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

Abstract

Compared with the widely used 1 H-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 1 H-1 H 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 1 H-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 1 H-NMR when using unlabeled information. Consequently, additional information appears to be relevant for metabolomics applications.

Multidimensional NMR approaches towards highly resolved, sensitive and high-throughput quantitative metabolomics

“…Indeed, two or more metabolites contribute to one bucket in the 1D spectra because of signal overlap, while 2D NMR resolves this ambiguity. A similar conclusion was recently reached by Fé raud et al, who demonstrated through multivariate approaches that 2D COSY spectra provided a higher level of clustering and were more informative from the statistical point of view [11].…”

PepsNMR for 1H NMR metabolomic data pre-processing

“…These criteria are derived from unsupervised as well as supervised chemometric tools. They are gathered under the Metabolomic Informative Content (MIC) concept [13] and include inertia measures, PCA, clustering and PLS-DA related criteria. The inertia analysis decomposes the total variance into two complementary parts: the variance between the groups and the variance within the groups of observations.…”

“…The inertia analysis decomposes the total variance into two complementary parts: the variance between the groups and the variance within the groups of observations. For the clustering, the (adjusted) Rand indexes measure the true class recovery efficiency and should be maximised while the Dunn and Davies-Bouldin indexes measure the clustering homogeneity and they have to be respectively maximised and minimised (formula details can be found in Féraud et al [13]). PLS-DA builds a latent structure with Latent Variables (LV) to extract the variance components from the data but instead of maximising the spectral data matrix variance as does PCA, it maximises the variance of both the spectral data matrix (the regressors) and the class labels (the response) and their correlation.…”

Combining strong sparsity and competitive predictive power with the L-sOPLS approach for biomarker discovery in metabolomics

“…Several signals, for instance peaks in 1 H-NMR, can overlap, move together and/ or be associated to a same molecule. So, an idea would be to apply L-sOPLS on 2D-NMR spectra (COSY for instance Feraud et al 2015) or to take into account groups of features instead of individual features as inputs of the sparse algorithms. Methods like Group-LASSO, Sparse-Group-LASSO or Overlay-Group-LASSO (Friedman et al 2010) could then be tested in this context.…”