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.
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.
Authors: A K Smilde; J A Westerhuis; H C J Hoefsloot; S Bijlsma; C M Rubingh; D J Vis; R H Jellema; H Pijl; F Roelfsema; J van der Greef Journal: Metabolomics Date: 2009-12-04 Impact factor: 4.290
Authors: Ben van Ommen; Jaap Keijer; Robert Kleemann; Ruan Elliott; Christian A Drevon; Harry McArdle; Mike Gibney; Michael Müller Journal: Genes Nutr Date: 2008-06-25 Impact factor: 5.523
Authors: Anna Rosa Sprocati; Chiara Alisi; Valentina Pinto; Maria Rita Montereali; Paola Marconi; Flavia Tasso; Katarzyna Turnau; Giovanni De Giudici; Katarzyna Goralska; Marta Bevilacqua; Federico Marini; Carlo Cremisini Journal: Environ Sci Pollut Res Int Date: 2013-10-03 Impact factor: 4.223