Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America.
Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, Florida, United States of America.
Brief Bioinform. 2023 Sep 20;24(5). doi: 10.1093/bib/bbad313.
Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.
整合单细胞多组学数据是一项具有挑战性的任务，它为深入了解复杂的细胞系统提供了新的见解。已经提出了各种计算方法来有效地整合这些快速积累的数据集，包括深度学习。然而，尽管深度学习在整合多组学数据方面已经被证明是成功的，并且在性能上优于经典的计算方法，但对于其在单细胞多组学数据整合中的应用还没有进行系统的研究。为了填补这一空白，我们进行了文献综述，从多个角度探讨了多模态深度学习技术在单细胞多组学数据整合中的应用。具体来说，我们首先总结了单细胞多组学数据中发现的不同模态。然后，我们回顾了目前用于处理多模态数据的深度学习技术，并根据数据模态、深度学习架构、融合策略、关键任务和下游分析对基于深度学习的单细胞多组学数据整合方法进行了分类。最后，我们深入了解了使用这些深度学习模型来整合多组学数据，以及更好地理解单细胞生物学机制。
