Hypergraph-based analysis of weighted gene co-expression hypernetwork.

Background: With the rapid advancement of gene sequencing technologies, Traditional weighted gene co-expression network analysis (WGCNA), which relies on pairwise gene relationships, struggles to capture higher-order interactions and exhibits low computational efficiency when handling large, complex datasets. Methods: To overcome these challenges, we propose a novel Weighted Gene Co-expression Hypernetwork Analysis (WGCHNA) based on weighted hypergraph, where genes are modeled as nodes and samples as hyperedges. By calculating the hypergraph Laplacian matrix, WGCHNA generates a topological overlap matrix for module identification through hierarchical clustering. Results: Results on four gene expression datasets show that WGCHNA outperforms WGCNA in module identification and functional enrichment. WGCHNA identifies biologically relevant modules with greater complexity, particularly in processes like neuronal energy metabolism linked to Alzheimer's disease. Additionally, functional enrichment analysis uncovers more comprehensive pathway hierarchies, revealing potential regulatory relationships and novel targets. Conclusion: WGCHNA effectively addresses WGCNA's limitations, providing superior accuracy in detecting gene modules and deeper insights for disease research, making it a powerful tool for analyzing complex biological systems.