| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 12, Iss 1, Pp 1-10 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-021-25210-5 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink