↓ Skip to main content

NEAT: an efficient network enrichment analysis test

Overview of attention for article published in BMC Bioinformatics, September 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)

Mentioned by

twitter
14 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
73 Mendeley
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
NEAT: an efficient network enrichment analysis test
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1203-6
Pubmed ID
Authors

Mirko Signorelli, Veronica Vinciotti, Ernst C. Wit

Abstract

Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).

Twitter Demographics

The data shown below were collected from the profiles of 14 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 1%
Norway 1 1%
Brazil 1 1%
Unknown 70 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 26%
Student > Ph. D. Student 17 23%
Student > Master 13 18%
Student > Bachelor 10 14%
Professor 5 7%
Other 6 8%
Unknown 3 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 25 34%
Agricultural and Biological Sciences 23 32%
Computer Science 8 11%
Engineering 3 4%
Medicine and Dentistry 3 4%
Other 4 5%
Unknown 7 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 17 October 2020.
All research outputs
#3,002,145
of 19,157,212 outputs
Outputs from BMC Bioinformatics
#1,171
of 6,527 outputs
Outputs of similar age
#52,351
of 274,332 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 19,157,212 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,527 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 81% 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 274,332 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 80% of its contemporaries.
We're also able to compare this research output to 1 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