Topic: MOFA

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

Practical training in integrating multiple molecular datasets, aimed at biologists and beginning computational researchers. The programme covers curation, identifier mapping, quality control, visualisation and data- or knowledge-driven integration using Cytoscape, MOFA, COSMOS and OmniPath. Small-group projects apply the tools to a defined biological question and culminate in presentations. The course does not cover systems-biology modelling or machine learning. Basic Unix, Python and R familiarity is needed for some practicals. Teaching runs over five days in GMT, beginning with introductions