Multi-Omics Factor Analysis (MOFA)
Trainer: Ricard Argelaguet
Overview: This lecture illustrates MOFA as example for an unsupervised method for the integration of multi-omics data. Based on a probabilistic factor model, MOFA performs a joint dimension reduction of multiple omics data sets by identifying the major sources of variation in the data in terms of latent factors. It discusses the underlying probabilistic model and explore different downstream analyses that can help to interpret the results of the method. In particular, this would enable you to visualise and cluster the samples based on the information from all omics layers, annotate the inferred factors to molecular drivers, gene sets and omics layers and use the factors for data imputation or customised analyses. It illustrates the method on case studies and also discuss extensions to studies involving multiple sample groups and omics data with temporal and spatial resolution.
Learning outcomes
By the end of this session you will be able to:
- Describe MOFA as a tool for unsupervised data
- Familiarise yourself with different downstream analyses and interpretation of MOFA results
- Explain use cases & extensions to omics data with multiple sample groups or temporal and spatial resolution
Materials: