Detecting modules in biological networks by edge weight clustering and entropy significance - Lecca, Paola; Re, Angela (Frontiers in Genetics, 2015)
Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data. We present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features are the: (1) simultaneous exploitation of network node and edge weights to improve the biological interpretability of connected components detected, (2) assessment of their statistical significance, and (3) identification of emerging topological properties in the connected components. Applying WG-Cluster to a protein-protein network weighted by measurements of differential gene expression permitted to explore the changes in network topology under two distinct (normal vs tumour) conditions.