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The characteristic direction: a geometrical approach to identify differentially expressed genes

Overview of attention for article published in BMC Bioinformatics, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
1 blog
twitter
15 tweeters
googleplus
1 Google+ user
f1000
1 research highlight platform
video
1 video uploader

Citations

dimensions_citation
76 Dimensions

Readers on

mendeley
302 Mendeley
citeulike
6 CiteULike
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Title
The characteristic direction: a geometrical approach to identify differentially expressed genes
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-79
Pubmed ID
Authors

Neil R Clark, Kevin S Hu, Axel S Feldmann, Yan Kou, Edward Y Chen, Qiaonan Duan, Avi Ma’ayan

Abstract

Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.

Twitter Demographics

The data shown below were collected from the profiles of 15 tweeters 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 302 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 4%
France 3 <1%
United Kingdom 3 <1%
Netherlands 2 <1%
Germany 2 <1%
Russia 2 <1%
Portugal 2 <1%
Brazil 1 <1%
Australia 1 <1%
Other 9 3%
Unknown 264 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 84 28%
Researcher 81 27%
Student > Master 27 9%
Student > Bachelor 20 7%
Student > Doctoral Student 18 6%
Other 53 18%
Unknown 19 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 124 41%
Biochemistry, Genetics and Molecular Biology 49 16%
Medicine and Dentistry 30 10%
Computer Science 29 10%
Engineering 9 3%
Other 30 10%
Unknown 31 10%

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 29 March 2020.
All research outputs
#1,077,913
of 14,747,975 outputs
Outputs from BMC Bioinformatics
#301
of 5,480 outputs
Outputs of similar age
#16,214
of 189,669 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 11 outputs
Altmetric has tracked 14,747,975 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,480 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 94% 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 189,669 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 11 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 90% of its contemporaries.