↓ Skip to main content

WGCNA: an R package for weighted correlation network analysis

Overview of attention for article published in BMC Bioinformatics, December 2008
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#39 of 6,044)
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Citations

dimensions_citation
6677 Dimensions

Readers on

mendeley
5082 Mendeley
citeulike
37 CiteULike
connotea
2 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
WGCNA: an R package for weighted correlation network analysis
Published in
BMC Bioinformatics, December 2008
DOI 10.1186/1471-2105-9-559
Pubmed ID
Authors

Peter Langfelder, Steve Horvath

Abstract

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 5,082 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 95 2%
United Kingdom 20 <1%
Germany 14 <1%
Brazil 14 <1%
France 11 <1%
Netherlands 10 <1%
Italy 9 <1%
Spain 8 <1%
Japan 7 <1%
Other 67 1%
Unknown 4827 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1412 28%
Researcher 1094 22%
Student > Master 586 12%
Student > Bachelor 366 7%
Student > Doctoral Student 292 6%
Other 756 15%
Unknown 576 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 1895 37%
Biochemistry, Genetics and Molecular Biology 1081 21%
Medicine and Dentistry 294 6%
Computer Science 234 5%
Neuroscience 199 4%
Other 602 12%
Unknown 777 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 44. 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 30 December 2020.
All research outputs
#562,105
of 16,956,273 outputs
Outputs from BMC Bioinformatics
#39
of 6,044 outputs
Outputs of similar age
#537,787
of 15,816,716 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 6,045 outputs
Altmetric has tracked 16,956,273 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,044 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 99% 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 15,816,716 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 6,045 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.