An efficient not-only-linear correlation coefficient based on clustering: Supplementary Files

Supplementary Files for the manuscript "An efficient not-only-linear correlation coefficient based on machine learning" available at https://doi.org/10.1101/2022.06.15.496326

Supplementary File 1: All pairwise gene correlations using Pearson, Spearman and CCC among the top 5,000 genes in GTEx’s whole blood with the largest variance. Columns indicates whether the gene pair was categorized in the top or bottom 30% of each coefficient, the correlation value, and the significance of the association. Significance is only present for the top 10 gene pairs of each intersection in the “Disagreements” group (Figure 3a, right) where CCC disagrees with Pearson, Spearman or both.

Supplementary File 2: Percentiles of the coefficient values for the top 5,000 genes in GTEx’s whole blood.

Supplementary File 3: Pearson, Spearman and CCC correlations values and their significance for two gene pairs (UTY - KDM6A and DDX3Y - KDM6A) across all tissues in GTEx.