空间转录组学技术生成具有空间上下文的基因表达谱，需要空间相关的分析工具来完成三项关键任务：空间聚类、多样本整合和细胞类型解混。我们提出了GraphST，一种图形自我监督对比学习方法，充分利用空间转录组学数据，优于现有方法。它将图神经网络与自我监督对比学习相结合，通过最小化空间相邻点之间的嵌入距离和相反情况来学习信息丰富、具有区分性的点表示形式。我们在多种组织类型和技术平台上演示了GraphST。GraphST在大脑和胚胎组织中实现了10％更高的聚类准确度，并更好地描绘了细粒度组织结构。GraphST也是唯一能够联合分析垂直或水平整合的多个组织切片并校正批次效应的方法。最后，GraphST表现出优越的细胞类型解混能力，捕获了乳腺肿瘤组织中淋巴结生发中心和衰竭的肿瘤浸润T细胞等空间区域。 ©2023年作者。

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.© 2023. The Author(s).