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.

中文翻译：

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MOFA+：多模态单细胞数据综合整合的统计框架

技术进步使得能够以单细胞分辨率分析多个分子层，分析来自多个样本或条件的细胞。因此，越来越需要计算策略来分析来自复杂实验设计的数据，这些实验设计包括多种数据模式和多组样本。我们提出了 Multi-Omics Factor Analysis v2 (MOFA+)，这是一个用于全面和可扩展地整合单细胞多模态数据的统计框架。MOFA+ 使用计算效率高的变分推理重建数据的低维表示，并支持灵活的稀疏约束，允许跨多个样本组和数据模态联合建模变化。