SiGra: single-cell spatial elucidation through an image-augmented graph transformer

Ziyang Tang, Zuotian Li, Tieying Hou, Tonglin Zhang, Baijian Yang (), Jing Su () and Qianqian Song ()
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Ziyang Tang: Purdue University
Zuotian Li: Indiana University School of Medicine
Tieying Hou: Indiana University School of Medicine
Tonglin Zhang: Purdue University
Baijian Yang: Purdue University
Jing Su: Indiana University School of Medicine
Qianqian Song: Wake Forest University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27–50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.

Date: 2023
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DOI: 10.1038/s41467-023-41437-w

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