same data set (*paired setting*). We can, however, also follow an alternative scheme where we train on *all* data sets except the test set, the so-called *merged setting*. More specifically, in this setting four of the five Affymetrix data sets were merged to form a single training data set and the fifth data set was used as test set. Thus, we receive for each feature selection method five AUC values. This increases the size of the training set, and by comparing the results obtained in this setting with the results from the paired setting, we can investigate the effect of the training set size on the classifier performances.

Figure \[fig:boxplot\_pw\_vs\_merged\] depicts the results for the merged setting and the pairwise setting for the CV-optimized feature sets and when only the top 50 features are selected. (Note that, in contrast to the results in Figure \[fig:auc\_boxplot\], this pairwise setting only employs the Affymetrix datasets). The results for the top 100 and 150 features are similar, see Figure S8. Statistical testing shows that in the paired setting (Tables S13-S16) when the number of features is set to 150, [*Lee*]{}employing the MsigDB performs better than the single gene classifier. However, this difference disappears 