↓ Skip to main content

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

Overview of attention for article published in Genome Biology, December 2019
Altmetric Badge

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 (88th percentile)

Mentioned by

blogs
1 blog
twitter
186 X users
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
359 Dimensions

Readers on

mendeley
524 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
Published in
Genome Biology, December 2019
DOI 10.1186/s13059-019-1861-6
Pubmed ID
Authors

F. William Townes, Stephanie C. Hicks, Martin J. Aryee, Rafael A. Irizarry

X Demographics

X Demographics

The data shown below were collected from the profiles of 186 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 524 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 524 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 138 26%
Researcher 84 16%
Student > Bachelor 55 10%
Student > Master 41 8%
Student > Doctoral Student 22 4%
Other 45 9%
Unknown 139 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 142 27%
Agricultural and Biological Sciences 81 15%
Computer Science 48 9%
Mathematics 20 4%
Engineering 15 3%
Other 60 11%
Unknown 158 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 112. 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 25 February 2024.
All research outputs
#378,988
of 25,552,933 outputs
Outputs from Genome Biology
#184
of 4,489 outputs
Outputs of similar age
#9,198
of 479,040 outputs
Outputs of similar age from Genome Biology
#12
of 92 outputs
Altmetric has tracked 25,552,933 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 4,489 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 95% 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 479,040 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 92 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.