Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE.

尽管出现了同时测量单细胞中多种组学模态的实验方法，但大多数单细胞数据集仅包含一种模态。整合来自多种模式的组学数据的一个主要障碍是不同的组学层通常具有不同的特征空间。在这里，我们提出了一个称为 GLUE（图链接统一嵌入）的计算框架，它通过显式地建模跨组学层的监管交互来弥合差距。系统基准测试表明，与用于异构单细胞多组学数据的最先进工具相比，GLUE 更准确、更强大且可扩展。我们将 GLUE 应用于各种具有挑战性的任务，包括三组学整合、整合调控推理和基于数百万个细胞的多组学人类细胞图谱构建，其中 GLUE 能够更正以前的注释。GLUE 采用模块化设计，可针对新的分析任务灵活扩展和增强。完整包可在 https://github.com/gao-lab/GLUE 在线获取。