↓ Skip to main content

Statistical methods for identifying differentially expressed genes in RNA-Seq experiments

Overview of attention for article published in Cell & Bioscience, January 2012
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
225 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Statistical methods for identifying differentially expressed genes in RNA-Seq experiments
Published in
Cell & Bioscience, January 2012
DOI 10.1186/2045-3701-2-26
Pubmed ID
Authors

Zhide Fang, 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 225 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 4%
Germany 3 1%
Spain 3 1%
Argentina 2 <1%
Brazil 2 <1%
United Kingdom 1 <1%
Sweden 1 <1%
Canada 1 <1%
Mexico 1 <1%
Other 5 2%
Unknown 198 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 29%
Researcher 61 27%
Student > Master 27 12%
Student > Bachelor 17 8%
Student > Doctoral Student 12 5%
Other 31 14%
Unknown 11 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 138 61%
Biochemistry, Genetics and Molecular Biology 31 14%
Mathematics 8 4%
Computer Science 7 3%
Medicine and Dentistry 7 3%
Other 18 8%
Unknown 16 7%

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
#10,969,282
of 12,378,087 outputs
Outputs from Cell & Bioscience
#220
of 258 outputs
Outputs of similar age
#104,886
of 122,578 outputs
Outputs of similar age from Cell & Bioscience
#1
of 1 outputs
Altmetric has tracked 12,378,087 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 258 research outputs from this source. They receive a mean Attention Score of 2.2. 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 122,578 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them