ArticleBMC systems biology2017
An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.
Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers, 1 of them a synthesis that pooled it.
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Who cites it
176 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- P4HA1 facilitates SPP1+ tumor-associated macrophages by activating the hypoxia pathway in head and neck squamous cell carcinoma.Scientific reports · 2025Article
- Integrative analysis of transcriptomics and drug-target networks identifies SMN1 as a novel biomarker and therapeutic target for amyotrophic lateral sclerosis.Journal, genetic engineering & biotechnology · 2025Article
- Genetic drivers of progression in Alzheimer's disease are distinct from disease risk.medRxiv : the preprint server for health sciences · 2025Article
- Systematic Analysis of Fertility Conversion via WGCNA Implicates a Compensatory Regulatory Network in a Reverse Thermosensitive Genic Male Sterility Line of Eggplant (International journal of molecular sciences · 2025Article
- Integrating omics and functional data via representation learning to prioritize candidate genes for pleiotropic effect in dairy sheep.PNAS nexus · 2025Article
- Whole genome duplication drives transcriptome reprogramming in response to drought in alfalfa.Plant cell reports · 2025Article
- Increased burden of rare risk variants across gene expression networks predisposes to sporadic Parkinson's disease.Cell reports · 2025Article
- Human longevity and Alzheimer's disease variants act via microglia and oligodendrocyte gene networks.Brain : a journal of neurology · 2025Article
116 more citing papers are in PubMed but not listed here.
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10 authors.
Funding
Abstract
backgroundWeighted 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 ).
resultsWe 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.
conclusionsThe 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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