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GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection

Overview of attention for article published in Genome Biology (Online Edition), 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 (85th percentile)

Mentioned by

twitter
31 tweeters

Citations

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25 Dimensions

Readers on

mendeley
47 Mendeley
citeulike
1 CiteULike
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Title
GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection
Published in
Genome Biology (Online Edition), May 2018
DOI 10.1186/s13059-018-1431-3
Pubmed ID
Authors

Daphne Tsoucas, Guo-Cheng Yuan

Abstract

Single-cell analysis is a powerful tool for dissecting the cellular composition within a tissue or organ. However, it remains difficult to detect rare and common cell types at the same time. Here, we present a new computational method, GiniClust2, to overcome this challenge. GiniClust2 combines the strengths of two complementary approaches, using the Gini index and Fano factor, respectively, through a cluster-aware, weighted ensemble clustering technique. GiniClust2 successfully identifies both common and rare cell types in diverse datasets, outperforming existing methods. GiniClust2 is scalable to large datasets.

Twitter Demographics

The data shown below were collected from the profiles of 31 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 19%
Researcher 9 19%
Student > Master 6 13%
Professor 3 6%
Student > Postgraduate 2 4%
Other 8 17%
Unknown 10 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 21%
Biochemistry, Genetics and Molecular Biology 7 15%
Computer Science 6 13%
Mathematics 4 9%
Neuroscience 3 6%
Other 7 15%
Unknown 10 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 24 May 2019.
All research outputs
#1,323,804
of 15,473,495 outputs
Outputs from Genome Biology (Online Edition)
#1,331
of 3,335 outputs
Outputs of similar age
#39,770
of 278,853 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 15,473,495 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,335 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.2. This one has gotten more attention than average, scoring higher than 60% 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 278,853 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% 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