Educators:
Ilia Kats, Arber Qoku, Florin Walter (HD-HuB)
Date:
2026-05-18 - 2026-05-20
Location:
DKFZ Heidelberg (Im Neuenheimer Feld 370, basement, seminar room)
Contents:
The course will introduce participants to integrative analysis of multi-omics data with a focus on interpretable factor models. We will start with basic data handling, covering the data formats and common workflows for multi-omics data. After a general introduction to Bayesian factor models, participants will become familiar with MOFA, the de facto factor analysis method for multi-omics data to date, as well as its extension to spatial data, MEFISTO. We will then cover several possibilities to incorporate prior domain knowledge in the analysis. Each day is split into a theory and a practical part. In the theory part, the basic principles behind the methods will be discussed. During the practical participants will run analyses on small datasets. Time permitting, participants may also analyze their own data.The course targets scientists with prior experience in bioinformatics and single-cell data analysis and a working knowledge of Python.
Learning goals:
- Understand the principles of integrative multi-omics data analysis
- Apply interpretable factor models to multi-omics and single-cell datasets
Prerequisites:
- Experience in single-cell data analysis, including familiarity with cell × gene matrices, sparse matrices, PCA, UMAP, and basic statistical concepts (e.g. probability distributions).
- Basic proficiency in Python, including prior use of the scientific Python stack (NumPy, SciPy, Pandas).
Keywords:
Multi-omics, single-cell, MOFA, MEFISTO, MuVI
Tools:
MOFA-FLEX
Contact & Registration:
https://indico.dkfz.de/e/multi-comics