Modern omics experiments pertain not only to the measurement of many variables but also follow complex
experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using
multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation
induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the
measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper
interpretatio