BioNetStat: A Tool for Biological Networks Differential Analysis - Jardim, Vinícius Carvalho; Santos, Suzana de Siqueira; Fujita, Andre; Buckeridge, Marcos Silveira (Frontiers in Genetics, 2019)
The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks (e.g., control versus disease). However, biological systems often present more than two biological states (e.g., tumor grades). To compare two or more networks simultaneously, we developed \verb+BioNetStat+, a \verb+Bioconductor+ package with a user-friendly graphical interface. \verb+BioNetStat+ compares correlation networks based on the probability distribution of a feature of the graph (e.g., centrality measures). The analysis of the structural alterations on the network reveals significant modifications in the system. For example, the analysis of centrality measures provides information about how the ``importance'' of the nodes changes among the biological states. We evaluated the performance of \verb+BioNetStat+ in both toy models and two case studies, namely gene expression of tumor cells and plant metabolism. Results based on simulated scenarios suggest that the statistical power of \verb+BioNetStat+ is less sensitive to the increase of the number of networks than Gene Set Coexpression Analysis (\verb+GSCA+). Moreover, \verb+BioNetStat+ identified altered networks associated with signaling pathways that were not identified by \verb+GSCA+. Furthermore, the proposed tool was able to identify nodes with modified centrality. Altogether, \verb+BioNetStat+ complements \verb+GSCA+.