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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging
|
|---|---|
| Published in |
Genome Biology, September 2014
|
| DOI | 10.1186/s13059-014-0465-4 |
| Pubmed ID | |
| Authors |
Belinda Phipson, Alicia Oshlack |
| Abstract |
Methylation of DNA is known to be essential to development and dramatically altered in cancers. The Illumina HumanMethylation450 BeadChip has been used extensively as a cost-effective way to profile nearly half a million CpG sites across the human genome. Here we present DiffVar, a novel method to test for differential variability between sample groups. DiffVar employs an empirical Bayes model framework that can take into account any experimental design and is robust to outliers. We applied DiffVar to several datasets from The Cancer Genome Atlas, as well as an aging dataset. DiffVar is available in the missMethyl Bioconductor R package. |
X Demographics
The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 10 | 67% |
| United States | 2 | 13% |
| United Kingdom | 1 | 7% |
| Unknown | 2 | 13% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 9 | 60% |
| Members of the public | 6 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 3 | 2% |
| United States | 1 | <1% |
| Turkey | 1 | <1% |
| Belgium | 1 | <1% |
| Unknown | 128 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 44 | 33% |
| Researcher | 42 | 31% |
| Student > Bachelor | 9 | 7% |
| Student > Postgraduate | 6 | 4% |
| Student > Master | 5 | 4% |
| Other | 15 | 11% |
| Unknown | 13 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 41 | 31% |
| Biochemistry, Genetics and Molecular Biology | 40 | 30% |
| Medicine and Dentistry | 10 | 7% |
| Computer Science | 9 | 7% |
| Mathematics | 3 | 2% |
| Other | 11 | 8% |
| Unknown | 20 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 October 2019.
All research outputs
#4,759,600
of 25,373,627 outputs
Outputs from Genome Biology
#2,774
of 4,467 outputs
Outputs of similar age
#48,487
of 263,029 outputs
Outputs of similar age from Genome Biology
#53
of 103 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 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 37th percentile – i.e., 37% 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 263,029 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 81% of its contemporaries.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.