期刊
GENOME BIOLOGY
卷 21, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s13059-020-02015-1
关键词
Single cell; Multi-omics; Data integration; Factor analysis
资金
- EMBL
- Higher Education, Research and Innovation Department of the French Embassy in the United Kingdom
- BMBF
- CRUK
- German Cancer Research Center
- Chan Zuckerberg Initiative
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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