European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK.
European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
Genome Biol. 2020 May 11;21(1):111. doi: 10.1186/s13059-020-02015-1.
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. 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.
技术进步使得能够在单细胞分辨率下对多个分子层进行分析，从而对来自多个样本或条件的细胞进行分析。因此，需要计算策略来分析包含多个数据模式和多个样本组的复杂实验设计的数据。我们提出了多组学因子分析 v2（MOFA+），这是一个用于单细胞多模式数据综合和可扩展集成的统计框架。MOFA+ 使用计算效率高的变分推断来重建数据的低维表示，并支持灵活的稀疏性约束，允许联合对多个样本组和数据模式的变化进行建模。
