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Seminar plus Workshop on Multi-Omics Data Integration

Seminar plus workshop held by Ricard Argelaguet, (European Bioinformatics Institute EMBL-EBI, UK) in the MSI's premises on December 10.

10/12/2019   :   14h00
MSI (Salle des séminaires) 1361 Route des Lucioles, 06560 Valbonne, France
 Speaker: Ricard Argelaguet, European Bioinformatics Institute (EMBL-EBI), UK
Publication : 10/12/2019
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Multi-Omics Factor Analysis (MOFA): a statistical framework for unsupervised integration of multi-omics data sets

Speaker: Ricard Argelaguet, European Bioinformatics Institute (EMBL-EBI), UK

Abstract: Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking.

I will present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. 

We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. Taken together, MOFA captured the key drivers of inter-patient variability, thereby enhancing data interpretation and facilitating the definition of predictive models for clinical outcomes.


The 30 minute-seminar is going to be followed by a 2 hour-workshop to learn how to use Multi-Omics Factor Analysis (MOFA). Please note that participants are required to bring and use their own laptop.


  • R>=3.4
  • Rstudio
  • Python>=3
  • MOFA R package (+ dependencies)
  • MOFA data R package (+ dependencies)
  • Mofapy python package (+ dependencies)