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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Bibliographic Details
Main Authors: 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
Format: article
Language:EN
Published: Nature Portfolio 2021
Subjects:
Science
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Online Access:https://doaj.org/article/b9f05e117dcb4f2a9c9c505e343dfa63
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