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X Demographics
Mendeley readers
Attention Score in Context
| Title |
An iteration normalization and test method for differential expression analysis of RNA-seq data
|
|---|---|
| Published in |
BioData Mining, August 2014
|
| DOI | 10.1186/1756-0381-7-15 |
| Pubmed ID | |
| Authors |
Yan Zhou, Nan Lin, Baoxue Zhang |
| Abstract |
Next generation sequencing technologies are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key to analyzing massive and complex sequencing data. In order to derive gene expression measures and compare these measures across samples or libraries, we first need to normalize read counts to adjust for varying sample sequencing depths and other potentially technical effects. |
X Demographics
The data shown below were collected from the profiles of 39 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 States | 10 | 26% |
| United Kingdom | 5 | 13% |
| Australia | 3 | 8% |
| Germany | 2 | 5% |
| France | 2 | 5% |
| Myanmar | 1 | 3% |
| New Zealand | 1 | 3% |
| Austria | 1 | 3% |
| India | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 10 | 26% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 20 | 51% |
| Members of the public | 16 | 41% |
| Science communicators (journalists, bloggers, editors) | 2 | 5% |
| Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 7% |
| Spain | 1 | 2% |
| Czechia | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 40 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 11 | 24% |
| Student > Ph. D. Student | 10 | 22% |
| Researcher | 9 | 20% |
| Student > Bachelor | 4 | 9% |
| Student > Postgraduate | 3 | 7% |
| Other | 7 | 15% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 22 | 48% |
| Biochemistry, Genetics and Molecular Biology | 8 | 17% |
| Computer Science | 5 | 11% |
| Medicine and Dentistry | 3 | 7% |
| Neuroscience | 2 | 4% |
| Other | 4 | 9% |
| Unknown | 2 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 32. 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 15 January 2015.
All research outputs
#1,259,978
of 25,711,518 outputs
Outputs from BioData Mining
#13
of 325 outputs
Outputs of similar age
#12,249
of 243,801 outputs
Outputs of similar age from BioData Mining
#1
of 10 outputs
Altmetric has tracked 25,711,518 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 325 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has done particularly well, scoring higher than 96% 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 243,801 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them