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

作者：R. Argelaguet, Damien Arnol, Danila Bredikhin, Yonatan Deloro, B. Velten, J. Marioni, O. Stegle · 发表于：Genome Biology · 年份：2020 · DOI：10.1186/s13059-020-02015-1 · 被引用次数：893 · 研究领域：Biology、Medicine

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.