Title
Statistical validation of megavariate effects in ASCA
Author
Vis, D.J.
Westerhuis, J.A.
Smilde, A.K.
van der Greef, J.
TNO Kwaliteit van Leven
Publication year
2007
Abstract
Background: Innovative extensions of (M) ANOVA gain common ground for the analysis of designed metabolomics experiments. ASCA is such a multivariate analysis method; it has successfully estimated effects in megavariate metabolomics data from biological experiments. However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist. Methods: A permutation approach is used to validate megavariate effects observed with ASCA. By permuting the class labels of the underlying experimental design, a distribution of no-effect is calculated. If the observed effect is clearly different from this distribution the effect is deemed significant. Results: The permutation approach is studied using simulated data which gave successful results. It was then used on real-life metabolomics data set dealing with bromobenzene-dosed rats. In this metabolomics experiment the dosage and time-interaction effect were validated, both effects are significant. Histological screening of the treated rats' liver agrees with this finding. Conclusion: The suggested procedure gives approximate p-values for testing effects underlying metabolomics data sets. Therefore, performing model validation is possible using the proposed procedure. © 2007 Vis et al; licensee BioMed Central Ltd.
Subject
Biology
Analytical research
bromobenzene
article
controlled study
mathematical analysis
mathematical variable
metabolomics
methodology
multivariate analysis of variance
nonhuman
principal component analysis
protein function
rat
statistical analysis
statistical model
validation process
animal
dose response
drug effect
genetics
genomics
liver
metabolism
multivariate analysis
time
validation study
Rattus
Animals
Bromobenzenes
Data Interpretation, Statistical
Dose-Response Relationship, Drug
Genomics
Liver
Metabolic Networks and Pathways
Models, Statistical
Multivariate Analysis
Rats
Time Factors
To reference this document use:
http://resolver.tudelft.nl/uuid:22e23780-c52c-49d4-bac7-98d79594bcfa
DOI
https://doi.org/10.1186/1471-2105-8-322
TNO identifier
240152
ISSN
1471-2105
Source
BMC Bioinformatics, 8
Article number
No.: 322
Document type
article