Statistical treatment of 2D NMR COSY spectra in metabolomics: data preparation, clustering-based evaluation of the Metabolomic Informative Content and comparison with 1H-NMR

Compared with the widely used 1H-NMR spectroscopy, two-dimensional NMR experiments provide more sophisticated spectra which should facilitate the identification of relevant spectral zones or biomarkers in metabolomics. This paper focuses on 1H-1H COrrelation SpectroscopY (COSY) spectral data. In spite of longer inherent acquisition times, it is commonly accepted by users (biologists, healthcare professionals) that the introduction of an additional dimension probably represents a huge qualitative step for investigations in terms of metabolites identification. Moreover, it seems natural that more information leads to more predictive power. But, until now, very few statistical studies clearly proved this assumption. Therefore a fundamental question is “Is this supplementary information relevant?”. In order to extend the statistical properties developed for 1D spectroscopy to the challenges raised by 2D spectra, a rigorous study of the performances of COSY spectra is needed as a prerequisite. Having introduced new pre-processing concepts, such as the Global Peak List or an ad hoc 2D “bucketing”, this paper presents an innovative methodology based on multivariate clustering algorithms to evaluate this question. Numerical clustering quality indexes and graphical results are proposed, based both on the spectral presence or absence of peaks (binary position vectors) and on peak intensities, and through different levels of spectral resolution. The second goal of this paper is to compare clustering performances obtained on COSY and on 1H-NMR spectra, with the aim of understanding to what extent the COSY spectra carry more Metabolomic Informative Content about the signal than 1D ones. The methodology is applied to two real experimental designs involving different groups of spectra (which define the signal): a 4-mixture cell culture media containing various supervised metabolites and a complex human serum based design. It is shown that COSY spectra appear to be statistically powerful and, in addition, provide better clustering results than corresponding 1H-NMR when using unlabeled information. Consequently, additional information appears to be relevant for metabolomics application