ANOVA-simultaneous component analysis (ASCA): A new tool for analyzing designed metabolomics data
article
Motivation: Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. Results: We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors. © The Author 2005. Published by Oxford University Press. All rights reserved.
Topics
Analytical researchBiomedical researchAscorbic acidAnalysis of varianceAnalytic methodAnimal experimentAnimal modelBiostatisticsControlled studyData analysisDrug dose regimenDrug urine levelExperimental designKnee osteoarthritisMaleMetabolomicsMultivariate analysisNonhumanPriority journalStatistical analysisStructure analysisTimeVarianceAlgorithmsAnalysis of VarianceAnimalsAscorbic AcidBiological MarkersComputer SimulationDose-Response Relationship, DrugEnergy MetabolismGene Expression ProfilingGuinea PigsMaleModels, BiologicalModels, StatisticalMultivariate AnalysisOsteoarthritisProteomeSoftwareTreatment Outcome
TNO Identifier
238565
ISSN
13674803
Source
Bioinformatics, 21(13), pp. 3043-3048.
Pages
3043-3048
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