Metabolic pools of biological matrices can be extensively analyzed by NMR. Measured data consist of hundreds of NMR signals
with different chemical shifts and intensities representing different metabolites’ types and levels, respectively. Relevant predictive
NMR signals need to be extracted from the pool using variable selection methods. This paper presents both a review and research on this
metabolomics field. After reviews on discriminant potentials and statistical analyses of NMR data in biological fields, the paper presents
an original approach to extract a small number of NMR signals in a biological matrix A (BM-A) in order to predict metabolic levels in
another biological matrix B (BM-B). Initially, NMR dataset of BM-A was decomposed into several row-column homogeneous blocks using
hierarchical cluster analysis (HCA). Then, each block was subjected to a complete set of Jackknifed correspondence analysis (CA) by
removing separately each individual (row). Each CA condensed the numerous NMR signals into some principal components (PCs). The
different PCs representing the (n – 1) active individuals were used as latent variables in a stepwise multi-linear regression to predict
metabolic levels in BM-B. From the built regression model, metabolite level in the outside individual was predicted (for next model validation).
From all the PCs-based regression models resulting from all the jackknifed CA applied on all the individuals, the most contributive
NMR signals were identified by their highest absolute contributions to PCs. Finally, these selected NMR signals (measured in BMA)
were used to build final population and sub-population regression models predicting metabolite levels in BM-B.
Keywords: Aortic cholesteryl ester, cluster analysis, correspondence analysis, Jackknife technique, metabolomics, multiple linear regression,
stepwise technique, urinary 1H-NMR signals.
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