Abstract
Background: Brain implants have significant potential for therapeutic applications and neuroscience research, but complex tissue responses often compromise their long-term stability. To address this challenge, differential coexpression analysis can be used to identify key molecular regulators involved in brain implant responses. Results: We developed DiffCoRank, an integrated framework that improves differential coexpression analysis by integrating the techniques of RNA-Seq data preprocessing, gene filtering, correlation-based module identification, and network analysis to discover differentially coexpressed gene clusters. A key innovation of our approach is false discovery rate (FDR)-based selection of strongly connected genes (SCGs), by which we improve detection of strong coexpression patterns that otherwise could be lost to spurious correlations. To enhance the identification of different modules, we employ a hybrid clustering technique that combines uniform manifold approximation and projection (UMAP) with density-based spatial clustering of applications with noise (DBSCAN). We propose a multicriteria hub gene ranking system incorporating network centrality metrics such as degree, closeness, betweenness, and eigenvector centrality to prioritize biologically relevant genes. Conclusions: Our method successfully identified important gene modules linked to stress responses, immunological regulation, and axonal pathfinding from the RNA-Seq data of implanted rat brain tissue. Furthermore, we compared our results to those of other existing coexpression analysis frameworks, showing that our method successfully identifies unique regulatory processes and consistent coexpression patterns. Our research offers novel insights into the molecular processes that explain implant-tissue interactions and possible approaches to improve the robustness and biocompatibility of brain interfaces.