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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Correcting for cell-type composition bias in epigenome-wide association studies
|
|---|---|
| Published in |
Genome Medicine, March 2014
|
| DOI | 10.1186/gm540 |
| Pubmed ID | |
| Authors |
Robert Lowe, Vardhman K Rakyan |
| Abstract |
Recent epigenome-wide association studies have indicated a potential role for epigenetic variation in the etiology of complex human diseases. However, one major challenge is to distinguish true epigenetic variation from changes caused by differences in cellular composition between the disease and non-disease state, a problem that is particularly relevant when analyzing whole blood. For studies with large numbers of samples, it can be expensive and very time consuming to perform cell sorting, and it is often not clear which is the correct cell type to profile. Two recently published papers have attempted to address this confounding issue using bioinformatics. |
X Demographics
The data shown below were collected from the profiles of 10 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 Kingdom | 4 | 40% |
| United States | 2 | 20% |
| France | 1 | 10% |
| Australia | 1 | 10% |
| Unknown | 2 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 6 | 60% |
| Members of the public | 3 | 30% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 1 | 2% |
| Unknown | 43 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 27% |
| Researcher | 9 | 20% |
| Student > Bachelor | 4 | 9% |
| Student > Master | 4 | 9% |
| Professor > Associate Professor | 3 | 7% |
| Other | 6 | 14% |
| Unknown | 6 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 48% |
| Biochemistry, Genetics and Molecular Biology | 11 | 25% |
| Computer Science | 2 | 5% |
| Nursing and Health Professions | 1 | 2% |
| Physics and Astronomy | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 7 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 12 January 2023.
All research outputs
#5,210,739
of 25,373,627 outputs
Outputs from Genome Medicine
#962
of 1,585 outputs
Outputs of similar age
#48,151
of 237,681 outputs
Outputs of similar age from Genome Medicine
#14
of 21 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one is in the 38th percentile – i.e., 38% 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 237,681 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 79% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.