Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS datas - A potential tool for multi-parametric diagnosis. Jonsson, P., Johansson, E. S., Wuolikainen, A., Lindberg, J., Schuppe-Koistinen, I., Kusano, M., Sjostrom, M., Trygg, J., Moritz, T., & Antti, H. Journal of Proteome Research, 5(6):1407–1414, June, 2006. Place: Washington Publisher: Amer Chemical Soc WOS:000237973400012doi abstract bibtex A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e. g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample ( similar to 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.
@article{jonsson_predictive_2006,
title = {Predictive metabolite profiling applying hierarchical multivariate curve resolution to {GC}-{MS} datas - {A} potential tool for multi-parametric diagnosis},
volume = {5},
issn = {1535-3893},
doi = {10.1021/pr0600071},
abstract = {A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e. g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample ( similar to 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.},
language = {English},
number = {6},
journal = {Journal of Proteome Research},
author = {Jonsson, Par and Johansson, Elin Sjovik and Wuolikainen, Anna and Lindberg, Johan and Schuppe-Koistinen, Ina and Kusano, Miyako and Sjostrom, Michael and Trygg, Johan and Moritz, Thomas and Antti, Henrik},
month = jun,
year = {2006},
note = {Place: Washington
Publisher: Amer Chemical Soc
WOS:000237973400012},
keywords = {chemometrics, chromatography, clinical diagnosis, components, curve resolution, design, gc-ms, genomics, h-mcr, high-throughput, identifying differences, metabolic profiling, metabolomics, metabonomics, o-pls, projections, samples, strategy},
pages = {1407--1414},
}
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Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e. g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample ( similar to 15 min in total). 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The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample ( similar to 15 min in total). 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