MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.
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Peer-reviewed
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Abstract
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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Keywords
Data integration, Factor analysis, Multi-omics, Single cell, Animals, DNA Methylation, Embryonic Development, Factor Analysis, Statistical, Frontal Lobe, Mice, Sequence Analysis, RNA, Single-Cell Analysis
Journal Title
Genome Biol
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Journal ISSN
1474-7596
1474-760X
1474-760X
Volume Title
21
Publisher
Springer Science and Business Media LLC
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Sponsorship
Cancer Research UK (22231)