A new grid- and modularity-based layout algorithm for complex biological networks

Abstract

The visualization of biological networks is critically important to aid researchers in understanding complex biological systems and arouses interest in designing efficient layout algorithms to draw biological networks according to their topology structures, especially for those networks with potential modules. The algorithms of grid layout series have an advantage in generating compact layouts with overlap-free nodes compared to force-directed; however, extant grid layout algorithms have difficulty in drawing modular networks and often generate layouts of high visual complexity when applied to networks with dense or clustered connectivity structure. To specifically assist the study of modular networks, we propose a grid- and modularity-based layout algorithm (GML) that consists of three stages: network preprocessing, module layout and grid optimization. The algorithm can draw complex biological networks with or without predefined modules based on the grid layout algorithm. It also outperforms other existing grid-based algorithms in the measurement of computation performance, ratio of edge-edge/node-edge crossings, relative edge lengths, and connectivity F-measures. GML helps users to gain insight into the network global characteristics through module layout, as well as to discern network details with grid optimization. GML has been developed as a VisANT plugin ( https://hscz.github.io/Biological-Network-Visualization/ ) and is freely available to the research community.

Performance Evaluation of Hospital Economic Management with the Clustering Algorithm Oriented towards Electronic Health Management

“…The grid-based clustering algorithm mainly dealt with the multiresolutions. He et al [ 31 ] illustrated that the algorithm could classify the data set space, which was dispersed into some units and formed a grid shape gradually. Then, all clustering operations were performed on the grid.…”

visMOP – A Visual Analytics Approach for Multi‐omics Pathways

“…Our proposed layout is based on the idea of using clusters, similar to the Grid‐ and Modularity‐based Layout algorithm by He et al [HLY*19]. For the clustering, the values and statistics about the supplied multi‐omics data are taken into account, such as the average fold change per omics, the number of regulated entities per pathway, etc.…”

“…In these next steps, the space formed by the above‐mentioned high‐level hierarchy nodes is partitioned in a way that both the distance from one cluster to another and the proportion of nodes contained vs. space allocated are considered. He et al [HLY*19] use rectangular regions for the clusters in this step and try to optimize their placement to reduce edge crossings. In contrast, we use a circular layout, in which a Voronoi treemap is used to allocate space [BD05; NB12].…”

“…This shows the semantic relations of the pathways based on the Reactome hierarchy and, thus, facilitates the analysis using biological domain knowledge. He et al [HLY*19], in contrast, again only optimize the layout by using intra‐ and intercluster connectivity and minimizing edge crossings.…”

Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure

“…A network-based approach is useful for analyzing disease–disease relationships and many methods are derived (Iida et al 2020 ), such as MiRNA-disease Association Prediction method (Sumathipala and Weiss 2020 ), dynamic network biomarker method (Yang et al 2018 ), and meta-path-based Disease Network capturing algorithm method et al (Jin et al 2019 ). Modularity, one of the most significant global characteristics of biological networks, has been the subject of intense investigation in systems biology for more than two decades (Grunberg and Del Vecchio 2020 ; He et al 2019 ). This principle is important because it helps to account for the robustness and reliability of biological systems (Kashtan and Alon 2005 ).…”