HyperMet Metabolomics Data Analysis Workshop

The nature of HyperMet research requires analyzing large, complex datasets. To provide HyperMet researchers with practical training in metabolomics data analysis using data from their own projects, the leaders of the S09-HyperGraph project, Dr. Gabi Kastenmüller and Dr. Dominik Lutter, organized the HyperMet Metabolomics Data Analysis Workshop. The workshop was further supported by Dr. Konstantinos Makris, Dr. Werner Römisch-Margl, and Gülsah Erdogan from S09-HyperGraph.

The program covered chemical compound annotation, omics data processing, statistical analysis, and practical data handling in R/RStudio. Participants also took part in a hands-on data analysis hackathon and presented project ideas and analysis results during interactive discussion sessions.

A central focus of the workshop was transforming raw metabolomics data into biologically meaningful results. Topics included quality control, filtering and imputation of missing values, data normalization and scaling, statistical testing, batch-effect correction, and approaches to biomarker discovery using machine learning. Throughout the workshop, participants were encouraged to critically evaluate each processing step and to document analytical decisions to ensure reproducibility and biological relevance of their results.

HyperMet research examines the impact of muscle growth (hypertrophy) and muscle loss (atrophy) on metabolism. Increased muscle mass reduces the risk of obesity, diabetes, osteoporosis, and potentially cancer. We are exploring the underlying metabolic processes to develop new strategies for prevention and everyday life.