Title
ANOVA-simultaneous component analysis (ASCA): A new tool for analyzing designed metabolomics data
Author
Smilde, A.K.
Jansen, J.J.
Hoefsloot, H.C.J.
Lamers, R.J.A.N.
van der Greef, J.
Timmerman, M.E.
TNO Kwaliteit van Leven
Publication year
2005
Abstract
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.
Subject
Chemistry Nutrition
Analytical research
Biomedical research
ascorbic acid
analysis of variance
analytic method
animal experiment
animal model
article
biostatistics
controlled study
data analysis
drug dose regimen
drug urine level
experimental design
knee osteoarthritis
male
metabolomics
multivariate analysis
nonhuman
priority journal
statistical analysis
structure analysis
time
variance
Algorithms
Analysis of Variance
Animals
Ascorbic Acid
Biological Markers
Computer Simulation
Dose-Response Relationship, Drug
Energy Metabolism
Gene Expression Profiling
Guinea Pigs
Male
Models, Biological
Models, Statistical
Multivariate Analysis
Osteoarthritis
Proteome
Software
Treatment Outcome
To reference this document use:
http://resolver.tudelft.nl/uuid:b0b14b25-b395-47e7-a8e1-76f3be009853
DOI
https://doi.org/10.1093/bioinformatics/bti476
TNO identifier
238565
ISSN
1367-4803
Source
Bioinformatics, 21 (13), 3043-3048
Document type
article