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GCalignR: An R package for aligning gas-chromatography data for ecological and evolutionary studies

Overview of attention for article published in PLOS ONE, June 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 (84th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
22 tweeters

Citations

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31 Dimensions

Readers on

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70 Mendeley
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Title
GCalignR: An R package for aligning gas-chromatography data for ecological and evolutionary studies
Published in
PLOS ONE, June 2018
DOI 10.1371/journal.pone.0198311
Pubmed ID
Authors

Meinolf Ottensmann, Martin A. Stoffel, Hazel J. Nichols, Joseph I. Hoffman

Abstract

Chemical cues are arguably the most fundamental means of animal communication and play an important role in mate choice and kin recognition. Consequently, there is growing interest in the use of gas chromatography (GC) to investigate the chemical basis of eco-evolutionary interactions. Both GC-MS (mass spectrometry) and FID (flame ionization detection) are commonly used to characterise the chemical composition of biological samples such as skin swabs. The resulting chromatograms comprise peaks that are separated according to their retention times and which represent different substances. Across chromatograms of different samples, homologous substances are expected to elute at similar retention times. However, random and often unavoidable experimental variation introduces noise, making the alignment of homologous peaks challenging, particularly with GC-FID data where mass spectral data are lacking. Here we present GCalignR, a user-friendly R package for aligning GC-FID data based on retention times. The package was developed specifically for ecological and evolutionary studies that seek to investigate similarity patterns across multiple and often highly variable biological samples, for example representing different sexes, age classes or reproductive stages. The package also implements dynamic visualisations to facilitate inspection and fine-tuning of the resulting alignments and can be integrated within a broader workflow in R to facilitate downstream multivariate analyses. We demonstrate an example workflow using empirical data from Antarctic fur seals and explore the impact of user-defined parameter values by calculating alignment error rates for multiple datasets. The resulting alignments had low error rates for most of the explored parameter space and we could also show that GCalignR performed equally well or better than other available software. We hope that GCalignR will help to simplify the processing of chemical datasets and improve the standardization and reproducibility of chemical analyses in studies of animal chemical communication and related fields.

Twitter Demographics

The data shown below were collected from the profiles of 22 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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 26%
Student > Master 13 19%
Researcher 8 11%
Student > Bachelor 7 10%
Student > Postgraduate 3 4%
Other 6 9%
Unknown 15 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 30%
Chemistry 13 19%
Environmental Science 4 6%
Biochemistry, Genetics and Molecular Biology 3 4%
Chemical Engineering 3 4%
Other 10 14%
Unknown 16 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 08 July 2019.
All research outputs
#2,304,538
of 22,736,112 outputs
Outputs from PLOS ONE
#29,315
of 194,041 outputs
Outputs of similar age
#51,022
of 328,303 outputs
Outputs of similar age from PLOS ONE
#580
of 3,248 outputs
Altmetric has tracked 22,736,112 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 194,041 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has done well, scoring higher than 84% 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 328,303 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 84% of its contemporaries.
We're also able to compare this research output to 3,248 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.