Abstract
High resolution of NMR spectroscopic data of biosamples are a rich source of information on the metabolic response to physiological variation or pathological events. There are many advantages of NMR techniques such as the sample preparation is fast, simple and non-invasive. Statistical analysis of NMR spectra usually focuses on differential expression of large resonance intensity corresponding to abundant metabolites and involves several data preprocessing steps. In this paper we estimate functional components of spectra and test their significance using multiscale techniques. We also explore scaling in NMR spectra and use the systematic variability of scaling descriptors to predict the level of cysteine, an important precursor of glutathione, a control antioxidant in human body. This is motivated by high cost (in time and resources) of traditional methods for assessing cysteine level by high performance liquid chromatograph (HPLC).
Highlights
During the last decade, metabolomics has provided new opportunities to investigate complex dietary and nutritional questions by applying quantitative methodologies to information-rich profiles of dietary chemicals and their metabolites (German et al, 2003; 2004)
NMR spectroscopy has been utilized in exploring physiological variations in macronutrient metabolism and has shown to be a fast, simple, and non-invasive method for “fingerprinting” of metabolic compounds
We suggest new methods to extract biologically significant information about the interactions of metabolites and their relationship with biological functions that is contained in NMR spectra by using scaling measures computed from wavelet coefficients
Summary
Metabolomics has provided new opportunities to investigate complex dietary and nutritional questions by applying quantitative methodologies to information-rich profiles of dietary chemicals and their metabolites (German et al, 2003; 2004). Statistical analysis of NMR spectra traditionally focuses on differential expression of large resonance intensity corresponding to abundant metabolites and involves several data preprocessing steps such as baseline correction, peak alignment and normalization. These preprocessing steps are not perfect and often lead to ambiguities and information loss. We suggest new methods to extract biologically significant information about the interactions of metabolites and their relationship with biological functions that is contained in NMR spectra by using scaling measures computed from wavelet coefficients. To focus on the effect of diurnal time on the scaling coefficient, we use functional repeated measure block design, a statistical design technique in which the observations are spectra.
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