Speaker
Description
Metabolomics data is rich and often large, containing far more variables than samples while many features remain unannotated. Therefore, the analysis of metabolomics data is not trivial and calls for approaches complementary to (multivariate) statisticial analyses not only to overcome the metabolite annotation bottleneck, but more importantly, to facilitate the rapid exploration of the chemical space in a (biological) sample. In this workshop, molecular networking (and thus graph theory) concepts will be introduced and real-world examples will be discussed using metabolomics data with special attention to the differences between mass-difference networking, classical, and feature-based molecular networking. During a hands-on session, available computational metabolomics tools for molecular substructure/motif discovery (MS2LDA/MASS2MOTIF) and data visualization (Cytoscape) will be collectively explored. The targeted audience includes experienced, novice and aspiring mass spectromery-based metabolomics users.
| Status Group | Doctoral Researcher |
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