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Mendeley readers
Attention Score in Context
Title |
GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection
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Published in |
Genome Biology, May 2018
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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. |
X Demographics
The data shown below were collected from the profiles of 30 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 20 | 67% |
Singapore | 1 | 3% |
Austria | 1 | 3% |
France | 1 | 3% |
Japan | 1 | 3% |
United Kingdom | 1 | 3% |
Unknown | 5 | 17% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 21 | 70% |
Members of the public | 8 | 27% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 76 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 19 | 25% |
Student > Ph. D. Student | 13 | 17% |
Student > Master | 8 | 11% |
Professor | 4 | 5% |
Other | 4 | 5% |
Other | 10 | 13% |
Unknown | 18 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 15 | 20% |
Biochemistry, Genetics and Molecular Biology | 12 | 16% |
Computer Science | 9 | 12% |
Mathematics | 4 | 5% |
Neuroscience | 3 | 4% |
Other | 12 | 16% |
Unknown | 21 | 28% |
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
#2,369,335
of 25,382,440 outputs
Outputs from Genome Biology
#1,929
of 4,468 outputs
Outputs of similar age
#47,736
of 339,704 outputs
Outputs of similar age from Genome Biology
#19
of 38 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 90th 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 56% 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 339,704 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 38 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 50% of its contemporaries.