K-Module Algorithm: An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks.

ABSTRACT: Among biological networks, co-expression networks have been widely studied. One of the most commonly used pipelines for the construction of co-expression networks is weighted gene co-expression network analysis (WGCNA), which can identify highly co-expressed clusters of genes (modules). WGCNA identifies gene modules using hierarchical clustering. The major drawback of hierarchical clustering is that once two objects are clustered together, it cannot be reversed; thus, re-adjustment of the unbefitting decision is impossible. In this paper, we calculate the similarity matrix with the distance correlation for WGCNA to construct a gene co-expression network, and present a new approach called the k-module algorithm to improve the WGCNA clustering results. This method can assign all genes to the

SUBMITTER: Hou J 

PROVIDER: S-EPMC7828115 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature