Topological complex networks properties for gene community detection strategy: DRD2 case study

Abstract

Gene interactions can suitably be modeled as communities through weighted complex networks. However, the problem to efficiently detect these communities, eventually gaining biological insight from them, is still an open question. This paper presents a novel data-driven strategy for community detection and tests it on the specific case study of DRD2 gene coding for the D2 dopamine receptor, which plays a prominent role in risk for Schizophrenia. We adopt a combined use of centrality and topological properties to detect an optimal network partition. We find that 21 genes belongs with our target community with probability P ≥ 90 %. The robustness of the partition is assessed with two independent methodologies: (i) fuzzy c-means and (ii) consensus analyses. We use the first one to measure how strong the membership of these genes to the DRD2 community is and the latter to confirm the stability of the detected partition. These results show an interesting reduction (∼80 %) of the target community size. Moreover, to allow this validation on different case studies, the proposed methodology is available on an open cloud infrastructure, according to the Software as a Service paradigm (SaaS).