Physics

Statistical inference

Polymer chemistry

Sample (material)

Inference

Mathematics

Biology

Politics

Operating system

Algorithm

Data mining

Statistics

Political science

Artificial intelligence

Profiling (computer programming)

Materials science

Representation (politics)

Computer science

Computational biology

Modal

Scalability

Data integration

Database

Thermodynamics

Genome biology

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.

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