I'm looking for tools which can be used to check the importance of covariates (either continuous or categorical) in explaining information in data (e.g. gene expression data), so as to screen which variables one may want to adjust for when testing in a linear model framework (in limma, DESeq2, etc.).
For example, I have often used the
pcrplot of the ENmix R package, which correlates variables to principal components and gives this useful plot:
(And of course there is always visual screening of the PCAs coloring by variables).
But I'm wondering if anyone knows of more sophisticated methods, or methods from which one can extract more "objective" stats to justify subsequent inclusion/exclusion of variables in the models. For example I've seen the R package pvca but it only works with categorical covariates.
Or else, what is your usual process when you want to do differential testing through linear models and have a lot of phenotypical associated variables?