A hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant informat...

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Autores principales:	Taiyun Kim, Owen Tang, Stephen T. Vernon, Katharine A. Kott, Yen Chin Koay, John Park, David E. James, Stuart M. Grieve, Terence P. Speed, Pengyi Yang, Gemma A. Figtree, John F. O’Sullivan, Jean Yee Hwa Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b9f05e117dcb4f2a9c9c505e343dfa63
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id	oai:doaj.org-article:b9f05e117dcb4f2a9c9c505e343dfa63
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spelling	oai:doaj.org-article:b9f05e117dcb4f2a9c9c505e343dfa632021-12-02T16:45:48ZA hierarchical approach to removal of unwanted variation for large-scale metabolomics data10.1038/s41467-021-25210-52041-1723https://doaj.org/article/b9f05e117dcb4f2a9c9c505e343dfa632021-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25210-5https://doaj.org/toc/2041-1723Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.Taiyun KimOwen TangStephen T. VernonKatharine A. KottYen Chin KoayJohn ParkDavid E. JamesStuart M. GrieveTerence P. SpeedPengyi YangGemma A. FigtreeJohn F. O’SullivanJean Yee Hwa YangNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Taiyun Kim Owen Tang Stephen T. Vernon Katharine A. Kott Yen Chin Koay John Park David E. James Stuart M. Grieve Terence P. Speed Pengyi Yang Gemma A. Figtree John F. O’Sullivan Jean Yee Hwa Yang A hierarchical approach to removal of unwanted variation for large-scale metabolomics data
description	Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.
format	article
author	Taiyun Kim Owen Tang Stephen T. Vernon Katharine A. Kott Yen Chin Koay John Park David E. James Stuart M. Grieve Terence P. Speed Pengyi Yang Gemma A. Figtree John F. O’Sullivan Jean Yee Hwa Yang
author_facet	Taiyun Kim Owen Tang Stephen T. Vernon Katharine A. Kott Yen Chin Koay John Park David E. James Stuart M. Grieve Terence P. Speed Pengyi Yang Gemma A. Figtree John F. O’Sullivan Jean Yee Hwa Yang
author_sort	Taiyun Kim
title	A hierarchical approach to removal of unwanted variation for large-scale metabolomics data
title_short	A hierarchical approach to removal of unwanted variation for large-scale metabolomics data
title_full	A hierarchical approach to removal of unwanted variation for large-scale metabolomics data
title_fullStr	A hierarchical approach to removal of unwanted variation for large-scale metabolomics data
title_full_unstemmed	A hierarchical approach to removal of unwanted variation for large-scale metabolomics data
title_sort	hierarchical approach to removal of unwanted variation for large-scale metabolomics data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b9f05e117dcb4f2a9c9c505e343dfa63
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A hierarchical approach to removal of unwanted variation for large-scale metabolomics data

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