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Mendeley readers
Attention Score in Context
| Title |
Statistical methods for identifying differentially expressed genes in RNA-Seq experiments
|
|---|---|
| Published in |
Cell & Bioscience, July 2012
|
| DOI | 10.1186/2045-3701-2-26 |
| Pubmed ID | |
| Authors |
Zhide Fang, Jeffrey Martin, Zhong Wang |
| Abstract |
RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can be applied to RNA-Seq data with or without modifications. Recently several additional methods have been developed specifically for RNA-Seq data sets. This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses. |
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 % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 311 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 3% |
| Spain | 3 | <1% |
| Brazil | 2 | <1% |
| Germany | 2 | <1% |
| Argentina | 2 | <1% |
| United Kingdom | 1 | <1% |
| Canada | 1 | <1% |
| Mexico | 1 | <1% |
| Cuba | 1 | <1% |
| Other | 4 | 1% |
| Unknown | 286 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 74 | 24% |
| Researcher | 67 | 22% |
| Student > Master | 44 | 14% |
| Student > Bachelor | 34 | 11% |
| Student > Doctoral Student | 14 | 5% |
| Other | 33 | 11% |
| Unknown | 45 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 151 | 49% |
| Biochemistry, Genetics and Molecular Biology | 50 | 16% |
| Mathematics | 11 | 4% |
| Medicine and Dentistry | 10 | 3% |
| Computer Science | 8 | 3% |
| Other | 30 | 10% |
| Unknown | 51 | 16% |
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 31 July 2012.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from Cell & Bioscience
#956
of 1,177 outputs
Outputs of similar age
#161,223
of 178,869 outputs
Outputs of similar age from Cell & Bioscience
#7
of 7 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,177 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 1st percentile – i.e., 1% 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 178,869 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.