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X Demographics
Mendeley readers
Attention Score in Context
| Title |
LFCseq: a nonparametric approach for differential expression analysis of RNA-seq data
|
|---|---|
| Published in |
BMC Genomics, December 2014
|
| DOI | 10.1186/1471-2164-15-s10-s7 |
| Pubmed ID | |
| Authors |
Bingqing Lin, Li-Feng Zhang, Xin Chen |
| Abstract |
With the advances in high-throughput DNA sequencing technologies, RNA-seq has rapidly emerged as a powerful tool for the quantitative analysis of gene expression and transcript variant discovery. In comparative experiments, differential expression analysis is commonly performed on RNA-seq data to identify genes/features that are differentially expressed between biological conditions. Most existing statistical methods for differential expression analysis are parametric and assume either Poisson distribution or negative binomial distribution on gene read counts. However, violation of distributional assumptions or a poor estimation of parameters often leads to unreliable results. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 40% |
| France | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 5% |
| Ireland | 1 | 3% |
| Italy | 1 | 3% |
| Brazil | 1 | 3% |
| New Zealand | 1 | 3% |
| Russia | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 32 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 28% |
| Student > Ph. D. Student | 9 | 23% |
| Student > Master | 6 | 15% |
| Student > Doctoral Student | 4 | 10% |
| Professor > Associate Professor | 4 | 10% |
| Other | 2 | 5% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 57% |
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Computer Science | 2 | 5% |
| Mathematics | 1 | 3% |
| Immunology and Microbiology | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 5 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 09 January 2015.
All research outputs
#3,050,900
of 22,776,824 outputs
Outputs from BMC Genomics
#1,120
of 10,643 outputs
Outputs of similar age
#45,133
of 356,570 outputs
Outputs of similar age from BMC Genomics
#27
of 234 outputs
Altmetric has tracked 22,776,824 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,643 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 89% 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 356,570 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 87% of its contemporaries.
We're also able to compare this research output to 234 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.