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An integrative method to normalize RNA-Seq data

Overview of attention for article published in BMC Bioinformatics, January 2014
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About this Attention Score

  • In the top 5% 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 (92nd percentile)

Mentioned by

blogs
1 blog
twitter
34 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
132 Mendeley
citeulike
2 CiteULike
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Title
An integrative method to normalize RNA-Seq data
Published in
BMC Bioinformatics, January 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.

Twitter Demographics

The data shown below were collected from the profiles of 34 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 132 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 2%
Norway 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
Finland 1 <1%
Czechia 1 <1%
United Kingdom 1 <1%
Belgium 1 <1%
Other 1 <1%
Unknown 114 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 34%
Researcher 31 23%
Student > Master 14 11%
Other 9 7%
Student > Bachelor 9 7%
Other 19 14%
Unknown 5 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 75 57%
Biochemistry, Genetics and Molecular Biology 24 18%
Computer Science 9 7%
Neuroscience 4 3%
Chemistry 3 2%
Other 9 7%
Unknown 8 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 25 November 2014.
All research outputs
#723,861
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#119
of 5,420 outputs
Outputs of similar age
#10,988
of 190,160 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 13 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. 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 190,160 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 13 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 92% of its contemporaries.