PCA is done with raw values. One way to visualize what PCA is doing is by thinking of your data as an n-dimensional cloud where n equals the number of variables that you have in your dataset. The principal axes are defined to describe the most variation in this cloud and to be orthogonal (non-correlated) with each other.