2007/08/30 by Daniël J. Vis, Johan A. Westerhuis, Age K. Smilde +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Chemistry · #Chromatography in Natural Products #Gene expression and cancer classification #Metabolomics and Mass Spectrometry Studies
paper · pdf · doi:10.1186/1471-2105-8-322
openalex publication_date 2007/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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.