Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Our workflow makes it possible to easily analyze complex multi-omics data sets of different resolutions. The approach extracts a major patterns of variants that are either unique or shared to the specific data types and aggregates them. The resulting so-called factors can then be linked to molecular processes and clinical or technical covariates.

Our analysis was among the first to apply the MOFA model to multi-omics and single-cell data of multiple samples. Importantly, these samples were from a clinical cohort of heart attack patients. This allowed us to identify multicellular immune signatures that are associated with outcome and disease state.

So, the availability of single-cell and multi-omic data sets increases, but often, the features of those data sets are only analyzed separately. This limits insights because usually, biological processes are the result of the interactions between multiple features and cell types. With our protocols, users can easily perform an integrated analysis of the complete dataset and identify those multicellular programs.

We think that applying this protocol to additional multi-omics data sets will generate new insights into other diseases or contexts. These insights will then inform future biomarker or therapeutic studies.