Principal component and discriminant analysis.

<p><b>(A)</b> Principal component analysis (PCA) indicates a strong center specific effect. Each center is depicted with a different colour code, e.g. red data points clusters together and do not allow to separate clusters independent of each center. (<b>B</b>) PCA within the groups of all clusters implicates some difference, however the clusters cannot reliably be separated. (<b>C</b>) The discriminant analysis highlights that the clusters can be separated. Each cluster is shown with a different colour code.