↓ Skip to main content

EntropyExplorer: an R package for computing and comparing differential Shannon entropy, differential coefficient of variation and differential expression

Overview of attention for article published in BMC Research Notes, December 2015
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
51 Mendeley
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
EntropyExplorer: an R package for computing and comparing differential Shannon entropy, differential coefficient of variation and differential expression
Published in
BMC Research Notes, December 2015
DOI 10.1186/s13104-015-1786-4
Pubmed ID
Authors

Kai Wang, Charles A. Phillips, Arnold M. Saxton, Michael A. Langston

Abstract

Differential Shannon entropy (DSE) and differential coefficient of variation (DCV) are effective metrics for the study of gene expression data. They can serve to augment differential expression (DE), and be applied in numerous settings whenever one seeks to measure differences in variability rather than mere differences in magnitude. A general purpose, easily accessible tool for DSE and DCV would help make these two metrics available to data scientists. Automated p value computations would additionally be useful, and are often easier to interpret than raw test statistic values alone. EntropyExplorer is an R package for calculating DSE, DCV and DE. It also computes corresponding p values for each metric. All features are available through a single R function call. Based on extensive investigations in the literature, the Fligner-Killeen test was chosen to compute DCV p values. No standard method was found to be appropriate for DSE, and so permutation testing is used to calculate DSE p values. EntropyExplorer provides a convenient resource for calculating DSE, DCV, DE and associated p values. The package, along with its source code and reference manual, are freely available from the CRAN public repository at http://cran.r-project.org/web/packages/EntropyExplorer/index.html .

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 20%
Student > Bachelor 8 16%
Student > Ph. D. Student 7 14%
Student > Postgraduate 4 8%
Professor > Associate Professor 4 8%
Other 10 20%
Unknown 8 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 24%
Biochemistry, Genetics and Molecular Biology 7 14%
Computer Science 5 10%
Medicine and Dentistry 4 8%
Mathematics 3 6%
Other 8 16%
Unknown 12 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 18 November 2022.
All research outputs
#13,568,637
of 23,138,859 outputs
Outputs from BMC Research Notes
#1,684
of 4,292 outputs
Outputs of similar age
#189,712
of 394,699 outputs
Outputs of similar age from BMC Research Notes
#58
of 148 outputs
Altmetric has tracked 23,138,859 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,292 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has gotten more attention than average, scoring higher than 60% 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 394,699 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 148 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.