ANOVA–simultaneous component analysis

References

(Wikipedia, 2016) ⇒ https://www.wikiwand.com/en/ANOVA%E2%80%93simultaneous_component_analysis Retrieved 2016-07-31
- Analysis of variance – simultaneous component analysis (ASCA or ANOVA–SCA) is a method that partitions variation and enables interpretation of these partitions by SCA, a method that is similar to principal components analysis (PCA). This method is a multivariate or even megavariate extension of analysis of variance (ANOVA). The variation partitioning is similar to ANOVA. Each partition matches all variation induced by an effect or factor, usually a treatment regime or experimental condition. The calculated effect partitions are called effect estimates. Because even the effect estimates are multivariate, interpretation of these effects estimates is not intuitive. By applying SCA on the effect estimates one gets a simple interpretable result. In case of more than one effect this method estimates the effects in such a way that the different effects are not correlated.

(Smilde et al., 2005) ⇒ Smilde, A. K., Jansen, J. J., Hoefsloot, H. C., Lamers, R. J. A., Van Der Greef, J., & Timmerman, M. E. (2005). ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043-3048. http://bioinformatics.oxfordjournals.org/content/21/13/3043.full
- We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.