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Splatter: simulation of single-cell RNA sequencing data

Overview of attention for article published in Genome Biology (Online Edition), 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)

Mentioned by

blogs
3 blogs
twitter
93 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
197 Dimensions

Readers on

mendeley
324 Mendeley
citeulike
2 CiteULike
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Title
Splatter: simulation of single-cell RNA sequencing data
Published in
Genome Biology (Online Edition), 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.

Twitter Demographics

The data shown below were collected from the profiles of 93 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 324 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 323 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 101 31%
Researcher 68 21%
Student > Bachelor 30 9%
Student > Master 28 9%
Other 12 4%
Other 38 12%
Unknown 47 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 100 31%
Agricultural and Biological Sciences 63 19%
Computer Science 45 14%
Mathematics 16 5%
Engineering 9 3%
Other 35 11%
Unknown 56 17%

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 16 March 2020.
All research outputs
#343,087
of 15,922,017 outputs
Outputs from Genome Biology (Online Edition)
#294
of 3,414 outputs
Outputs of similar age
#11,414
of 275,096 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 15,922,017 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 3,414 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.7. This one has done particularly well, scoring higher than 91% 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 275,096 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 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