Weighted Gene Co-Expression Network Analysis

WGCNA is an approach utilizing gene expression data to construct co-expression networks weighted for high correlations (Langfelder and Horvath, 2008) and was used in this study to evaluate correlation between lncRNAs and mRNAs. The RNA-seq data was used as input for the R package WGCNA (Langfelder and Horvath, 2008; R Core Team, 2021), from which a pairwise bi-weight mid-correlation matrix was computed and then transformed into an adjacency matrix. To construct a scale-free network, each absolute mid-correlation value was raised to a soft-thresholding power. Soft-thresholding amplified disparity between strong and weak correlations, leading to the construction of the scale-free network. We chose power 7, which was the lowest power for which the scale-free topology fit index reached 0.8. To minimise the spurious connections, WGCNA utilised the topological overlap measure (TOM) accounting for how large the overlap of each gene pair’s network neighbours. The TOM matrix considered as a similarity measure was then transformed into pairwise dissimilarity measure (calculated by 1-TOM) for the hierarchical clustering of genes. From this, tightly connected genes would be clustered for module assignment (dynamic tree cut algorithm), and unassigned genes with weak connections would not be considered for further analyses. The default value of 0.25 was set as the threshold for cut height to merge possible similar modules. The expression level of each module was represented by an eigengene value. Module membership values were also calculated, which reflect the degree of correlation between genes and modules. Higher absolute values of module memberships mean stronger correlation, while zero values mean no association.