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Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network

Author

Listed:
  • Wenyi Yang

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Pingping Wang

    (Harbin Medical University)

  • Shouping Xu

    (Harbin Medical University Cancer Hospital)

  • Tao Wang

    (Northwestern Polytechnical University)

  • Meng Luo

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Yideng Cai

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Chang Xu

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Guangfu Xue

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Jinhao Que

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Qian Ding

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Xiyun Jin

    (Harbin Medical University)

  • Yuexin Yang

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Fenglan Pang

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Boran Pang

    (Tongji University School of Medicine)

  • Yi Lin

    (Harbin Medical University)

  • Huan Nie

    (School of Life Science and Technology, Harbin Institute of Technology)

  • Zhaochun Xu

    (Harbin Medical University)

  • Yong Ji

    (Harbin Medical University)

  • Qinghua Jiang

    (School of Life Science and Technology, Harbin Institute of Technology
    Harbin Medical University)

Abstract

The inference of cell–cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.

Suggested Citation

  • Wenyi Yang & Pingping Wang & Shouping Xu & Tao Wang & Meng Luo & Yideng Cai & Chang Xu & Guangfu Xue & Jinhao Que & Qian Ding & Xiyun Jin & Yuexin Yang & Fenglan Pang & Boran Pang & Yi Lin & Huan Nie , 2024. "Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51329-2
    DOI: 10.1038/s41467-024-51329-2
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    References listed on IDEAS

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