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Splatter: simulation of single-cell RNA sequencing data

Overview of attention for article published in Genome Biology, September 2017
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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
3 blogs
twitter
87 X users
facebook
1 Facebook page

Citations

dimensions_citation
694 Dimensions

Readers on

mendeley
494 Mendeley
citeulike
2 CiteULike
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Title
Splatter: simulation of single-cell RNA sequencing data
Published in
Genome Biology, September 2017
DOI 10.1186/s13059-017-1305-0
Pubmed ID
Authors

Luke Zappia, Belinda Phipson, Alicia Oshlack

Abstract

As single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed, and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available. Here, we present the Splatter Bioconductor package for simple, reproducible, and well-documented simulation of scRNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types, or differentiation paths.

X Demographics

X Demographics

The data shown below were collected from the profiles of 87 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 494 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 493 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 139 28%
Researcher 89 18%
Student > Bachelor 44 9%
Student > Master 39 8%
Student > Doctoral Student 15 3%
Other 60 12%
Unknown 108 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 146 30%
Agricultural and Biological Sciences 72 15%
Computer Science 62 13%
Mathematics 26 5%
Neuroscience 12 2%
Other 57 12%
Unknown 119 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 66. 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 January 2024.
All research outputs
#660,810
of 25,918,104 outputs
Outputs from Genome Biology
#413
of 4,513 outputs
Outputs of similar age
#13,629
of 326,702 outputs
Outputs of similar age from Genome Biology
#6
of 57 outputs
Altmetric has tracked 25,918,104 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,513 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.7. This one has done particularly well, scoring higher than 90% 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 326,702 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 95% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.