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Differential expression analysis for sequence count data

Overview of attention for article published in Genome Biology (Online Edition), October 2010
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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 (98th percentile)

Citations

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9542 Dimensions

Readers on

mendeley
7836 Mendeley
citeulike
83 CiteULike
connotea
2 Connotea
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Title
Differential expression analysis for sequence count data
Published in
Genome Biology (Online Edition), October 2010
DOI 10.1186/gb-2010-11-10-r106
Pubmed ID
Authors

Simon Anders, Wolfgang Huber

Abstract

High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.

Twitter Demographics

The data shown below were collected from the profiles of 16 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 7,836 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 190 2%
United Kingdom 56 <1%
Germany 53 <1%
France 28 <1%
Brazil 28 <1%
Italy 22 <1%
Spain 16 <1%
Canada 15 <1%
Netherlands 13 <1%
Other 145 2%
Unknown 7270 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2316 30%
Researcher 1763 22%
Student > Master 1003 13%
Student > Bachelor 536 7%
Student > Doctoral Student 428 5%
Other 1138 15%
Unknown 652 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 3793 48%
Biochemistry, Genetics and Molecular Biology 1633 21%
Computer Science 344 4%
Medicine and Dentistry 278 4%
Mathematics 172 2%
Other 776 10%
Unknown 840 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 86. 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 22 December 2020.
All research outputs
#292,604
of 17,399,150 outputs
Outputs from Genome Biology (Online Edition)
#222
of 3,594 outputs
Outputs of similar age
#1,150
of 101,510 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 17,399,150 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,594 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. This one has done particularly well, scoring higher than 93% 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 101,510 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 98% 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