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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
  • Among the highest-scoring outputs from this source (#50 of 3,967)
  • High Attention Score compared to outputs of the same age (98th percentile)

Mentioned by

news
15 news outlets
blogs
5 blogs
twitter
122 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
2005 Dimensions

Readers on

mendeley
1276 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 122 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 1,276 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1276 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 340 27%
Researcher 224 18%
Student > Master 120 9%
Student > Bachelor 106 8%
Student > Doctoral Student 52 4%
Other 152 12%
Unknown 282 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 376 29%
Agricultural and Biological Sciences 183 14%
Computer Science 96 8%
Medicine and Dentistry 67 5%
Neuroscience 59 5%
Other 162 13%
Unknown 333 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 201. 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 27 May 2022.
All research outputs
#142,438
of 21,422,252 outputs
Outputs from Genome Biology (Online Edition)
#50
of 3,967 outputs
Outputs of similar age
#4,148
of 400,270 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 21,422,252 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 3,967 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 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 400,270 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