Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing

Abstract With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography–Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. Across different batches, we often...

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Autores principales:	Qin Liu, Douglas Walker, Karan Uppal, Zihe Liu, Chunyu Ma, ViLinh Tran, Shuzhao Li, Dean P. Jones, Tianwei Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b7e4a35c4a4445a4849e3d300e5b7365
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spelling	oai:doaj.org-article:b7e4a35c4a4445a4849e3d300e5b73652021-12-02T18:51:41ZAddressing the batch effect issue for LC/MS metabolomics data in data preprocessing10.1038/s41598-020-70850-02045-2322https://doaj.org/article/b7e4a35c4a4445a4849e3d300e5b73652020-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-70850-0https://doaj.org/toc/2045-2322Abstract With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography–Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group. Such practice can result in errors in the alignment of peaks, which cannot be corrected by post hoc application of batch effect correction methods. In this work, we developed a new approach that address the batch effect issue in the preprocessing stage, resulting in better peak detection, alignment and quantification. It can be combined with down-stream batch effect correction methods to further correct for between-batch intensity differences. The method is implemented in the existing workflow of the apLCMS platform. Analyzing data with multiple batches, both generated from standardized quality control (QC) plasma samples and from real biological studies, the new method resulted in feature tables with better consistency, as well as better down-stream analysis results. The method can be a useful addition to the tools available for large studies involving multiple batches. The method is available as part of the apLCMS package. Download link and instructions are at https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/ .Qin LiuDouglas WalkerKaran UppalZihe LiuChunyu MaViLinh TranShuzhao LiDean P. JonesTianwei YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qin Liu Douglas Walker Karan Uppal Zihe Liu Chunyu Ma ViLinh Tran Shuzhao Li Dean P. Jones Tianwei Yu Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing
description	Abstract With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography–Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group. Such practice can result in errors in the alignment of peaks, which cannot be corrected by post hoc application of batch effect correction methods. In this work, we developed a new approach that address the batch effect issue in the preprocessing stage, resulting in better peak detection, alignment and quantification. It can be combined with down-stream batch effect correction methods to further correct for between-batch intensity differences. The method is implemented in the existing workflow of the apLCMS platform. Analyzing data with multiple batches, both generated from standardized quality control (QC) plasma samples and from real biological studies, the new method resulted in feature tables with better consistency, as well as better down-stream analysis results. The method can be a useful addition to the tools available for large studies involving multiple batches. The method is available as part of the apLCMS package. Download link and instructions are at https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/ .
format	article
author	Qin Liu Douglas Walker Karan Uppal Zihe Liu Chunyu Ma ViLinh Tran Shuzhao Li Dean P. Jones Tianwei Yu
author_facet	Qin Liu Douglas Walker Karan Uppal Zihe Liu Chunyu Ma ViLinh Tran Shuzhao Li Dean P. Jones Tianwei Yu
author_sort	Qin Liu
title	Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing
title_short	Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing
title_full	Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing
title_fullStr	Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing
title_full_unstemmed	Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing
title_sort	addressing the batch effect issue for lc/ms metabolomics data in data preprocessing
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b7e4a35c4a4445a4849e3d300e5b7365
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Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing

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