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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information
|
|---|---|
| Published in |
BMC Genomics, January 2013
|
| DOI | 10.1186/1471-2164-14-s1-s9 |
| Pubmed ID | |
| Authors |
Ellis Patrick, Michael Buckley, David Ming Lin, Yee Hwa Yang |
| Abstract |
The cost of RNA-Seq has been decreasing over the last few years. Despite this, experiments with four or less biological replicates are still quite common. Estimating the variances of gene expression estimates becomes both a challenging and interesting problem in these situations of low replication. However, with the wealth of microarray and other publicly available gene expression data readily accessible on public repositories, these sources of information can be leveraged to make improvements in variance estimation. |
X Demographics
The data shown below were collected from the profiles of 9 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 | 4 | 44% |
| Canada | 1 | 11% |
| Australia | 1 | 11% |
| Netherlands | 1 | 11% |
| France | 1 | 11% |
| Unknown | 1 | 11% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 6 | 67% |
| Members of the public | 3 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 8% |
| Unknown | 23 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 28% |
| Student > Ph. D. Student | 6 | 24% |
| Student > Bachelor | 4 | 16% |
| Student > Postgraduate | 2 | 8% |
| Student > Master | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 2 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 52% |
| Computer Science | 3 | 12% |
| Mathematics | 2 | 8% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Medicine and Dentistry | 2 | 8% |
| Other | 1 | 4% |
| Unknown | 2 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 13 June 2017.
All research outputs
#2,661,126
of 24,552,012 outputs
Outputs from BMC Genomics
#806
of 11,010 outputs
Outputs of similar age
#27,145
of 288,427 outputs
Outputs of similar age from BMC Genomics
#37
of 365 outputs
Altmetric has tracked 24,552,012 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,010 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 92% 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 288,427 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 90% of its contemporaries.
We're also able to compare this research output to 365 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.