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MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.

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.

SUBMITTER: Argelaguet R

PROVIDER: S-EPMC7212577 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.

Argelaguet Ricard R Arnol Damien D Bredikhin Danila D Deloro Yonatan Y Velten Britta B Marioni John C JC Stegle Oliver O

Genome biology 20200511 1

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. ...[more]

PMID: 32393329

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