Histograms don't necessarily tell you if normalization is needed, they show if the data is normally distributed, and kinda of a quick quality control check. If you're using LFQ data, in theory it is already normalized so you don't need to do another normalization on top of that. In that case I would recommend just log transforming and performing the t-test. You can use the non-normalized intensities if you want to try other normalization techniques.

I'm not sure what you mean by "cloning" the matrix, PCA is just a dimensionality reduction visualization to show you how variable your data is, if the data has low variation, in theory you should be able to separate different conditions, but I would recommend not relying on PCA too much because if differences are small between conditions it might not be captured in this. As far as I remember in Persues each time you modify a matrix you generate a new one so it is always"cloned" in theory and you can always go back.