Network-based Diseasome Construction from Multi-omics Data and RadTrix Visualization

Gene-gene association studies are essential for genomic analysis in network medicine. Multi-omics datasets of multiple disease phenotypes are used to construct a disease-gene network or diseasome, which gives a set of gene nodes much larger than the set of disease phenotypes. The resulting network is an unbalanced bipartite graph, which leads to cluttered visualizations. In the state of the art, a relatively small set of top-ranked genes is used for exploratory analysis to resolve the issue of unbalancedness, but that approach usually limits the scope of the analysis. Hence, we use a novel hybrid graph layout, RadTrix, for representing an unbalanced diseasome. We design a novel browser-based tool, RadTrixVis, to integrate the diseasome construction from multi-omics data and its RadTrix-based visualization. We further propose novel gene ranking and filtering techniques to improve the efficiency of our tool. We demonstrate the use of RadTrixVis through a case study of seven oncological phenotypes from The Cancer Genome Atlas (TCGA). Our results show the effectiveness of RadTrixVis in the exploratory study of the diseasome.