Pan Duo, Li Huamei, Liu Hongde, Sun Xiao
State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Oct 25;38(5):1010-1017. doi: 10.7507/1001-5515.202104073.
The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.
单细胞测序技术的出现使人们能够以前所未有的精度观察细胞。然而，在一次单细胞RNA测序（scRNA-seq）实验中难以捕获所有细胞和基因的信息。单一模态的单细胞数据无法详细解释细胞状态和系统变化。单细胞数据的整合分析旨在解决这两类问题。整合多个scRNA-seq数据可以收集完整的细胞类型，并为细胞图谱的构建提供有力推动。整合单细胞多模态数据可用于研究跨模态的因果关系和基因调控机制。数据整合方法的发展与应用有助于充分探索单细胞数据的丰富性和相关性，并发现有意义的生物学变化。基于此，本文综述了多个scRNA-seq数据整合和单细胞多模态数据整合的基本原理、方法及应用。此外，还讨论了现有方法的优缺点。最后，对未来发展进行了展望。
