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CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

Overview of attention for article published in BMC Bioinformatics, October 2020
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

blogs
1 blog
twitter
24 X users

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
39 Mendeley
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Title
CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures
Published in
BMC Bioinformatics, October 2020
DOI 10.1186/s12859-020-03796-9
Pubmed ID
Authors

Thomas D. Sherman, Tiger Gao, Elana J. Fertig

X Demographics

X Demographics

The data shown below were collected from the profiles of 24 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 31%
Researcher 4 10%
Student > Bachelor 3 8%
Student > Master 2 5%
Professor 1 3%
Other 2 5%
Unknown 15 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 23%
Computer Science 7 18%
Engineering 2 5%
Immunology and Microbiology 2 5%
Mathematics 1 3%
Other 3 8%
Unknown 15 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 11 November 2020.
All research outputs
#1,906,293
of 24,938,276 outputs
Outputs from BMC Bioinformatics
#407
of 7,613 outputs
Outputs of similar age
#49,481
of 422,638 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 165 outputs
Altmetric has tracked 24,938,276 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,613 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 94% 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 422,638 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 165 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 98% of its contemporaries.