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ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs

Overview of attention for article published in BMC Bioinformatics, April 2014
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
1 blog
twitter
13 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
67 Mendeley
citeulike
2 CiteULike
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Title
ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs
Published in
BMC Bioinformatics, April 2014
DOI 10.1186/1471-2105-15-116
Pubmed ID
Authors

Mark A van de Wiel, Maarten Neerincx, Tineke E Buffart, Daoud Sie, Henk MW Verheul

Abstract

Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference. Moreover, when sample sizes are small, inference is likely to be too liberal when, in a Bayesian setting, applying a non-appropriate prior or to lack power when not carefully borrowing information across features.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Netherlands 2 3%
Sweden 1 1%
Australia 1 1%
United Kingdom 1 1%
Spain 1 1%
Finland 1 1%
Unknown 57 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 34%
Researcher 18 27%
Student > Master 4 6%
Student > Bachelor 4 6%
Professor 4 6%
Other 9 13%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 49%
Biochemistry, Genetics and Molecular Biology 8 12%
Mathematics 4 6%
Medicine and Dentistry 4 6%
Engineering 3 4%
Other 8 12%
Unknown 7 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 14 June 2014.
All research outputs
#1,388,119
of 15,708,475 outputs
Outputs from BMC Bioinformatics
#434
of 5,693 outputs
Outputs of similar age
#19,094
of 191,967 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 11 outputs
Altmetric has tracked 15,708,475 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,693 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 92% 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 191,967 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 90% of its contemporaries.
We're also able to compare this research output to 11 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 90% of its contemporaries.