q000032:analysis / Q000378:metabolism; D004040:Dietary Carbohydrates / Q000032:analysis / Q000378:metabolism; D016002:Discriminant Analysis; D005988:Glyceric Acids / Q000032:analysis / Q000378:metabolism; D045782:Hemiterpenes; D006626:Hippurates / Q000032:analysis / Q000378:metabolism; D007333:Insulin Resistance; D007656:Ketoglutaric Acids / Q000032:analysis / Q000378:metabolism; D009682:Magnetic Resonance Spectroscopy / Q000379:methods* / Q000706:statistics & numerical data; D008297:Male; D051379:Mice; D008807:Mice, Inbred BALB C; D008810:Mice, Inbred C57BL; D008815:Mice, Inbred Strains; D010421:Pentanoic Acids / Q000032:analysis / Q000378:metabolism; D010636:Phenols; D025341:Principal Component Analysis; D011422:Propionates / Q000032:analysis / Q000378:metabolism; D011522:Protons; D012521:Sarcosine / Q000031:analogs & derivatives / Q000032:analysis / Q000378:metabolism; D019802:Succinic Acid / Q000032:analysis / Q000378:metabolism; D013654:Taurine / Q000032:analysis / Q000378:metabolism
C491266:3-hydroxyphenylpropionic acid; D000588:Amines; D015415:Biomarkers; D004040:Dietary Carbohydrates; D005988:Glyceric Acids; D045782:Hemiterpenes; D006626:Hippurates; D007656:Ketoglutaric Acids; D010421:Pentanoic Acids; D010636:Phenols; D011422:Propionates; D011522:Protons; C008216:isovaleric acid; D013654:Taurine; D019343:Citric Acid; C042971:glyceric acid; C025138:dimethylglycine; D019802:Succinic Acid; D003404:Creatinine; D003401:Creatine; C030514:hippuric acid; D012521:Sarcosine