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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Correcting for Sample Heterogeneity in Methylome-Wide Association Studies.
|
|---|---|
| Chapter number | 266 |
| Book title |
Population Epigenetics
|
| Published in |
Methods in molecular biology, August 2015
|
| DOI | 10.1007/7651_2015_266 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6901-2, 978-1-4939-6903-6
|
| Authors |
Zou, James Y, James Y. Zou, Zou, James Y. |
| Abstract |
Epigenome-wide association studies (EWAS) face many of the same challenges as genome-wide association studies (GWAS), but have an added challenge in that the epigenome can vary dramatically across cell types. When cell-type composition differs between cases and controls, this leads to spurious associations that may obscure true associations. We have developed a computational method, FaST-LMM-EWASher, which automatically corrects for cell-type composition without needing explicit knowledge of it. In this chapter, we provide a tutorial on using FaST-LMM-EWASher for DNA methylation data and discuss data analysis strategies. |
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 % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 31% |
| Student > Ph. D. Student | 2 | 15% |
| Unspecified | 1 | 8% |
| Student > Bachelor | 1 | 8% |
| Student > Master | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 4 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 15% |
| Agricultural and Biological Sciences | 2 | 15% |
| Computer Science | 2 | 15% |
| Nursing and Health Professions | 1 | 8% |
| Unspecified | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 4 | 31% |
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 08 August 2015.
All research outputs
#18,422,065
of 22,821,814 outputs
Outputs from Methods in molecular biology
#7,917
of 13,124 outputs
Outputs of similar age
#189,905
of 264,036 outputs
Outputs of similar age from Methods in molecular biology
#11
of 18 outputs
Altmetric has tracked 22,821,814 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,124 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% 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 264,036 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.