Relationship between variable importance measures based on the Pearson correlation across 70 tests. This figure shows the hierarchical cluster tree (dendrogram) of 7 variable importance measures. absPearsonCor is the absolute Pearson correlation between each gene and the dichotomous trait. KruskalWallis stands for the −l o g10 p-value of the Kruskal-Wallis group comparison test (which evaluates whether the gene is differentially expressed between the two groups defined by the binary trait). RFdecreasedAccuracy and RFdecreasedPurity are variable importance measures of the RF. timesSelectedAsCandidates, timesSelectedByForwardRegression and sumAbsCoefByForwardRegression are RGLM measures. These measures are evaluated in 10 tests from each of the 7 empirical expression data sets. In every test, different measures independently score genes for their relationship with a specific dichotomized gene trait. A Pearson correlation matrix was calculated by correlating the scores of different variable importance methods. Matrices across the 70 tests were averaged and the result was transformed to a dissimilarity measure that was subsequently used as input of hierarchical clustering.