Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples

Spraul, Manfred; Neidig, Peter; Klauck, Ulrich; Kessler, P.; Holmes, Elaine; Nicholson, Jeremy K.; Sweatman, Brian C.; Salman, S. R.; Farrant, R. D.; Rahr, Elizabeth; Beddell, C.R.; Lindon, John C.

doi:10.1016/0731-7085(94)00073-5

Cited by 106 publications

(68 citation statements)

References 23 publications

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“…To account for dilution or bulk mass differences between samples, individual integral regions were normalized to the total integral region following exclusion of the water resonance. Individual integrals were thereby standardized to the total integral of all low molecular weight metabolites (16,17). Data sets were imported into the SIMCA package (Umetrics, Umeå, Sweden) and pre-processed using Pareto scaling by weighting each integral region or variable by (1/S k ) 1 ⁄2, where S k represents the standard deviation of the variable.…”

Section: Methodsmentioning

confidence: 99%

High Resolution 1H NMR-based Metabolomics Indicates a Neurotransmitter Cycling Deficit in Cerebral Tissue from a Mouse Model of Batten Disease

Pears

Cooper

Mitchison

et al. 2005

Journal of Biological Chemistry

138

108

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The neuronal ceroid lipofuscinoses (NCLs) constitute a range of progressive neurological disorders primarily affecting children. Although six of the causative genes have been characterized, the underlying disease pathogenesis for this family of disorders is unknown. Using a metabolomics approach based on high resolution 1 H NMR spectroscopy of the cortex, cerebellum, and remaining regions of the brain in conjunction with statistical pattern recognition, we report metabolic deficits associated with juvenile NCL in a Cln3 knock-out mouse model. Tissue from Cln3 null mutant mice aged 1-6 months was characterized by an increased glutamate concentration and a decrease in ␥-amino butyric acid (GABA) concentration in aqueous extracts from the three regions of the brain. These changes are consistent with the reported altered expression of genes involved in glutamate metabolism in older mice and imply a change in neurotransmitter cycling between glutamate/glutamine and the production of GABA. Further variations in myo-inositol, creatine, and N-acetyl-aspartate were also identified. These metabolic changes were distinct from the normal aging/developmental process. Together, these changes represent the first documented pre-symptomatic symptoms of the Cln3 mouse at 1 month of age and demonstrate the versatility of 1 H NMR spectroscopy as a tool for phenotyping mouse models of disease.

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Section: Methodsmentioning

confidence: 99%

High Resolution 1H NMR-based Metabolomics Indicates a Neurotransmitter Cycling Deficit in Cerebral Tissue from a Mouse Model of Batten Disease

Pears

Cooper

Mitchison

et al. 2005

Journal of Biological Chemistry

138

108

View full text Add to dashboard Cite

show abstract

“…Spraul et al have developed automated methods for reducing NMR spectra to provide numerical descriptors of samples for statistical analysis and pattern recognition methods, such as artificial neural networks (55). The methods, tested on high-resolution 1D spectra of biofluids, are expected to be valid for multidimensional and heteronuclear spectra and to be of particular use for complex mixtures.…”

Section: "Atypical" and Indirect Usesmentioning

confidence: 98%

Artificial neural networks for pattern recognition

Corne¹

1996

Concepts Magn. Reson.

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“…These methods fall under what has been termed the chemometric or targeted/quantitative approach [91]. In the chemometric approach, each spectrum is divided into equal or nonequal distant segments called bins or buckets, integrated, and multivariate statistical analysis of spectral patterns are used to identify features responsible for group discrimination [92]. Binning helps remove residual chemical shift drift and reduces data complexity but at the expense of the loss of spectral resolution important for biomarker identification later.…”

Section: Nmr Data Pretreatmentmentioning

confidence: 99%

Analytical and statistical approaches to metabolomics research

Issaq

Van

Waybright

et al. 2009

J of Separation Science

199

136

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Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.

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Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples

Cited by 106 publications

References 23 publications

High Resolution 1H NMR-based Metabolomics Indicates a Neurotransmitter Cycling Deficit in Cerebral Tissue from a Mouse Model of Batten Disease

High Resolution 1H NMR-based Metabolomics Indicates a Neurotransmitter Cycling Deficit in Cerebral Tissue from a Mouse Model of Batten Disease

Artificial neural networks for pattern recognition

Analytical and statistical approaches to metabolomics research

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