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SCANPY: large-scale single-cell gene expression data analysis

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

Mentioned by

news
14 news outlets
blogs
3 blogs
twitter
124 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
1125 Dimensions

Readers on

mendeley
905 Mendeley
citeulike
6 CiteULike
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Title
SCANPY: large-scale single-cell gene expression data analysis
Published in
Genome Biology (Online Edition), February 2018
DOI 10.1186/s13059-017-1382-0
Pubmed ID
Authors

F. Alexander Wolf, Philipp Angerer, Fabian J. Theis

Abstract

SCANPY is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with SCANPY, we present ANNDATA, a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).

Twitter Demographics

The data shown below were collected from the profiles of 124 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 905 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 905 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 256 28%
Researcher 176 19%
Student > Master 82 9%
Student > Bachelor 76 8%
Student > Doctoral Student 38 4%
Other 120 13%
Unknown 157 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 278 31%
Agricultural and Biological Sciences 160 18%
Computer Science 78 9%
Medicine and Dentistry 48 5%
Neuroscience 40 4%
Other 109 12%
Unknown 192 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 188. 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 November 2020.
All research outputs
#117,512
of 17,776,174 outputs
Outputs from Genome Biology (Online Edition)
#59
of 3,661 outputs
Outputs of similar age
#4,160
of 375,638 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 17,776,174 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,661 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one has done particularly well, scoring higher than 98% 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 375,638 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