Journal
GENOME BIOLOGY
Volume 21, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13059-020-02015-1
Keywords
Single cell; Multi-omics; Data integration; Factor analysis
Funding
- EMBL
- Higher Education, Research and Innovation Department of the French Embassy in the United Kingdom
- BMBF
- CRUK
- German Cancer Research Center
- Chan Zuckerberg Initiative
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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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