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

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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Detalles Bibliográficos
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
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Science
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Acceso en línea:https://doaj.org/article/b9f05e117dcb4f2a9c9c505e343dfa63
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Sumario: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.

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