Supplementary Figure 3: treeClust detects specifically positive linear associations.
From: Co-regulation map of the human proteome enables identification of protein functions
We tested which types of relationships treeClust detects by using a synthetic dataset consisting of 100 variables and 200 proteins, where 0.5% of all possible protein - protein combination have a defined relationship. (a) Precision - recall (PR) analyses show that treeClust separates linear from random relationships perfectly, resulting in an area under the PR curve (AUPRC) of 1. The same result is observed for the three tested correlation-based metrics: PCC, Spearman’s rho and biweight midcorrelation (bicor). The four PR curves overlap fully. (b) TreeClust completely fails to detect exponential or logistic relationships (AUPRC = 0). In contrast, although these pairs receive lower correlation coefficients than linear pairs, they still score high enough with PCC, rho and bicor to be completely separated from the pool of random associations. No metric detects quadratic relationships. (c) Anti-correlations are not identified well by treeClust.
