Figure 1

Two parameters - used for final model selection. Each dot corresponds to one model that performs well in cross-validation in the training data. Each row corresponds to a given training dataset (name on the left), each column to the corresponding test dataset (name in header). For instance, the field row 1 (Normal) – column 4 (CIN2+(a)) shows the two parameters (x-axis Eval1, y-axis EV1dist) for all >300 models selected from the training dataset Normal (LOO-prediction-accuracy > 0.65), when applied to the test data CIN2+(a). For better visualization, the 10% of the models predicting the test data best are shown in red, the next 10% (between deciles 1 and 2) are coloured green and the next (between deciles 2 and 3) blue. Black dots represent the remaining 70%. Eval1 is the normalized largest eigenvalue of the covariance matrix taken from the methylation matrix of the test data. EV1dist is the Euclidean distance between the leading Eigenvectors of the model’s covariance matrix in the training data and in the test data.