| Title |
Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data
|
|---|---|
| Published in |
BMC Bioinformatics, December 2013
|
| DOI | 10.1186/1471-2105-14-357 |
| Pubmed ID | |
| Authors |
Chung-I Li, Pei-Fang Su, Yu Shyr |
| Abstract |
Sample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size. |
X Demographics
The data shown below were collected from the profiles of 18 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 | 22% |
| France | 2 | 11% |
| Norway | 1 | 6% |
| Sweden | 1 | 6% |
| Belgium | 1 | 6% |
| India | 1 | 6% |
| Unknown | 8 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 56% |
| Members of the public | 8 | 44% |
Mendeley readers
The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 4% |
| Germany | 3 | 2% |
| United Kingdom | 2 | 1% |
| Denmark | 1 | <1% |
| Sweden | 1 | <1% |
| Unknown | 145 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 55 | 35% |
| Student > Ph. D. Student | 28 | 18% |
| Student > Master | 14 | 9% |
| Professor > Associate Professor | 11 | 7% |
| Student > Bachelor | 9 | 6% |
| Other | 30 | 19% |
| Unknown | 12 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 79 | 50% |
| Biochemistry, Genetics and Molecular Biology | 22 | 14% |
| Computer Science | 13 | 8% |
| Mathematics | 10 | 6% |
| Medicine and Dentistry | 8 | 5% |
| Other | 13 | 8% |
| Unknown | 14 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 20 September 2018.
All research outputs
#1,608,386
of 23,891,012 outputs
Outputs from BMC Bioinformatics
#306
of 7,455 outputs
Outputs of similar age
#18,716
of 312,690 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 108 outputs
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So far Altmetric has tracked 7,455 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 95% of its peers.
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We're also able to compare this research output to 108 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 91% of its contemporaries.