C3: connect separate connected components to form a succinct disease module

Abstract

Background Precise disease module is conducive to understanding the molecular mechanism of disease causation and identifying drug targets. However, due to the fragmentization of disease module in incomplete human interactome, how to determine connectivity pattern and detect a complete neighbourhood of disease based on this is still an open question. Results In this paper, we perform exploratory analysis leading to an important observation that through a few intermediate nodes, most separate connected components formed by disease-associated proteins can be effectively connected and eventually form a complete disease module. And based on the topological properties of these intermediate nodes, we propose a connect separate connected components (C3) method to detect a succinct disease module by introducing a relatively small number of intermediate nodes, which allows us to obtain more pure disease module than other methods. Then we apply C3 across a large corpus of diseases to validate this connectivity pattern of disease module. Furthermore, the connectivity of the perturbed genes in multi-omics data such as The Cancer Genome Atlas also fits this pattern. Conclusions C3 tool is not only useful in detecting a clearly-defined connected disease neighbourhood of 299 diseases and cancer with multi-omics data, but also helpful in better understanding the interconnection of phenotypically related genes in different omics data and studying complex pathological processes.

Conserved Control Path in Multilayer Networks

“…Consequently, we acquired 99 drug-cancer pairs consisting of 25 drugs using drug repositioning, of which there were 33 drug-cancer pairs that were FDA-approved and 25 drug-cancer pairs that were implied in previous studies ( Supplementary data 5 ). Then, we used disease genes (from GWAS and OMIM data), C3 module [ 39 ], and DIAMOnD module [ 40 ] generated from disease genes to perform drug repositioning for BCLLS. Furthermore, we compared the results of drug repositioning obtained for different cases.…”

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

“…Another class of methods tries to use static prior networks as a base and uses expression data in a contextualization step to find active subnetworks [ 97 – 99 ]. For example, the connect separate connected components (C3) modularizes a network into disease-relevant modules by iteratively connecting sub-networks made of a small number of disease-associated proteins [ 100 ]. DeRegNet combines prior regulatory networks with omics abundance measurements to identify maximally deregulated subnetworks [ 101 ].…”

Multi-omics peripheral and core regions of cancer

“…Therefore, different strategies should be adopted to analyse the disease neighbourhood of different omics studies. Previous methods 8,9,[74][75][76] based on network proximity could only identify mesoscopic cores in transcriptome and somatic mutation aspects, and to present macroscopic cancer neighbourhoods in somatic mutation and CNV aspects. Our CLine and its uniform UCurve identify the common structural properties across cancers and discriminate the differential connectivity pattern between multiple omics aspects.…”

“…Genes related to specific diseases tend to cluster in the network neighbourhood, which gives rise to the concept of disease modules 6 , which usually consist of dozens of genes. To accurately identify the network’s disease modules, researchers have developed the connectivity-based DIAMOnD 8 and C3 algorithms 9 . Both algorithms determine the candidate genes to be imported to connect scattered pathogenic genes and obtain connected disease modules.…”