Measuring rank robustness in scored protein interaction networks

Many protein interaction databases provide confidence scores based on the experimental evidence underpinning each in- teraction. The databases recommend that protein interac- tion networks (PINs) are built by thresholding on these scores. We demonstrate that varying the score threshold can re- sult in PINs with significantly different topologies. We ar- gue that if a node metric is to be useful for extracting bio- logical signal, it should induce similar node rankings across PINs obtained at different thresholds. We propose three measures—rank continuity, identifiability, and instability— to test for threshold robustness. We apply these to a set of twenty-five metrics of which we identify four: number of edges in the step-1 ego network, the leave-one-out dif- ference in average redundancy, average number of edges in the step-1 ego network, and natural connectivity, as robust across medium-high confidence thresholds. Our measures show good agreement across PINs from different species and data sources. However, analysis of synthetically gen- erated scored networks shows that robustness results are context-specific, and depend both on network topology and on how scores are placed across network edges.