Abstract
Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample’s metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.
Original language | English |
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Article number | 99 |
Journal | Metabolomics |
Volume | 16 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1 Sept 2020 |
Keywords
- Annotation
- Direct infusion
- Machine learning
- Mass spectrometry
- Metabolomics
- R
- Statistics