Machine Learning for Multi-Omics Integration

Combine complementary omics measurements to identify biological pathways and model cellular behaviour. Lectures and practicals compare supervised feature selection and integration using LASSO, PLS, LDA, mixOmics and DIABLO with unsupervised MOFA models and deep-learning autoencoders. The final day covers dimensionality reduction, graph-based integration, batch correction and multimodal single-cell analysis using Seurat and weighted nearest neighbours. Participants learn to select and implement an integration strategy suited to their research question.

Live online, 15–17 February, 14:00–19:00 Europe/Berlin (CET). Fee €450. Basic Unix awareness and beginner-level R or Python are expected.