Statistical Total Correlation Spectroscopy:  An Exploratory Approach for Latent Biomarker Identification from Metabolic ¹H NMR Data Sets

Abstract

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.

Analytic Properties of Statistical Total Correlation Spectroscopy Based Information Recovery in 1H NMR Metabolic Data Sets

“…The work presented here supports the application of STOCSY for metabolite structural identification, as already applied in the literature 3,5,[8][9][10] . The distributions of structural and non-structural correlations are highly distinguishable and simple correlation thresholds are able to achieve a high probability of structural association at reasonable sample sizes.…”

“…There are now numerous applications of STOCSY and closely related covariance techniques for enhanced information recovery from low dimensional NMR data sets on multiple and single samples [3][4][5][6][7][8][9][10][11][12][13] . The STOCSY approach has been applied to the structural assignment problem in a variety of NMR metabolic profiling contexts, such as deconvolution of overlapped chromatographic peaks in LC-NMR 7 , delineation of drug metabolism in molecular epidemiology studies 5 and separation of different molecular signatures in diffusion edited spectroscopy 6 .…”

Identification of Urinary Biomarkers Related to Cisplatin-Induced Acute Renal Toxicity Using NMR-Based Metabolomics

“…Orthogonal projections to latent structure-discriminant analysis (OPLS-DA) was performed with one predictive and four orthogonal component (Trygg and Wold, 2002;Bylesjo et al, 2006). 1D projection of Statistical Correlation Spectroscopy (STOCSY) was built by overlaying the colorcoded correlation values on to the OPLS-DA variable plot (Cloarec et al, 2005;Maher et al, 2009;Sands et al, 2009 …”

Extraction of Alkaloids for NMR-Based Profiling: Exploratory Analysis of an Archaic Cinchona Bark Collection

“…extraction method using a 5 : 5 : 1 : 1 mixture of chloroform, methanol, water, and perchloric acid. The dataset containing spectra from 117 different samples (approximately half of them prepared twice) are evaluated using principal component analysis (PCA) [9] and statistical correlation spectroscopy (STOCSY) [10] to study the variations between samples and replicates as well as correlated variables so as to find out the compounds responsible for the variations.…”

“…However, by using the STOCSY method (statistical total correlation spectroscopy), correlated signals in the dataset can be identified [10]. Hence, the variables corresponding to the signals from H-2′ in quinine and cinchonine were selected along the two unknown doublet signals.…”