Statistical experimental design and partial least squares regression analysis of biofluid metabonomic NMR and clinical chemistry data for screening of adverse drug effects. Antti, H., Ebbels, T. M. D., Keun, H. C., Bollard, M. E., Beckonert, O., Lindon, J. C., Nicholson, J. K., & Holmes, E. Chemometrics and Intelligent Laboratory Systems, 73(1):139–149, September, 2004. Place: Amsterdam Publisher: Elsevier WOS:000223990900016doi abstract bibtex Metabonomic analysis is increasingly recognised as a powerful approach for delineating the integrated metabolic changes in biofluids and tissues due to toxicity, disease processes or genetic modification in whole animal systems. When dealing with complex biological data sets, as generated within metabonomics, as well as related fields such as genomics and proteomics, reliability and significance of identified biomarkers associated with specific states related to toxicity or disease are crucial in order to gain detailed and relevant interpretations of the metabolic fluxes in the studied systems. Since various physiological factors, such as diet, state of health, age, diurnal cycles, stress, genetic drift, and strain differences, affect the metabolic composition of biological matrices, it is of great importance to create statistically reliable decision tools for distinguishing between physiological and pathological responses in animal models. In the screening for new biomarkers or patterns of pathological dysfunction, methods providing statistically valid measures of effect-related changes will become increasingly important as the data within areas such as genomics, proteomics and metabonomics continues to grow in size and complexity. H-1 NMR spectroscopy and mass spectrometry are the principal analytical platforms used to derive the data and, because extensively large data sets are required, as much consideration has to be given to optimum design of experiments (DoE) as for subsequent data analysis. Thus, statistical experimental design combined with partial least squares (PLS) regression is proposed as an efficient approach for undertaking metabonomic studies and for analysis of the results. The method was applied to data from a liver toxicology study in the rat using hydrazine as a model toxin. 1D projections of 2D J-resolved (J-RES) H-1 NMR spectra and the corresponding clinical chemistry parameters of blood serum samples from control and dosed rats (30 and 90 mg/ kg) collected at 48 and 168 h post dose were analysed. Confidence intervals for the PLS regression coefficients were used to create a statistical means for screening of biomarkers in the two combined data blocks (NMR and clinical chemistry data). PLS analysis was also used to reveal the correlation pattern between the two blocks of data as well as the within the two blocks according to dose, time and the interaction dose x time. (C) 2004 Elsevier B.V. All rights reserved.
@article{antti_statistical_2004,
title = {Statistical experimental design and partial least squares regression analysis of biofluid metabonomic {NMR} and clinical chemistry data for screening of adverse drug effects},
volume = {73},
issn = {0169-7439},
doi = {10/cndrbv},
abstract = {Metabonomic analysis is increasingly recognised as a powerful approach for delineating the integrated metabolic changes in biofluids and tissues due to toxicity, disease processes or genetic modification in whole animal systems. When dealing with complex biological data sets, as generated within metabonomics, as well as related fields such as genomics and proteomics, reliability and significance of identified biomarkers associated with specific states related to toxicity or disease are crucial in order to gain detailed and relevant interpretations of the metabolic fluxes in the studied systems. Since various physiological factors, such as diet, state of health, age, diurnal cycles, stress, genetic drift, and strain differences, affect the metabolic composition of biological matrices, it is of great importance to create statistically reliable decision tools for distinguishing between physiological and pathological responses in animal models. In the screening for new biomarkers or patterns of pathological dysfunction, methods providing statistically valid measures of effect-related changes will become increasingly important as the data within areas such as genomics, proteomics and metabonomics continues to grow in size and complexity. H-1 NMR spectroscopy and mass spectrometry are the principal analytical platforms used to derive the data and, because extensively large data sets are required, as much consideration has to be given to optimum design of experiments (DoE) as for subsequent data analysis. Thus, statistical experimental design combined with partial least squares (PLS) regression is proposed as an efficient approach for undertaking metabonomic studies and for analysis of the results. The method was applied to data from a liver toxicology study in the rat using hydrazine as a model toxin. 1D projections of 2D J-resolved (J-RES) H-1 NMR spectra and the corresponding clinical chemistry parameters of blood serum samples from control and dosed rats (30 and 90 mg/ kg) collected at 48 and 168 h post dose were analysed. Confidence intervals for the PLS regression coefficients were used to create a statistical means for screening of biomarkers in the two combined data blocks (NMR and clinical chemistry data). PLS analysis was also used to reveal the correlation pattern between the two blocks of data as well as the within the two blocks according to dose, time and the interaction dose x time. (C) 2004 Elsevier B.V. All rights reserved.},
language = {English},
number = {1},
journal = {Chemometrics and Intelligent Laboratory Systems},
author = {Antti, H. and Ebbels, T. M. D. and Keun, H. C. and Bollard, M. E. and Beckonert, O. and Lindon, J. C. and Nicholson, J. K. and Holmes, E.},
month = sep,
year = {2004},
note = {Place: Amsterdam
Publisher: Elsevier
WOS:000223990900016},
keywords = {PLS analysis, classification, clinical chemistry, design of experiments, h-1, h-1-nmr spectroscopy, lesions, magnetic-resonance spectroscopy, metabonomics, model, nmr, pattern-recognition methods, spectra, urine},
pages = {139--149},
}
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When dealing with complex biological data sets, as generated within metabonomics, as well as related fields such as genomics and proteomics, reliability and significance of identified biomarkers associated with specific states related to toxicity or disease are crucial in order to gain detailed and relevant interpretations of the metabolic fluxes in the studied systems. Since various physiological factors, such as diet, state of health, age, diurnal cycles, stress, genetic drift, and strain differences, affect the metabolic composition of biological matrices, it is of great importance to create statistically reliable decision tools for distinguishing between physiological and pathological responses in animal models. In the screening for new biomarkers or patterns of pathological dysfunction, methods providing statistically valid measures of effect-related changes will become increasingly important as the data within areas such as genomics, proteomics and metabonomics continues to grow in size and complexity. H-1 NMR spectroscopy and mass spectrometry are the principal analytical platforms used to derive the data and, because extensively large data sets are required, as much consideration has to be given to optimum design of experiments (DoE) as for subsequent data analysis. Thus, statistical experimental design combined with partial least squares (PLS) regression is proposed as an efficient approach for undertaking metabonomic studies and for analysis of the results. The method was applied to data from a liver toxicology study in the rat using hydrazine as a model toxin. 1D projections of 2D J-resolved (J-RES) H-1 NMR spectra and the corresponding clinical chemistry parameters of blood serum samples from control and dosed rats (30 and 90 mg/ kg) collected at 48 and 168 h post dose were analysed. Confidence intervals for the PLS regression coefficients were used to create a statistical means for screening of biomarkers in the two combined data blocks (NMR and clinical chemistry data). PLS analysis was also used to reveal the correlation pattern between the two blocks of data as well as the within the two blocks according to dose, time and the interaction dose x time. (C) 2004 Elsevier B.V. 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