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

Overview of attention for article published in BMC Systems Biology, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#43 of 1,127)
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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28 tweeters

Citations

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123 Dimensions

Readers on

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168 Mendeley
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Title
An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks
Published in
BMC Systems Biology, April 2017
DOI 10.1186/s12918-017-0420-6
Pubmed ID
Authors

Juan A. Botía, Jana Vandrovcova, Paola Forabosco, Sebastian Guelfi, Karishma D’Sa, John Hardy, Cathryn M. Lewis, Mina Ryten, Michael E. Weale

Abstract

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 ). 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 28 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 168 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 22%
Researcher 31 18%
Student > Master 19 11%
Student > Bachelor 17 10%
Professor 8 5%
Other 24 14%
Unknown 32 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 50 30%
Agricultural and Biological Sciences 40 24%
Computer Science 16 10%
Medicine and Dentistry 6 4%
Chemistry 4 2%
Other 14 8%
Unknown 38 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 August 2021.
All research outputs
#1,703,949
of 18,792,779 outputs
Outputs from BMC Systems Biology
#43
of 1,127 outputs
Outputs of similar age
#38,583
of 277,393 outputs
Outputs of similar age from BMC Systems Biology
#1
of 9 outputs
Altmetric has tracked 18,792,779 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,127 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,393 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them