Graph Neural Networks (GNN) are reshaping our understanding of biomedicine
and diseases by revealing the deep connections among genes and cells. As both
algorithmic and biomedical technologies have advanced significantly, we're
entering a transformative phase of personalized medicine. While pioneering
tools like Graph Attention Networks (GAT) and Graph Convolutional Neural
Networks (Graph CNN) are advancing graph-based learning, the rise of
single-cell sequencing techniques is reshaping our insights on cellular
diversity and function. Numerous studies have combined GNNs with single-cell
data, showing promising results. In this work, we highlight the GNN
methodologies tailored for single-cell data over the recent years. We outline
the diverse range of graph deep learning architectures that center on GAT
methodologies. Furthermore, we underscore the several objectives of GNN
strategies in single-cell data contexts, ranging from cell-type annotation,
data integration and imputation, gene regulatory network reconstruction,
clustering and many others. This review anticipates a future where GNNs become
central to single-cell analysis efforts, particularly as vast omics datasets
are continuously generated and the interconnectedness of cells and genes
enhances our depth of knowledge in biomedicine.

图神经网络是通过揭示基因和细胞之间的深层连接来重塑我们对生物医学和疾病的理解。本文总结了过去几年针对单细胞数据量身定制的图神经网络方法，并强调了以图注意力网络为中心的多样化图深度学习架构及其在单细胞数据上的几个目标，包括细胞类型注释、数据整合和插补、基因调控网络重建、聚类等。预计在生物医学领域中，随着不断产生的大规模组学数据和细胞与基因之间的相互关联性加强，图神经网络将成为单细胞分析工作的核心。