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

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 cleani...

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Autores principales: Rusilowicz, Martin, Dickinson, Michael, Charlton, Adrian, O’Keefe, Simon, Wilson, Julie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757603/
https://www.ncbi.nlm.nih.gov/pubmed/27069441
http://dx.doi.org/10.1007/s11306-016-0972-2
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author Rusilowicz, Martin
Dickinson, Michael
Charlton, Adrian
O’Keefe, Simon
Wilson, Julie
author_facet Rusilowicz, Martin
Dickinson, Michael
Charlton, Adrian
O’Keefe, Simon
Wilson, Julie
author_sort Rusilowicz, Martin
collection PubMed
description 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.
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spelling pubmed-47576032016-04-09 A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples Rusilowicz, Martin Dickinson, Michael Charlton, Adrian O’Keefe, Simon Wilson, Julie Metabolomics Original Article 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. Springer US 2016-02-18 2016 /pmc/articles/PMC4757603/ /pubmed/27069441 http://dx.doi.org/10.1007/s11306-016-0972-2 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Rusilowicz, Martin
Dickinson, Michael
Charlton, Adrian
O’Keefe, Simon
Wilson, Julie
A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
title A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
title_full A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
title_fullStr A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
title_full_unstemmed A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
title_short A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
title_sort batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757603/
https://www.ncbi.nlm.nih.gov/pubmed/27069441
http://dx.doi.org/10.1007/s11306-016-0972-2
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