stable in all approaches. The results for fixing the number of features to 50 are depicted in Figure \[fig:boxplot50\] while results for 100 and 150 features are presented in Figures S5 and S6. When accounting for multiple testing, no classifier using a fixed number of features performs significantly different from its counterpart using the number of features determined by cross-validation. See p-values of the pairwise Wilcoxon rank test in Tables S4, S6 and S8. As expected, there are only minor differences between the performance of classifiers when the number of features is restricted to 50, 100 and 150 (Tables S3, S5 and S7) with any significant differences favoring single genes. This confirms that the number of features is not a critical parameter. Based on these results, we can conclude that the number of selected features does not explain the observed differences between composite feature classifiers and single gene classifiers.

![ Comparison of the performance of the classifiers when the number of features is trained in the CV procedure (denoted as ‘CV-opt number of features’, same values as in Figure \[fig:auc\_boxplot\]) and when the 50 best scoring features (denoted as ‘50 best features’) are selected for classification. We cannot show the 