For supervised multivariate analyses (OPLS-DA in this case), the calculated models were validated and assessed. Firstly, to note that for all the OPLS-DA models of the four datasets, there was clear discrimination between the sample groups in the scores space (Figure 2A and Figure S4). The analysis of variance testing of cross-validated predictive residuals (CV-ANOVA) was used to assess the reliability of the obtained models . The computed OPLS-DA models for the four datasets, to separate multivariate relationships into predictive and orthogonal variation, were statistically good models with p-values significantly lower than 0.05 (Table 2). Furthermore, the response permutation test (with n = 50) was used to validate the predictive capability of the computed OPLS-DA models. In this statistical test the R2 and Q2 values of the true model are compared with that of the permutated model. The test is carried out by randomly assigning to the two different groups, after which the OPLS-DA models are fitted to each permutated class variable. The R2 and Q2 values are then computed for the permutated models and compared to the values of the true models .