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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Cell population-specific expression analysis of human cerebellum
|
|---|---|
| Published in |
BMC Genomics, November 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Peru | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 6% |
| Brazil | 1 | 1% |
| Germany | 1 | 1% |
| New Zealand | 1 | 1% |
| Israel | 1 | 1% |
| Unknown | 90 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 21% |
| Student > Ph. D. Student | 18 | 18% |
| Student > Doctoral Student | 14 | 14% |
| Student > Master | 11 | 11% |
| Student > Bachelor | 10 | 10% |
| Other | 15 | 15% |
| Unknown | 11 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 31 | 31% |
| Biochemistry, Genetics and Molecular Biology | 29 | 29% |
| Medicine and Dentistry | 7 | 7% |
| Computer Science | 5 | 5% |
| Neuroscience | 3 | 3% |
| Other | 10 | 10% |
| Unknown | 15 | 15% |
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
#7,960,052
of 25,374,647 outputs
Outputs from BMC Genomics
#3,479
of 11,244 outputs
Outputs of similar age
#58,361
of 193,286 outputs
Outputs of similar age from BMC Genomics
#66
of 213 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 67% 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 193,286 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 68% of its contemporaries.
We're also able to compare this research output to 213 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 65% of its contemporaries.