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BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process

Overview of attention for article published in Genome Biology, May 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 (90th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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1 blog
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28 X users

Citations

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24 Dimensions

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58 Mendeley
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Title
BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process
Published in
Genome Biology, May 2018
DOI 10.1186/s13059-018-1440-2
Pubmed ID
Authors

Alexis Boukouvalas, James Hensman, Magnus Rattray

Abstract

High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provide an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on simulated data, a single-cell RNA-seq haematopoiesis study and mouse embryonic stem cells generated using droplet barcoding. The method is robust to high levels of technical variation and dropout, which are common in single-cell data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 28 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 29%
Student > Ph. D. Student 12 21%
Student > Bachelor 5 9%
Student > Master 4 7%
Professor 3 5%
Other 10 17%
Unknown 7 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 31%
Agricultural and Biological Sciences 12 21%
Computer Science 10 17%
Mathematics 4 7%
Neuroscience 2 3%
Other 2 3%
Unknown 10 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 October 2018.
All research outputs
#1,571,498
of 25,382,440 outputs
Outputs from Genome Biology
#1,281
of 4,468 outputs
Outputs of similar age
#33,229
of 344,685 outputs
Outputs of similar age from Genome Biology
#14
of 36 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 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 gotten more attention than average, scoring higher than 71% 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 344,685 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 90% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.