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BMC Syst Biol. 2017 Apr 12;11(1):47. doi: 10.1186/s12918-017-0420-6.

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

Author information

1
Department of Molecular Neuroscience, Institute of Neurology, University College London, Queen Square, London, WC1N, UK. j.botia@ucl.ac.uk.
2
Department of Medical & Molecular Genetics, School of Medical Sciences, King's College London, Guy's Hospital, London, SE1 9RT, UK. j.botia@ucl.ac.uk.
3
Department of Medical & Molecular Genetics, School of Medical Sciences, King's College London, Guy's Hospital, London, SE1 9RT, UK.
4
Istituto di Ricerca Genetica e Biomedica, CNR, Cittadella Universitaria di Monserrato, Monserrato, 09042, CA, Italy.
5
Department of Molecular Neuroscience, Institute of Neurology, University College London, Queen Square, London, WC1N, UK.

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 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.

KEYWORDS:

Assessment of better gene clusters on bulk tissue; Gene co-expression networks on brain; K-means applied to WGCNA

PMID:
28403906
PMCID:
PMC5389000
DOI:
10.1186/s12918-017-0420-6
[Indexed for MEDLINE]
Free PMC Article

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