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Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network

Yuying Huo,  Yilang Guo,  Jiakang Wang,  Huijie Xue,  Yujuan Feng,  Weizheng Chen,  Xiangyu Li

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文章导航 > Journal of Genetics and Genomics  > 2023 >  50(9): 720-733
Yuying Huo, Yilang Guo, Jiakang Wang, Huijie Xue, Yujuan Feng, Weizheng Chen, Xiangyu Li. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network[J]. 遗传学报, 2023, 50(9): 720-733. doi: 10.1016/j.jgg.2023.06.005
引用本文: Yuying Huo, Yilang Guo, Jiakang Wang, Huijie Xue, Yujuan Feng, Weizheng Chen, Xiangyu Li. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network[J]. 遗传学报, 2023, 50(9): 720-733. doi: 10.1016/j.jgg.2023.06.005
shu
Yuying Huo, Yilang Guo, Jiakang Wang, Huijie Xue, Yujuan Feng, Weizheng Chen, Xiangyu Li. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network[J]. Journal of Genetics and Genomics, 2023, 50(9): 720-733. doi: 10.1016/j.jgg.2023.06.005
Citation: Yuying Huo, Yilang Guo, Jiakang Wang, Huijie Xue, Yujuan Feng, Weizheng Chen, Xiangyu Li. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network[J]. Journal of Genetics and Genomics, 2023, 50(9): 720-733. doi: 10.1016/j.jgg.2023.06.005
shu

Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network

doi: 10.1016/j.jgg.2023.06.005

  • a. School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China;

  • b. School of Software Engineering, Beijing University of Technology, Beijing 100124, China;

  • c. Baidu Inc., Beijing 100193, China

基金项目: 

The authors wish to thank Tiantian Guo, Yan Pan, and Wenbo Guo for their discussions and suggestions

Feng Bao for the technical support. This work was supported by National Natural Science Foundation of China (62003028). X.L. was supported by a Scholarship from the China Scholarship Council.

详细信息
    通讯作者:

    Xiangyu Li,E-mail:lixiangyu@bjtu.edu.cn

Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network

  • a. School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China;

  • b. School of Software Engineering, Beijing University of Technology, Beijing 100124, China;

  • c. Baidu Inc., Beijing 100193, China

Funds: 

The authors wish to thank Tiantian Guo, Yan Pan, and Wenbo Guo for their discussions and suggestions

Feng Bao for the technical support. This work was supported by National Natural Science Foundation of China (62003028). X.L. was supported by a Scholarship from the China Scholarship Council.

    摘要
  • 摘要: Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial transcriptomics can provide multimodal and complementary information simultaneously, including gene expression profiles, spatial locations, and histology images. However, most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology images. To fully leverage the multi-modal information, we propose a SPAtially embedded Deep Attentional graph Clustering (SpaDAC) method to identify spatial domains while reconstructing denoised gene expression profiles. This method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological connectives. Benchmark results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics datasets. SpaDAC is a valuable tool for spatial domain detection, facilitating the comprehension of tissue architecture and cellular microenvironment. The source code of SpaDAC is freely available at Github (https://github.com/huoyuying/SpaDAC.git).
    Abstract: Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial transcriptomics can provide multimodal and complementary information simultaneously, including gene expression profiles, spatial locations, and histology images. However, most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology images. To fully leverage the multi-modal information, we propose a SPAtially embedded Deep Attentional graph Clustering (SpaDAC) method to identify spatial domains while reconstructing denoised gene expression profiles. This method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological connectives. Benchmark results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics datasets. SpaDAC is a valuable tool for spatial domain detection, facilitating the comprehension of tissue architecture and cellular microenvironment. The source code of SpaDAC is freely available at Github (https://github.com/huoyuying/SpaDAC.git).
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  • 收稿日期:  2023-02-14
  • 录用日期:  2023-06-16
  • 修回日期:  2023-06-15
  • 刊出日期:  2023-06-23

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