Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling. Keun, H. C., Ebbels, T. M. D., Antti, H., Bollard, M. E., Beckonert, O., Holmes, E., Lindon, J. C., & Nicholson, J. K. Analytica Chimica Acta, 490(1-2):265–276, August, 2003. Place: Amsterdam Publisher: Elsevier WOS:000185047900022doi abstract bibtex Variable scaling alters the covariance structure of data, affecting the outcome of multivariate analysis and calibration. Here we present a new method, variable stability (VAST) scaling, which weights each variable according to a metric of its stability. The beneficial effect of VAST scaling is demonstrated for a data set of H-1 NMR spectra of urine acquired as part of a metabonomic study into the effects of unilateral nephrectomy in an animal model. The application of VAST scaling improved the class distinction and predictive power of partial least squares discriminant analysis (PLS-DA) models. The effects of other data scaling and pre-processing methods, such as orthogonal signal correction (OSC), were also tested. VAST scaling produced the most robust models in terms of class prediction, outperforming OSC in this aspect. As a result the subtle, but consistent, metabolic perturbation caused by unilateral nephrectomy could be accurately characterised despite the presence of much greater biological differences caused by normal physiological variation. VAST scaling presents itself as an interpretable, robust and easily implemented data treatment for the enhancement of multivariate data analysis. (C) 2003 Elsevier Science B.V. All rights reserved.
@article{keun_improved_2003,
title = {Improved analysis of multivariate data by variable stability scaling: application to {NMR}-based metabolic profiling},
volume = {490},
issn = {0003-2670},
shorttitle = {Improved analysis of multivariate data by variable stability scaling},
doi = {10/cv3sj3},
abstract = {Variable scaling alters the covariance structure of data, affecting the outcome of multivariate analysis and calibration. Here we present a new method, variable stability (VAST) scaling, which weights each variable according to a metric of its stability. The beneficial effect of VAST scaling is demonstrated for a data set of H-1 NMR spectra of urine acquired as part of a metabonomic study into the effects of unilateral nephrectomy in an animal model. The application of VAST scaling improved the class distinction and predictive power of partial least squares discriminant analysis (PLS-DA) models. The effects of other data scaling and pre-processing methods, such as orthogonal signal correction (OSC), were also tested. VAST scaling produced the most robust models in terms of class prediction, outperforming OSC in this aspect. As a result the subtle, but consistent, metabolic perturbation caused by unilateral nephrectomy could be accurately characterised despite the presence of much greater biological differences caused by normal physiological variation. VAST scaling presents itself as an interpretable, robust and easily implemented data treatment for the enhancement of multivariate data analysis. (C) 2003 Elsevier Science B.V. All rights reserved.},
language = {English},
number = {1-2},
journal = {Analytica Chimica Acta},
author = {Keun, H. C. and Ebbels, T. M. D. and Antti, H. and Bollard, M. E. and Beckonert, O. and Holmes, E. and Lindon, J. C. and Nicholson, J. K.},
month = aug,
year = {2003},
note = {Place: Amsterdam
Publisher: Elsevier
WOS:000185047900022},
keywords = {biofluid NMR, coefficient of variations, data pre-processing, metabolomics, metabonomics, orthogonal signal correction, partial least squares discriminant analysis, spectra, toxicity, variable scaling, variable stability},
pages = {265--276},
}
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