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Bioinformatics Advance Access published online on May 12, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti476
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received December 23, 2004
Revised April 21, 2005
Accepted April 27, 2005

Article

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

Age K. Smilde 1*, Jeroen J. Jansen 2, Huub C. J. Hoefsloot 2, Robert-Jan A. N. Lamers 3, Jan van der Greef 4, and Marieke E. Timmerman 5

1 Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands; TNO Quality of life, PO Box 360, 3700 AJ Zeist, The Netherlands
2 Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands
3 TNO Quality of life, PO Box 360, 3700 AJ Zeist, The Netherlands
4 TNO Quality of life, PO Box 360, 3700 AJ Zeist, The Netherlands; Center for Medical Systems Biology, LACDR, Leiden University, Gorleaus Laboratories, 2300 RA Leiden, The Netherlands
5 Heymans Institute of Psychology, DPMG, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands

* To whom correspondence should be addressed.
Age K. Smilde, E-mail: asmilde{at}science.uva.nl


  Abstract

Motivation: Data sets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such data sets 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 data set from a metabolomics experiment with time and dose factors.

Availability: M-files for MATLAB for the algorithm used in this research and the Appendices to this manuscript are available at: http://www-its.chem.uva.nl/research/pac/Software/ or at http://www.bdagroup.nl.


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