Abstract
Direct infusion untargeted metabolomics, as mass-over-charge values and intensity of ions, 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 metabolomics data.
Competing Interest Statement
Jeroen de Ridder is co-founder of Cyclomics BV.
Footnotes
↵* Joint senior authors
Conflict of Interest Jeroen de Ridder is co-founder of Cyclomics BV.
Major revisions to figures and text. Extensive manual on the GitHub. Changed to move example dataset results to the supplementary materials (currently in submission as short communications manuscript).