WGCHNA_SupplementaryMaterial.pdf

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.
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