Performance Assessment of the Network Reconstruction Approaches on Various Interactomes - Arici, M. Kaan; Tuncbag, Nurcan (Frontiers in Molecular Biosciences, 2021)
The single omic data type is not capable of fully representing cellular activity. Additionally, individual entities do not behave isolated but rather function synergistically or may not be functional in the presence of some other entities. Beyond the list of molecules from each data type, there is a necessity to consider collectively multiple sets of omic data and to reconstruct the relations between these molecules. Especially, reconstruction of the signaling pathways is crucial to understand disease biology, because abnormal cellular signaling may be pathological. The main challenge is how to integrate this data together in an accurate way and how to develop efficient methods to reverse engineer from this big data to explain the molecular basis of disease. In this study, we aim to comparatively analyze the performance of a set of networks reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of critical proteins, structural information, and their bias to well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized-PageRank with flux, and prize-collecting Steiner forest algorithms (PCSF). Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed with the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage, disease- or tissue- specificity of each interactome may vary, which may result in differences in the reconstructed networks.