Using MOFA for integration of omics data
07/12/2023 14h00-16h30
11/12/2023 14h00-16h30
General context
This course consists of an online demo/hands-on session and an online Q&A session.
This training provides an introduction to Multi-Omics Factor Analysis (MOFA2) (Argelaguet et al., 2020) for the integration of different omic data sets in an unsupervised fashion. It will enable you to run MOFA2 on multi-omic data, identify and explore the major drivers of variations across omics and use the inferred factors in various downstream analyses.
Participants can analyze their own data in the course. Both bulk and single cell applications will be discussed.
- What kind of preprocessing of the data is required for MOFA?
- How to train MOFA on a multi-omic data set?
- How to interpret the MOFA factors by their loadings, using gene set enrichment or sample ordination?
- How to use MOFA for downstream analyses including regression, classification or clustering?
- How to impute missing values with MOFA?
- How to select the number of factors and compare different MOFA fits?
Familiarity with R and RStudio. If you have no experience with R you should follow the Basic statistics in R training first. Contact Janick.Mathys@vib.be if you want to follow the R introduction training.
MOFA (Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets) - http://msb.embopress.org/content/14/6/e8124
Trainers
Ricard Argelaguet
Research Scientist, Altos Labs UK.