Osteoarthritis Research Today is a free monthly online journal that collates and summarizes the latest research about Osteoarthritis, including details on treatment, symptoms, causes, medication.

ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data.

Smilde AK, Jansen JJ, Hoefsloot HC, Lamers RJ, van der Greef J, Timmerman ME

Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. asmilde@science.uva.nl

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.

Published 16 June 2005 in Bioinformatics, 21(13): 3043-8.
Full-text of this article is available online (may require subscription).

© 2004-2008 Osteoarthritis Research Today. All Rights Reserved.