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