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MetaboDiff: an R package for differential metabolomic analysis

Overview of attention for article published in Bioinformatics, April 2018
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
4 X users
patent
2 patents
reddit
1 Redditor
q&a
1 Q&A thread

Readers on

mendeley
119 Mendeley
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Article details
Title
MetaboDiff: an R package for differential metabolomic analysis
Published in
Bioinformatics, April 2018
DOI 10.1093/bioinformatics/bty344
Pubmed ID
Authors
Abstract

Comparative metabolomics comes of age through commercial vendors offering metabolomics for translational researchers outside the mass spectrometry field. The MetaboDiff packages aims to provide a low-level entry to differential metabolomic analysis with R by starting off with the table of metabolite measurements. As a key functionality, MetaboDiffs offers the exploration of sample traits in a data-derived metabolic correlation network. The MetaboDiff R package is platform-independent, available at http://github.com/andreasmock/MetaboDiff/ and released under the MIT licence. The package documentation comprises a step-by-step markdown tutorial. [email protected]. Supplementary data are available at Bioinformatics online.

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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 demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 119 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 27 23%
Researcher 26 22%
Student > Bachelor 10 8%
Student > Master 8 7%
Student > Postgraduate 7 6%
Other 17 14%
Unknown 24 20%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 24%
Agricultural and Biological Sciences 26 22%
Chemistry 8 7%
Medicine and Dentistry 6 5%
Immunology and Microbiology 4 3%
Other 15 13%
Unknown 31 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 01 September 2022.
All research outputs
#4,144,232
of 34,089,221 outputs
Outputs from Bioinformatics
#2,959
of 14,407 outputs
Outputs of similar age
#61,315
of 369,605 outputs
Outputs of similar age from Bioinformatics
#45
of 241 outputs
Altmetric has tracked 34,089,221 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,407 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has done well, scoring higher than 79% 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 369,605 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 241 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.