SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data.

Abstract

Weighted gene co-expression network analysis (WGCNA) is among the most widely employed methods in bioinformatics. WGCNA enables the identification of gene clusters (modules) exhibiting correlated expression patterns, the association of these modules with traits, and the exploration of candidate biomarker genes by focusing on hub genes within the modules. WGCNA has been successfully applied in diverse biological contexts. However, conventional algorithms manifest three principal limitations: the assumption of scale-free topology, the requirement for parameter tuning, and the neglect of regression line slopes. These limitations are addressed by SGCRNA. SGCRNA provides Julia functions for the analysis of co-expression networks derived from various types of biological data, such as gene expression data. The Julia packages and their source code are freely available at https://github.com/C37H41N2O6/SGCRNAs.jl.