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...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Daniel J. Vis, Johan A. Westerhuis, Age K. Smilde, Jan van der Greef
• Keywords: BMCBI 2007, Metabolomics, Metabolomics Data, Permutation Approach