A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers

摘要

Author Summary Pan-cancer proteomic datasets from The Cancer Genome Atlas provide a unique opportunity to study the functions of proteins in human cancers. Such datasets, where proteins are measured in different conditions and where correlations are informative, can enable the discovery of potentially causal protein-protein interactions, which may in turn shed light on the function of proteins. However, it has been shown that the dominant correlations in a system can be the result of parallel transitive (i.e. indirect) interactions. A wide suite of computational methods has been proposed in the literature for the discrimination between direct and transitive interactions. These methods have been extensively tested for their performance in gene regulatory network inference due to the prevalence of mRNA data. However, the understanding of the performance and limitations of these methods in retrieving curated pathway interactions is lacking. Here, we utilize a high-throughput proteomic dataset from The Cancer Genome Atlas to systematically test different families of network inference methods. We observe that most methods are able to achieve a similar level of performance provided their parameter space is sufficiently explored; but a group of six methods consistently rank highly among the tested methods. The protein-protein interactions inferred by the high-performing methods reveal the pathways that are shared by or specific to different cancer types.