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An additional k-means clustering step improves the biological features of WGCNA
gene co-expression networks
#MMPMID28403906
Botía JA
; Vandrovcova J
; Forabosco P
; Guelfi S
; D'Sa K
; Hardy J
; Lewis CM
; Ryten M
; Weale ME
BMC Syst Biol
2017[Apr]; 11
(1
): 47
PMID28403906
show ga
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
genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on
average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4)
improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more
accurate partitions in simulated data according to a range of similarity indices.
CONCLUSIONS: The results obtained from our investigations indicate that our
k-means method, applied as an adjunct to standard WGCNA, results in better
network partitions. These improved partitions enable more fruitful downstream
analyses, as gene modules are more biologically meaningful.
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