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Statistical validation of megavariate effects in ASCA

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Author: Vis, D.J. · Westerhuis, J.A. · Smilde, A.K. · Greef, J. van der
Institution: TNO Kwaliteit van Leven
Source:BMC Bioinformatics, 8
Identifier: 240152
doi: doi:10.1186/1471-2105-8-322
Article number: No.: 322
Keywords: 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


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.