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MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data

Overview of attention for article published in BMC Bioinformatics, July 2014
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Mentioned by

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2 tweeters
facebook
1 Facebook page

Citations

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11 Dimensions

Readers on

mendeley
61 Mendeley
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1 CiteULike
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Title
MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
Published in
BMC Bioinformatics, July 2014
DOI 10.1186/1471-2105-15-240
Pubmed ID
Authors

Justin Feigelman, Fabian J Theis, Carsten Marr

Abstract

Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of distinct subpopulations may be difficult if biological measurements yield distributions that are not easily separable.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
United States 1 2%
Sweden 1 2%
Brazil 1 2%
Unknown 57 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 31%
Student > Ph. D. Student 15 25%
Professor > Associate Professor 8 13%
Student > Bachelor 5 8%
Other 4 7%
Other 7 11%
Unknown 3 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 41%
Biochemistry, Genetics and Molecular Biology 11 18%
Computer Science 7 11%
Engineering 3 5%
Immunology and Microbiology 2 3%
Other 10 16%
Unknown 3 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 11 July 2014.
All research outputs
#17,723,043
of 22,758,248 outputs
Outputs from BMC Bioinformatics
#5,927
of 7,272 outputs
Outputs of similar age
#153,631
of 226,417 outputs
Outputs of similar age from BMC Bioinformatics
#101
of 140 outputs
Altmetric has tracked 22,758,248 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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 226,417 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.