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An integrative method to normalize RNA-Seq data

Overview of attention for article published in BMC Bioinformatics, June 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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
32 X users
patent
1 patent
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
136 Mendeley
citeulike
2 CiteULike
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Title
An integrative method to normalize RNA-Seq data
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-188
Pubmed ID
Authors

Cyril Filloux, Meersseman Cédric, Philippe Romain, Forestier Lionel, Klopp Christophe, Rocha Dominique, Maftah Abderrahman, Petit Daniel

Abstract

Transcriptome sequencing is a powerful tool for measuring gene expression, but as well as some other technologies, various artifacts and biases affect the quantification. In order to correct some of them, several normalization approaches have emerged, differing both in the statistical strategy employed and in the type of corrected biases. However, there is no clear standard normalization method.

X Demographics

X Demographics

The data shown below were collected from the profiles of 32 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 6%
Germany 2 1%
Switzerland 1 <1%
Netherlands 1 <1%
Norway 1 <1%
Portugal 1 <1%
Czechia 1 <1%
Finland 1 <1%
Belgium 1 <1%
Other 1 <1%
Unknown 118 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 33%
Researcher 33 24%
Student > Master 14 10%
Student > Bachelor 10 7%
Student > Doctoral Student 8 6%
Other 20 15%
Unknown 6 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 76 56%
Biochemistry, Genetics and Molecular Biology 25 18%
Computer Science 11 8%
Neuroscience 4 3%
Chemistry 3 2%
Other 9 7%
Unknown 8 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 April 2020.
All research outputs
#1,340,755
of 25,205,864 outputs
Outputs from BMC Bioinformatics
#157
of 7,660 outputs
Outputs of similar age
#12,960
of 234,796 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 155 outputs
Altmetric has tracked 25,205,864 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,660 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 97% 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 234,796 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 94% of its contemporaries.
We're also able to compare this research output to 155 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 96% of its contemporaries.