Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Abstract

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network 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 cancer driver proteins, structural information of protein interactions, and the bias toward 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 (PCSF) approaches. 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 using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics

“…Graph-based filters aim to trim networks while keeping important nodes such as seeds, hubs or bottlenecks. A new graph-based filter in version 2.0 is the Prize-collecting Steiner Forest (PCSF) algorithm ( 34 ), which has been shown to give balanced performance in a recent benchmark study ( 35 ). To further assist users in network refinement, we have added a topology dialog to show graphical summaries of node degree and betweenness distributions for each subnetwork.…”

Unveiling Hidden Connections in Omics DataviapyPARAGON: an Integrative Hybrid Approach for Disease Network Construction

“…Recent studies have shown that when integrating different types of biological data (such as genomic, proteomic, and transcriptomic data) to reconstruct signaling networks, using hybrid approaches can be more effective than relying on a single method alone 16 . The accuracy of reconstructed networks is also highly dependent on the quality of the reference interactome 42,43 . However, there are tradeoffs involved in increasing the number of interactions in the reference interactome.…”

“…Each available interactome has a specific evaluation and scoring scheme to integrate PPIs from different resources 42 . In this study, we used ConsensusPathDB, HIPPIE v2.2, and HIPPIE v2.3 which have different topological features (Supplementary Table 1).…”

“…We separately calculated precision, recall, F1 scores, and area under the precision-recall curve (AUPRC) for each pathway and each cancer specific network 42 . We created grid search parameter sets for tools.…”

From time-series transcriptomics to gene regulatory networks: A review on inference methods

“…[ 122 ]. In addition, the structural features of inferred networks, such as PageRank, heat diffusion, the shortest path, etc., can be used to compare GRNs inferred from different methods [ 123 ], whereas other metrics can be used to define the method performance, such as stability (across simulations, artificially removing measurements), identification of network motifs, and computational time and memory usage [ 98 ].…”