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

By R. Argelaguet, Damien Arnol, Danila Bredikhin, Yonatan Deloro, B. Velten, John C. Marioni, Oliver Stegle. Genome Biology, 2020

866 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/83c9b16ad8542507e83b156404787d151096dc11

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In Molecular Biology

Citations in the corpus

Computational principles and challenges in single-cell data integration (2021, background): “Querying exceptionally large datasets requires fast computational methods that typically rely on stochastic inference schemes 26,65 .”
Methods and applications for single-cell and spatial multi-omics (2023, background): “For example, MOFA+ 142 extends on this principle and builds on the Bayesian Group Factor Analysis framework to jointly model variation across spatial and temporal covariates and employs a sparse hierarchical prior architecture that handles both shared sources of variation and private variation that ”
Integration strategies of multi-omics data for machine learning analysis (2021, background): “MOFA/MOFA+ [141,142] Bayesian Factor Analysis biomarker discovery, systemic knowledge MOFA found new biomarkers and pathways associated with Alzeihmer’s disease based on MO data including proteomics, metabolomics, lipidomics [143].”
Best practices for single-cell analysis across modalities (2023, background): “The actual generated products, the proteins, take on either intracellular or extracellular tasks, and a subset of proteins are presented on the cell surface.”
Network analysis methods for studying microbial communities: A mini review (2021, methodology): “However, very few methods have been adapted for trans -kingdom analysis, motivating the use of general methods developed for multi-modal integrative analysis such as MOFA+ [100].”