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Gene-level differential analysis at transcript-level resolution

Overview of attention for article published in Genome Biology (Online Edition), April 2018
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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 (92nd percentile)

Mentioned by

blogs
1 blog
twitter
46 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
68 Dimensions

Readers on

mendeley
230 Mendeley
citeulike
2 CiteULike
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Title
Gene-level differential analysis at transcript-level resolution
Published in
Genome Biology (Online Edition), April 2018
DOI 10.1186/s13059-018-1419-z
Pubmed ID
Authors

Lynn Yi, Harold Pimentel, Nicolas L. Bray, Lior Pachter

Abstract

Compared to RNA-sequencing transcript differential analysis, gene-level differential expression analysis is more robust and experimentally actionable. However, the use of gene counts for statistical analysis can mask transcript-level dynamics. We demonstrate that 'analysis first, aggregation second,' where the p values derived from transcript analysis are aggregated to obtain gene-level results, increase sensitivity and accuracy. The method we propose can also be applied to transcript compatibility counts obtained from pseudoalignment of reads, which circumvents the need for quantification and is fast, accurate, and model-free. The method generalizes to various levels of biology and we showcase an application to gene ontologies.

Twitter Demographics

The data shown below were collected from the profiles of 46 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 230 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 230 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 59 26%
Student > Ph. D. Student 45 20%
Student > Master 23 10%
Student > Bachelor 20 9%
Student > Postgraduate 15 7%
Other 31 13%
Unknown 37 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 91 40%
Agricultural and Biological Sciences 55 24%
Computer Science 11 5%
Neuroscience 5 2%
Medicine and Dentistry 3 1%
Other 19 8%
Unknown 46 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 33. 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 09 December 2020.
All research outputs
#838,379
of 19,152,115 outputs
Outputs from Genome Biology (Online Edition)
#734
of 3,803 outputs
Outputs of similar age
#22,844
of 293,029 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 19,152,115 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 3,803 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has done well, scoring higher than 80% 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 293,029 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 92% 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