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Attention Score in Context
Chapter title |
Experimental Design and Power Calculation for RNA-seq Experiments
|
---|---|
Chapter number | 18 |
Book title |
Statistical Genomics
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3578-9_18 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3576-5, 978-1-4939-3578-9
|
Authors |
Zhijin Wu, Hao Wu, Wu, Zhijin, Wu, Hao |
Editors |
Ewy Mathé, Sean Davis |
Abstract |
Power calculation is a critical component of RNA-seq experimental design. The flexibility of RNA-seq experiment and the wide dynamic range of transcription it measures make it an attractive technology for whole transcriptome analysis. These features, in addition to the high dimensionality of RNA-seq data, bring complexity in experimental design, making an analytical power calculation no longer realistic. In this chapter we review the major factors that influence the statistical power of detecting differential expression, and give examples of power assessment using the R package PROPER. |
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 % |
---|---|---|
France | 2 | 22% |
Canada | 1 | 11% |
Australia | 1 | 11% |
United Kingdom | 1 | 11% |
Unknown | 4 | 44% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 5 | 56% |
Members of the public | 4 | 44% |
Mendeley readers
The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 1% |
Sweden | 1 | <1% |
Unknown | 148 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 43 | 28% |
Researcher | 33 | 22% |
Student > Master | 19 | 13% |
Student > Bachelor | 13 | 9% |
Other | 8 | 5% |
Other | 15 | 10% |
Unknown | 20 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 46 | 30% |
Biochemistry, Genetics and Molecular Biology | 39 | 26% |
Medicine and Dentistry | 7 | 5% |
Neuroscience | 6 | 4% |
Computer Science | 5 | 3% |
Other | 22 | 15% |
Unknown | 26 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 17. 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 25 July 2016.
All research outputs
#1,916,099
of 23,577,654 outputs
Outputs from Methods in molecular biology
#278
of 13,410 outputs
Outputs of similar age
#34,504
of 397,072 outputs
Outputs of similar age from Methods in molecular biology
#37
of 1,472 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,410 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 97% 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 397,072 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 91% of its contemporaries.
We're also able to compare this research output to 1,472 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 97% of its contemporaries.