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voom: precision weights unlock linear model analysis tools for RNA-seq read counts

Overview of attention for article published in Genome Biology (Online Edition), 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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Citations

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2132 Dimensions

Readers on

mendeley
2149 Mendeley
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17 CiteULike
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Title
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Published in
Genome Biology (Online Edition), January 2014
DOI 10.1186/gb-2014-15-2-r29
Pubmed ID
Authors

Charity W Law, Yunshun Chen, Wei Shi, Gordon K Smyth

Abstract

Normal linear modeling methods are developed for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation, and then enters these into a limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

Twitter Demographics

The data shown below were collected from the profiles of 57 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 2,149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 40 2%
United Kingdom 12 <1%
Germany 8 <1%
Australia 7 <1%
Netherlands 6 <1%
Spain 5 <1%
China 4 <1%
Brazil 4 <1%
New Zealand 3 <1%
Other 24 1%
Unknown 2036 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 623 29%
Researcher 533 25%
Student > Master 249 12%
Student > Bachelor 151 7%
Student > Doctoral Student 106 5%
Other 302 14%
Unknown 185 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 873 41%
Biochemistry, Genetics and Molecular Biology 515 24%
Medicine and Dentistry 120 6%
Computer Science 115 5%
Mathematics 63 3%
Other 218 10%
Unknown 245 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 69. 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 19 December 2019.
All research outputs
#321,930
of 15,766,637 outputs
Outputs from Genome Biology (Online Edition)
#272
of 3,384 outputs
Outputs of similar age
#4,903
of 257,161 outputs
Outputs of similar age from Genome Biology (Online Edition)
#13
of 139 outputs
Altmetric has tracked 15,766,637 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 3,384 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.5. 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 257,161 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 139 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.