Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value for the characterizing and understanding of tissue architecture. However, the inherent heterogeneity and varying spatial resolutions present challenges in the joint analysis of multi-modal SRT data. We introduce a multi-modal geometric deep learning method, named stMMR, to effectively integrate gene expression, spatial location and histological information for accurate identifying spatial domains from SRT data. stMMR uses graph convolutional networks (GCN) and self-attention module for deep embedding of features within unimodal and incorporates similarity contrastive learning for integrating features across modalities. Comprehensive benchmark analysis on various types of spatial data shows superior performance of stMMR in multiple analyses, including spatial domain identification, pseudo-spatiotemporal analysis, and domain-specific gene discovery. In chicken heart development, stMMR reconstruct the spatiotemporal lineage structures indicating accurate developmental sequence. In breast cancer and lung cancer, stMMR clearly delineated the tumor microenvironment and identified marker genes associated with diagnosis and prognosis. Overall, stMMR is capable of effectively utilizing the multi-modal information of various SRT data to explore and characterize tissue architectures of homeostasis, development and tumor.

中文翻译：

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stMMR：具有多模态特征表示的空间解析转录组学的准确且稳健的空间域识别

使用空间解析转录组学 (SRT) 破译空间域对于表征和理解组织结构具有重要价值。然而，固有的异质性和不同的空间分辨率给多模态 SRT 数据的联合分析带来了挑战。我们引入了一种多模态几何深度学习方法，名为 stMMR，可以有效地整合基因表达、空间位置和组织学信息，从而从 SRT 数据中准确识别空间域。stMMR 使用图卷积网络 (GCN) 和自注意力模块在单模态中深度嵌入特征，并结合相似性对比学习来集成跨模态的特征。对各类空间数据的综合基准分析表明，stMMR 在空间域识别、伪时空分析和域特异性基因发现等多种分析中具有优越的性能。在鸡心脏发育中，stMMR 重建时空谱系结构，指示准确的发育顺序。在乳腺癌和肺癌中，stMMR 清楚地描绘了肿瘤微环境，并确定了与诊断和预后相关的标记基因。总体而言，stMMR能够有效地利用各种SRT数据的多模态信息来探索和表征稳态、发育和肿瘤的组织结构。