Publication

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

Juan A. Botía, Jana Vandrovcova, Paola Forabosco, Sebastian Guelfi, Karishma D'Sa, The United Kingdom Brain Expression Consortium, John Hardy, Cathryn M. Lewis, Mina Ryten & Michael E. Weale.
BMC Systems Biology volume 11, Article number: 47 (2017)


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.


Cite this article

CITATION DETAILS
Citation Botía, J.A., Vandrovcova, J., Forabosco, P. et al. An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks. BMC Syst Biol 11, 47 (2017) doi:10.1186/s12918-017-0420-6
DOI https://doi.org/10.1186/s12918-017-0420-6



Received Accepted Published
26 August 2016 17 March 2017 12 April 2017