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Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

Overview of attention for article published in BMC Genomics, June 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
2 news outlets
twitter
12 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
657 Dimensions

Readers on

mendeley
681 Mendeley
citeulike
1 CiteULike
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Title
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Published in
BMC Genomics, June 2018
DOI 10.1186/s12864-018-4772-0
Pubmed ID
Authors

Kelly Street, Davide Risso, Russell B. Fletcher, Diya Das, John Ngai, Nir Yosef, Elizabeth Purdom, Sandrine Dudoit

Abstract

Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 681 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 681 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 190 28%
Researcher 123 18%
Student > Master 76 11%
Student > Bachelor 75 11%
Student > Doctoral Student 36 5%
Other 59 9%
Unknown 122 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 212 31%
Agricultural and Biological Sciences 105 15%
Immunology and Microbiology 56 8%
Medicine and Dentistry 51 7%
Computer Science 35 5%
Other 88 13%
Unknown 134 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 15 December 2021.
All research outputs
#1,036,834
of 19,998,134 outputs
Outputs from BMC Genomics
#212
of 9,924 outputs
Outputs of similar age
#25,737
of 291,671 outputs
Outputs of similar age from BMC Genomics
#1
of 6 outputs
Altmetric has tracked 19,998,134 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,924 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 97% 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 291,671 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 91% of its contemporaries.
We're also able to compare this research output to 6 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