After I performed the standard Seurat workflow, I found top expressed markers are ribosomal genes in two clusters in my dataset.
My question is should I remove these two cell clusters and then perform normalization, scale, etc. again on the rest of the cell populations? Or, I can go back to the very beginning, grep ribosomal genes from my top VariableFeatures, remove them, and then perform dimension reduction and clustering with the rest of VariableFeatures? Which method do you think make more sense?