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Volume 52 Issue 1
Jan.  2025
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Article Navigation >  Journal of Genetics and Genomics  > 2025  >  52(1): 93-104

SpaGRA: Graph augmentation facilitates domain identification for spatially resolved transcriptomics

doi: 10.1016/j.jgg.2024.09.015
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    a. Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China;

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    b. MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China;

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    c. Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen, Guangdong 518036, China;

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    d. Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China;

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    e. Institute of Science and Technology for Brain-Inspired Intelligence, Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai 200433, China

Funds:

This work was supported by the National Natural Science Foundation of China (Nos. 62303271, U1806202, 62103397), the Natural Science Foundation of Shandong Province (ZR2023QF081). Funding for open access charge: the National Natural Science Foundation of China (Nos. 62303271, U1806202).

  • Received Date: 2024-06-23
  • Accepted Date: 2024-09-22
  • Rev Recd Date: 2024-09-07
    • Available Online: 2025-07-11
  • Publish Date: 2024-10-02
    Abstract
  • Recent advances in spatially resolved transcriptomics (SRT) have provided new opportunities for characterizing spatial structures of various tissues. Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks. Currently, most methods define adjacency relation between cells or spots by their spatial distance in SRT data, which overlooks key biological interactions like gene expression similarities, and leads to inaccuracies in spatial domain identification. To tackle this challenge, we propose a novel method, SpaGRA (https://github.com/sunxue-yy/SpaGRA), for automatic multi-relationship construction based on graph augmentation. SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks (GATs). This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning. Additionally, SpaGRA uses these multi-view relationships to construct negative samples, addressing sampling bias posed by random selection. Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols. Using SpaGRA, we analyze the functional regions in the mouse hypothalamus, identify key genes related to heart development in mouse embryos, and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data. Overall, SpaGRA can effectively characterize spatial structures across diverse SRT datasets.
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