median. **B:** This panel shows the result of pairwise comparisons between all combinations of feature extraction methods and secondary data sources. If, for a given combination of training and test data set, the AUC value of classifier $i$ is higher (lower) than the AUC value of classifier $j$ on the same test data set, it is counted as a win (loss) for classifier $i$. Element $(i,j)$ in the matrix represents the $\log_2$ ratio of wins to losses of method $i$ compared to method $j$. Green indicates an overall win, red an overall loss and white represents draws. The rows and columns are sorted as in Panel A. [**Abbreviations:**]{} SG: Single genes; C: [*Chuang*]{}; L: [*Lee*]{}and T: [*Taylor*]{}. []{data-label="fig:auc_boxplot"}](Plots/Winchart_permuted_Experiment_01_NMCv1.pdf "fig:"){width="3.5in"}

Four hypotheses regarding the lack of observed performance differences {#four-hypotheses-regarding-the-lack-of-observed-performance-differences .unnumbered}
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Next we formulated a number of hypotheses that could explain why classifiers employing secondary data sources do not outperform single gene classifiers. These hypotheses relate to (1) the feature selection approach employed; (2) the starting set of genes employed in each of the approaches; (3) the effect of the training set size on performance and (4) the homogeneity of the data set employed. In the following sections we will 