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CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data

Overview of attention for article published in BMC Genomics, January 2012
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  • Average Attention Score compared to outputs of the same age
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
1 tweeter
q&a
1 Q&A thread

Citations

dimensions_citation
83 Dimensions

Readers on

mendeley
103 Mendeley
citeulike
1 CiteULike
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Title
CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data
Published in
BMC Genomics, January 2012
DOI 10.1186/1471-2164-13-460
Pubmed ID
Authors

Jason E Shoemaker, Tiago JS Lopes, Samik Ghosh, Yukiko Matsuoka, Yoshihiro Kawaoka, Hiroaki Kitano

Abstract

Interpreting in vivo sampled microarray data is often complicated by changes in the cell population demographics. To put gene expression into its proper biological context, it is necessary to distinguish differential gene transcription from artificial gene expression induced by changes in the cellular demographics.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
France 1 <1%
Ireland 1 <1%
Israel 1 <1%
Denmark 1 <1%
United States 1 <1%
Unknown 97 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 22%
Researcher 23 22%
Student > Bachelor 10 10%
Student > Master 8 8%
Professor > Associate Professor 5 5%
Other 19 18%
Unknown 15 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 40%
Biochemistry, Genetics and Molecular Biology 13 13%
Computer Science 7 7%
Medicine and Dentistry 6 6%
Engineering 5 5%
Other 14 14%
Unknown 17 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 30 May 2017.
All research outputs
#6,742,709
of 20,751,917 outputs
Outputs from BMC Genomics
#3,378
of 10,132 outputs
Outputs of similar age
#48,565
of 147,165 outputs
Outputs of similar age from BMC Genomics
#1
of 8 outputs
Altmetric has tracked 20,751,917 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,132 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 59% 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 147,165 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 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