A new gene co-expression network analysis based on Core Structure Detection (CSD)

TL;DR

This paper introduces a novel gene clustering method based on core structure detection in gene co-expression networks, emphasizing the identification of biologically significant gene clusters and key hub genes.

Contribution

It presents a new approach combining correlation-based dissimilarity and graph coreness to identify central gene clusters and main hub networks, enhancing biological interpretability.

Findings

Identifies biologically meaningful gene clusters.

Highlights importance of core structure nodes.

Provides a new network representation for gene analysis.

Abstract

We propose a novel method to cluster gene networks. Based on a dissimilarity built using correlation structures, we consider networks that connect all the genes based on the strength of their dissimilarity. The large number of genes require the use of the threshold to find sparse structures in the graph. in this work, using the notion of graph coreness, we identify clusters of genes which are central in the network. Then we estimate a network that has these genes as main hubs. We use this new representation to identify biologically meaningful clusters, and to highlight the importance of the nodes that compose the core structures based on biological interpretations.