An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

ABSTRACT:

Background

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ).

Results

We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced ge

SUBMITTER: Botia JA 

PROVIDER: S-EPMC5389000 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature