近年来，空间转录组学的进展使得基因表达在细胞/斑点分辨率下进行测量，同时保留组织的空间信息和组织学图像。准确地识别斑点的空间域是空间转录组学分析中各种下游任务的关键步骤。为了去除基因表达中的噪声，已经开发了几种方法来将组织病理学图像与空间转录组学数据分析相结合。然而，这些方法要么仅使用图像进行斑点的空间关系，要么单独学习基因表达和图像的嵌入，而没有完全耦合这些信息。在这里，我们提出一种新的方法ConGI，通过对比学习来准确地利用基因表达与组织学图像的空间域。具体来说，我们设计了三个对比损失函数，在两种模态之间（基因表达和图像数据）内部和之间来学习共同的表示。学习到的表示然后被用于在肿瘤和正常的空间转录组学数据集上聚类空间域。结果表明，ConGI在空间域的识别方面优于现有的方法。此外，所学的表示也显示出在各种下游任务，包括轨迹推断、聚类和可视化方面具有强大的功能。©作者（们）2023。由牛津大学出版社出版。保留所有权利。如需授权，请发送电子邮件至：journals.permissions@oup.com。

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.