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Argelaguet, Ricard; Arnol, Damien; Bredikhin, Danila; Deloro, Yonatan; Velten, Britta; Marioni, John C; Stegle, Oliver
Genome Biology, 05/2020, Volume: 21, Issue: 1Journal Article
Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.
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