ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data.
BACKGROUND
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.
Smilde
Age K
AK
Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. asmilde@science.uva.nl
Jansen
Jeroen J
JJ
Hoefsloot
Huub C J
HC
Lamers
Robert-Jan A N
RJ
van der Greef
Jan
J
Timmerman
Marieke E
ME
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D001205:Ascorbic Acid / Q000627:therapeutic use; D015415:Biomarkers / Q000652:urine; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004734:Energy Metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D006168:Guinea Pigs; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D010003:Osteoarthritis / Q000175:diagnosis / Q000188:drug therapy / Q000652:urine*; D020543:Proteome / Q000378:metabolism*; D012984:Software; D016896:Treatment Outcome
D015415:Biomarkers; D020543:Proteome; D001205:Ascorbic Acid