Figure 2.

The GCC can detect the regulatory relationships missed by the PCC, SCC, and BiWt methods. A, The GCC can detect a linear relationship with similar correlation values to the PCC and SCC correlations. B, The PCC failed to infer the relationship in the samples containing outliers, which was detected by the GCC, SCC, and BiWt. The five outlier samples are represented by the red circles in the gray region in seed. C, The GCC was able to identify transient interactions that were overlooked by the PCC, SCC, and BiWt. The expression values of TF and target are only correlated in nine samples in apex out of the 79 samples (red circles in the gray region). Two correlations, GCC1 and GCC2, are produced by the GCC reciprocally using rank and value information of the two genes’ expression data. In the last two columns, the expression data of genes sorted with their own rank information are displayed as black dashed curves, while the expression data of genes sorted with the other gene’s rank information are shown as blue and red solid curves. The Gini correlation can be explained as the difference between the solid and dashed curves weighted by the rank information. “Value” and “Rank” denote the value and the rank information of the gene expression data, respectively.