Integrating multi-omics data using MOFA2
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. The trainer of this session has experience using MOFA2 to integrate datasets from different organisms.
Learning outcomes
- 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.
Approach
The course consists of an online demo and hands-on session, followed by an interactive Q&A session.
Required skills
Familiarity with Python.
Those with no Python experience whatsoever are recommended to complete the online Python introduction and Python essentials for data analysis courses.
Those with basic experience in Python who have never worked with Python environments, numpy, pandas, matplotlib, anndata or scanpy should follow the online Python essentials for data analysis course.
Trainer
Ricardo Omar Ramirez Flores
European Bioinformatics Institute, EMBL-EBI, UK
Ricardo Ramirez is a Staff Scientist at EMBL-EBI within the Saez-Rodriguez group, where he leads a research line that develops computational methods to study how tissues change in inflammatory and fibrotic diseases using distinct types of molecular data, including bulk, single-cell, and spatial omics. His research focuses on connecting molecular data from patient samples with clinically relevant questions, particularly with long-term prospective biobanks. He has a background in genomics and applied statistics and obtained his PhD in Biosciences from Heidelberg University