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BMCBI
2007

Statistical validation of megavariate effects in ASCA

8 years 8 months ago
Statistical validation of megavariate effects in ASCA
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, b...
Daniel J. Vis, Johan A. Westerhuis, Age K. Smilde,
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Daniel J. Vis, Johan A. Westerhuis, Age K. Smilde, Jan van der Greef
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