Analysis of longitudinal metabolomics data
article
Motivation: Metabolomics datasets are generally large and complex. Using principal component analysis (PCA), a simplified view of the variation in the data is obtained. The PCA model can be interpreted and the processes underlying the variation in the data can be analysed. In metabolomics, often a priori information is present about the data. Various forms of this information can be used in an unsupervised data analysis with weighted PCA (WPCA). A WPCA model will give a view on the data that is different from the view obtained using PCA, and it will add to the interpretation of the information in a metabolomics dataset. Results: A method is presented to translate spectra of repeated measurements into weights describing the experimental error. These weights are used in the data analysis with WPCA. The WPCA model will give a view on the data where the non-uniform experimental error is accounted for. Therefore, the WPCA model will focus more on the natural variation in the data. © Oxford University Press 2004; all rights reserved.
Topics
Analytical researchAlgorithmAnalytic methodAnalytical errorComputer programControlled studyFemaleMaleMathematical computingMetabolic regulationNonhumanNuclear magnetic resonancePrincipal component analysisPriority journalRhesus monkeyScoring systemUrinalysisAlgorithmsAnimalsComputer SimulationGene Expression ProfilingMacaca mulattaMagnetic Resonance SpectroscopyModels, BiologicalPrincipal Component AnalysisProtein Interaction MappingProteinsSignal TransductionTime FactorsUrinalysis
TNO Identifier
238053
ISSN
13674803
Source
Bioinformatics, 20(15), pp. 2438-2446.
Pages
2438-2446
Files
To receive the publication files, please send an e-mail request to TNO Repository.