I've seen PCA improperly applied in genetic research quite often. I wanted to clarify : when is it appropriate to use PCA as a visualization tool in your analysis?
1) Rarely is the % variance reported for the components. With human data it's been my experience that the first three components (which are often plotted) tend to contain very little % of the variance. How meaningful are you visual results (i.e. clustering) when the first three components cumulatively account for only (say) 10% of the variance?
2) Once you've actively performed feature selection, let's say simple a t-test, and you've widdeled your large data set down to a small set of features, should you perform PCA to visualize clusters? I have heard it argued that since you're so actively doing feature selection that PCA clustering, after the fact, is not really relevant. Is that true?
3) If you are going perform PCA, what are important parameters to report? I expect the %Variance each component covers, but is there something else?