how can principle component analysis be used to find similar patterns of variance in multiple group members?

Question

group analysis somehow gets messed up when creating an aggregate dataset array of several subjects.

Answer 1

Principal components analysis is a way to reduce the dimensionality of a data set by performing a linear transformation on it. The result is that the dimension that exhibits the greatest variance will lie on the first coordinate, with decreasing variance lying on sebsequent coordinates. If your data set seems to have lost coherence as a result of the analysis, it's possible that you used too few dimensions. The would have resulted in screening out a variance that was actually significant.