↓ Skip to main content

Cell population-specific expression analysis of human cerebellum

Overview of attention for article published in BMC Genomics, January 2012
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

twitter
1 tweeter
wikipedia
1 Wikipedia page

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
88 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Cell population-specific expression analysis of human cerebellum
Published in
BMC Genomics, January 2012
DOI 10.1186/1471-2164-13-610
Pubmed ID
Authors

Alexandre Kuhn, Azad Kumar, Alexandra Beilina, Allissa Dillman, Mark R Cookson, Andrew B Singleton

Abstract

Interpreting gene expression profiles obtained from heterogeneous samples can be difficult because bulk gene expression measures are not resolved to individual cell populations. We have recently devised Population-Specific Expression Analysis (PSEA), a statistical method that identifies individual cell types expressing genes of interest and achieves quantitative estimates of cell type-specific expression levels. This procedure makes use of marker gene expression and circumvents the need for additional experimental information like tissue composition.

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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 7%
Brazil 1 1%
Germany 1 1%
New Zealand 1 1%
Israel 1 1%
Unknown 78 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 24%
Student > Ph. D. Student 17 19%
Student > Doctoral Student 11 13%
Student > Master 10 11%
Student > Bachelor 7 8%
Other 14 16%
Unknown 8 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 35%
Biochemistry, Genetics and Molecular Biology 25 28%
Medicine and Dentistry 7 8%
Computer Science 5 6%
Business, Management and Accounting 2 2%
Other 7 8%
Unknown 11 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 13 March 2021.
All research outputs
#6,204,764
of 20,477,298 outputs
Outputs from BMC Genomics
#2,989
of 10,054 outputs
Outputs of similar age
#49,795
of 172,135 outputs
Outputs of similar age from BMC Genomics
#123
of 419 outputs
Altmetric has tracked 20,477,298 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 10,054 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 68% 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 172,135 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 419 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 69% of its contemporaries.