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Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
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2005 (English)In: The Analyst, ISSN 0003-2654, E-ISSN 1364-5528, Vol. 130, no 5, 701-707 p.Article in journal (Other (popular science, discussion, etc.)) Published
Abstract [en]

LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable ( transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e. g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu ( USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.

Place, publisher, year, edition, pages
2005. Vol. 130, no 5, 701-707 p.
Keyword [en]
National Category
Biological Sciences
URN: urn:nbn:se:umu:diva-13599DOI: 10.1039/b501890kPubMedID: 15852140OAI: diva2:153270
Available from: 2007-05-11 Created: 2007-05-11 Last updated: 2013-03-19
In thesis
1. Multivariate processing and modelling of hyphenated metabolite data
Open this publication in new window or tab >>Multivariate processing and modelling of hyphenated metabolite data
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

One trend in the ‘omics’ sciences is the generation of increasing amounts of data, describing complex biological samples. To cope with this and facilitate progress towards reliable diagnostic tools, it is crucial to develop methods for extracting representative and predictive information. In global metabolite analysis (metabolomics and metabonomics) NMR, GC/MS and LC/MS are the main platforms for data generation. Multivariate projection methods (e.g. PCA, PLS and O-PLS) have been recognized as efficient tools for data analysis within subjects such as biology and chemistry due to their ability to provide interpretable models based on many, correlated variables. In global metabolite analysis, these methods have been successfully applied in areas such as toxicology, disease diagnosis and plant functional genomics.

This thesis describes the development of processing methods for the unbiased extraction of representative and predictive information from metabolic GC/MS and LC/MS data characterizing biofluids, e.g. plant extracts, urine and blood plasma. In order to allow the multivariate projections to detect and highlight differences between samples, one requirement of the processing methods is that they must extract a common set of descriptors from all samples and still retain the metabolically relevant information in the data. In Papers I and II this was done by applying a hierarchical multivariate compression approach to both GC/MS and LC/MS data. In the study described in Paper III a hierarchical multivariate curve resolution strategy (H-MCR) was developed for simultaneously resolving multiple GC/MS samples into pure profiles. In Paper IV the H-MCR method was applied to a drug toxicity study in rats, where the method’s potential for biomarker detection and identification was exemplified. Finally, the H-MCR method was extended, as described in Paper V, allowing independent samples to be processed and predicted using a model based on an existing set of representative samples. The fact that these processing methods proved to be valid for predicting the properties of new independent samples indicates that it is now possible for global metabolite analysis to be extended beyond isolated studies. In addition, the results facilitate high through-put analysis, because predicting the nature of samples is rapid compared to the actual processing. In summary this research highlights the possibilities for using global metabolite analysis in diagnosis.

Place, publisher, year, edition, pages
Umeå: Kemi, 2005. 66 p.
Chemometrics, Curve Resolution, GC/MS, LC/MS, Metabolomics, Metabonomics, Multivariate Analysis and Multivariate Curve Resolution.
National Category
Organic Chemistry
urn:nbn:se:umu:diva-663 (URN)91-7305-922-7 (ISBN)
Public defence
2006-01-27, KB3B1, KBC, Umeå Univeristet, Umeå, 10:00 (English)
Available from: 2005-12-22 Created: 2005-12-22 Last updated: 2009-12-03Bibliographically approved

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Jonsson, PärTrygg, JohanSjöström, MichaelAntti, Henrik
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