I have read that when one encounters a confounding variable, the preferred method to account for their effects would be to add this variable to the design formula but not remove them using limma's removebatcheffect function.
My questions on this topic are:
should I remove batch effect using limma's removebatcheffect function if the confounding effect is large and inconsistent as suggested here (https://support.bioconductor.org/p/125386/#125387). For example, when one can see in a pca plot and heatmap that samples still cluster according to the presence or absence of a confounding variable even though it has been added to the design formula (eg when samples cluster by sex after it has been added to the design formula).
should one use the transformed counts after removal of a confounding effect or the untransformed counts (and only add the confounding variable to the design formula) for heatmaps, pca plots and DE analysis?