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Mendeley readers
Attention Score in Context
| Title |
DCGL v2.0: An R Package for Unveiling Differential Regulation from Differential Co-expression
|
|---|---|
| Published in |
PLOS ONE, November 2013
|
| DOI | 10.1371/journal.pone.0079729 |
| Pubmed ID | |
| Authors |
Jing Yang, Hui Yu, Bao-Hong Liu, Zhongming Zhao, Lei Liu, Liang-Xiao Ma, Yi-Xue Li, Yuan-Yuan Li |
| Abstract |
Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner. |
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 States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| Spain | 1 | 1% |
| China | 1 | 1% |
| Unknown | 74 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 31% |
| Researcher | 17 | 22% |
| Student > Master | 7 | 9% |
| Student > Bachelor | 6 | 8% |
| Student > Doctoral Student | 4 | 5% |
| Other | 11 | 14% |
| Unknown | 8 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 28 | 36% |
| Biochemistry, Genetics and Molecular Biology | 15 | 19% |
| Medicine and Dentistry | 9 | 12% |
| Computer Science | 6 | 8% |
| Engineering | 2 | 3% |
| Other | 4 | 5% |
| Unknown | 13 | 17% |
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 21 November 2013.
All research outputs
#15,285,728
of 22,731,677 outputs
Outputs from PLOS ONE
#130,294
of 194,033 outputs
Outputs of similar age
#188,909
of 302,015 outputs
Outputs of similar age from PLOS ONE
#3,263
of 5,172 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 194,033 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. 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 302,015 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,172 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.