I was looking at some PCA plots taken from an experiment. These data don't cluster in their first two principal components and if I dig a little bit deeper int he analysis and look at other combinations I can see a clear clustering when comparing 5 (3% of variance) vs 6 (2% of variance).
What does that mean? We would have expected a big separation in the first 2 main principal components. Does it mean that the expected separation is minimal and is explained by the (3+2)%=5% of variance of the whole experiment?
Thanks in advance.