From Raw Spectra to Meaningful Insights: Lessons from Long-Term Metabolomics Modelling and Support
Metabolomics generates complex, high-dimensional datasets that demand careful modelling before meaningful interpretation is possible. At Umeå University, the Computational Analytics Support Platform (CASP) and the Swedish Metabolomics Centre (SMC) have supported hundreds of projects across the life sciences, building a unique perspective on recurring challenges and effective strategies in metabolomics data analysis.
What makes a metabolomics project succeed and what causes it to fail? This presentation distils our experiences into practical guidance across the full workflow: from study design and sample collection to data preprocessing, modelling and interpretation. Equally important, we emphasize how to help customers gain value from their data — understanding ongoing patterns, aligning results with prior expectations, and recognizing both the opportunities and the limitations imposed by their current design.
Rather than focusing on a single case study, we share broad lessons learned from many years of consulting and modelling. Our goal is to provide participants with strategies that extend beyond individual projects — highlighting recurring pitfalls, key success factors and offering actionable approaches to turn complex data into clear, meaningful biological insights.
What You Will Learn:
- How early decisions — study design, sample collection, and replication — shape downstream analysis, comparability, and overall project success.
- Practical strategies for preprocessing, scaling, and interpreting metabolomics data.
- Recurring challenges in metabolomics projects — and tested strategies to avoid or overcome them.
For more information, please visit:
Computational Analytics Support Platform (CASP)