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Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system

Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those mo...

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Autores principales: Hattaway, Madison E., Black, Gabrielle P., Young, Thomas M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928919/
https://www.ncbi.nlm.nih.gov/pubmed/36627378
http://dx.doi.org/10.1007/s00216-023-04511-2
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author Hattaway, Madison E.
Black, Gabrielle P.
Young, Thomas M.
author_facet Hattaway, Madison E.
Black, Gabrielle P.
Young, Thomas M.
author_sort Hattaway, Madison E.
collection PubMed
description Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month’s samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00216-023-04511-2.
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spelling pubmed-99289192023-02-16 Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system Hattaway, Madison E. Black, Gabrielle P. Young, Thomas M. Anal Bioanal Chem Research Paper Nontarget chemical analysis using high-resolution mass spectrometry has increasingly been used to discern spatial patterns and temporal trends in anthropogenic chemical abundance in natural and engineered systems. A critical experimental design consideration in such applications, especially those monitoring complex matrices over long time periods, is a choice between analyzing samples in multiple batches as they are collected, or in one batch after all samples have been processed. While datasets acquired in multiple analytical batches can include the effects of instrumental variability over time, datasets acquired in a single batch risk compound degradation during sample storage. To assess the influence of batch effects on the analysis and interpretation of nontarget data, this study examined a set of 56 samples collected from a municipal wastewater system over 7 months. Each month’s samples included 6 from sites within the collection system, one combined influent, and one treated effluent sample. Samples were analyzed using liquid chromatography high-resolution mass spectrometry in positive electrospray ionization mode in multiple batches as the samples were collected and in a single batch at the conclusion of the study. Data were aligned and normalized using internal standard scaling and ComBat, an empirical Bayes method developed for estimating and removing batch effects in microarrays. As judged by multiple lines of evidence, including comparing principal variance component analysis between single and multi-batch datasets and through patterns in principal components and hierarchical clustering analyses, ComBat appeared to significantly reduce the influence of batch effects. For this reason, we recommend the use of more, small batches with an appropriate batch correction step rather than acquisition in one large batch. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00216-023-04511-2. Springer Berlin Heidelberg 2023-01-11 2023 /pmc/articles/PMC9928919/ /pubmed/36627378 http://dx.doi.org/10.1007/s00216-023-04511-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Paper
Hattaway, Madison E.
Black, Gabrielle P.
Young, Thomas M.
Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system
title Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system
title_full Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system
title_fullStr Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system
title_full_unstemmed Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system
title_short Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system
title_sort batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928919/
https://www.ncbi.nlm.nih.gov/pubmed/36627378
http://dx.doi.org/10.1007/s00216-023-04511-2
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