Integrating Multi-Omics Data using MOFA2
24/09/2024 13h00-15h30
26/09/2024 14h00-16h30
General context
This training introduces participants to Multi-Omics Factor Analysis (MOFA2), a statistical framework for the unsupervised integration of multiple omics datasets (Argelaguet et al., 2020). Through this course, you will learn how to run MOFA2 on bulk and single-cell multi-omics data, interpret the factors driving variation across modalities, and apply the results to downstream analyses such as clustering, regression, and classification.
- Prepare and preprocess data for MOFA2 analysis.
- Interpret MOFA2 factors and assess their biological relevance.
- Apply MOFA2 results to downstream tasks such as clustering, regression, and classification.
- Explore options for handling missing values and comparing multiple model fits.
The course consists of an online demo and hands-on session, followed by an interactive Q&A session.
Familiarity with R and RStudio. If you have no experience with R you should follow the Basic statistics in R training first.
Trainers
Ricard Argelaguet
After his PhD in the groups of John Marioni (EMBL-EBI, now at Genentech) and Oliver Stegle (EMBL & DKFZ), followed by a short postdoctoral stay in the group of Wolf Reik at the Babraham Institute, Ricard developed methods for single-cell and multi-omics data integration, the most popular one being MOFA. Currently, he's working as a Senior Scientist at Altos Labs in the AI/ML team, where he works on modelling gene perturbation screenings to characterise the regulatory networks that underlie cellular rejuvenation programming.