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Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software

Overview of attention for article published in Cancer Informatics, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#30 of 475)
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
5 X users
patent
1 patent
facebook
1 Facebook page
q&a
1 Q&A thread

Readers on

mendeley
233 Mendeley
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Article details
Title
Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software
Published in
Cancer Informatics, December 2015
DOI 10.4137/cin.s21631
Pubmed ID
Authors
Abstract

Deep sequencing has recently emerged as a powerful alternative to microarrays for the high-throughput profiling of gene expression. In order to account for the discrete nature of RNA sequencing data, new statistical methods and computational tools have been developed for the analysis of differential expression to identify genes that are relevant to a disease such as cancer. In this paper, it is thus timely to provide an overview of these analysis methods and tools. For readers with statistical background, we also review the parameter estimation algorithms and hypothesis testing strategies used in these methods.

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Timeline Attention over time Attention Score history
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X Demographics

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.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 233 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 <1%
Argentina 2 <1%
Mexico 1 <1%
United Kingdom 1 <1%
France 1 <1%
Finland 1 <1%
Spain 1 <1%
Germany 1 <1%
Unknown 223 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 69 30%
Researcher 43 18%
Student > Bachelor 23 10%
Student > Master 20 9%
Student > Doctoral Student 13 6%
Other 27 12%
Unknown 38 16%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 75 32%
Biochemistry, Genetics and Molecular Biology 64 27%
Computer Science 11 5%
Mathematics 11 5%
Medicine and Dentistry 8 3%
Other 22 9%
Unknown 42 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 02 September 2026.
All research outputs
#6,002,706
of 34,372,222 outputs
Outputs from Cancer Informatics
#30
of 475 outputs
Outputs of similar age
#72,634
of 443,076 outputs
Outputs of similar age from Cancer Informatics
#3
of 14 outputs
Altmetric has tracked 34,372,222 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 475 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done particularly well, scoring higher than 93% 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 443,076 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 83% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.