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Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Overview of attention for article published in Genome Biology (Online Edition), January 2013
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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 (97th percentile)

Mentioned by

blogs
2 blogs
twitter
96 tweeters
facebook
1 Facebook page
wikipedia
5 Wikipedia pages
googleplus
1 Google+ user

Citations

dimensions_citation
524 Dimensions

Readers on

mendeley
2381 Mendeley
citeulike
39 CiteULike
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Title
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Published in
Genome Biology (Online Edition), January 2013
DOI 10.1186/gb-2013-14-9-r95
Pubmed ID
Authors

Franck Rapaport, Raya Khanin, Yupu Liang, Mono Pirun, Azra Krek, Paul Zumbo, Christopher E Mason, Nicholas D Socci, Doron Betel

Abstract

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

Twitter Demographics

The data shown below were collected from the profiles of 96 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 2,381 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 60 3%
United Kingdom 31 1%
Germany 14 <1%
Brazil 13 <1%
Spain 13 <1%
France 6 <1%
Norway 5 <1%
Australia 5 <1%
Mexico 5 <1%
Other 61 3%
Unknown 2168 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 681 29%
Researcher 578 24%
Student > Master 299 13%
Student > Bachelor 151 6%
Student > Doctoral Student 113 5%
Other 361 15%
Unknown 198 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 1218 51%
Biochemistry, Genetics and Molecular Biology 449 19%
Computer Science 151 6%
Medicine and Dentistry 84 4%
Mathematics 41 2%
Other 183 8%
Unknown 255 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 70. 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 29 November 2020.
All research outputs
#467,700
of 21,406,274 outputs
Outputs from Genome Biology (Online Edition)
#341
of 3,966 outputs
Outputs of similar age
#3,959
of 178,876 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 3 outputs
Altmetric has tracked 21,406,274 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,966 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done particularly well, scoring higher than 91% 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 178,876 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 97% of its contemporaries.
We're also able to compare this research output to 3 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