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Deep learning methods and applications in single-cell multimodal data integration
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DOI：10.1039/D5MO00062A.png)
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Abstract
En 中文
The integration of multimodal single-cell omics data is a state-of-art strategy for deciphering cellular heterogeneity and gene regulatory mechanisms. Recent advances in single-cell technologies have enabled the comprehensive characterization of cellular states and their interactions. However; integrating these high-dimensional and heterogeneous datasets poses significant computational challenges; including batch effects; sparsity; and modality alignment. Deep learning has shown great promise in addressing these issues through neural network-based frameworks; including variational autoencoders (VAEs) and graph neural networks (GNNs). In this Review; we examine cutting-edge deep learning methodologies for integrating single-cell multimodal data; discussing their architectures; applications; and limitations. We highlight key tools such as sciCAN; scJoint; and scMaui; which use deep learning techniques to harmonize various omics layers; improve feature extraction; and improve downstream biological analyses. Despite significant advancements; it remains challenging to ensure model interpretability; scalability; and generalizability across different datasets. Future directions of research in this field include the development of self-supervised learning strategies; transformer-based architectures; and federated learning frameworks to enhance the robustness and reproducibility of single-cell multi-omics integration.
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