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A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples

Overview of attention for article published in Metabolomics, February 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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12 X users

Citations

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51 Dimensions

Readers on

mendeley
123 Mendeley
Title
A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
Published in
Metabolomics, February 2016
DOI 10.1007/s11306-016-0972-2
Pubmed ID
Authors

Martin Rusilowicz, Michael Dickinson, Adrian Charlton, Simon O’Keefe, Julie Wilson

Abstract

The need for reproducible and comparable results is of increasing importance in non-targeted metabolomic studies, especially when differences between experimental groups are small. Liquid chromatography-mass spectrometry spectra are often acquired batch-wise so that necessary calibrations and cleaning of the instrument can take place. However this may introduce further sources of variation, such as differences in the conditions under which the acquisition of individual batches is performed. Quality control (QC) samples are frequently employed as a means of both judging and correcting this variation. Here we show that the use of QC samples can lead to problems. The non-linearity of the response can result in substantial differences between the recorded intensities of the QCs and experimental samples, making the required adjustment difficult to predict. Furthermore, changes in the response profile between one QC interspersion and the next cannot be accounted for and QC based correction can actually exacerbate the problems by introducing artificial differences. "Background correction" methods utilise all experimental samples to estimate the variation over time rather than relying on the QC samples alone. We compare non-QC correction methods with standard QC correction and demonstrate their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 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 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Czechia 1 <1%
Brazil 1 <1%
Unknown 119 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 30%
Researcher 23 19%
Student > Master 15 12%
Student > Bachelor 14 11%
Professor > Associate Professor 7 6%
Other 11 9%
Unknown 16 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 20%
Chemistry 21 17%
Biochemistry, Genetics and Molecular Biology 20 16%
Medicine and Dentistry 11 9%
Pharmacology, Toxicology and Pharmaceutical Science 7 6%
Other 21 17%
Unknown 19 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 April 2021.
All research outputs
#5,246,809
of 24,703,227 outputs
Outputs from Metabolomics
#281
of 1,357 outputs
Outputs of similar age
#74,701
of 303,312 outputs
Outputs of similar age from Metabolomics
#11
of 60 outputs
Altmetric has tracked 24,703,227 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,357 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 done well, scoring higher than 78% 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 303,312 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 74% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.