difference between *unrestr* and I2D remains significant. Moreover, as indicated earlier, the optimization of the number of features by cross-validation introduces significant variation in the number of features without resulting in large performance changes. To eliminate this source of variation from the comparison, we fixed, as before, the number of features to 50, 100 and 150 and repeated the comparisons. The results are depicted in Figures \[fig:restricted\_genes\]B and S7, and Tables S10-S12. We can only find significant differences between the unrestricted single gene classifier and KEGG when the 50 best features are selected and the I2D when employing the 150 best features. However, both of these differences disappear when multiple testing correction is performed. We therefore conclude that the starting gene set has a minor influence on the single gene classifiers. Hence we can reject the hypothesis that feature extraction approaches employing secondary data sources are put at a disadvantage since they can not exploit the full set of genes present on the array.

A\
![ We compared the performance of the single gene classifier trained on all genes present on the microarray ([*unrestr.*]{}) with the performance of single gene classifiers that only employ genes present in the secondary data 