Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics

Zhixing Zhong, Junchen Hou, Zhixian Yao, Lei Dong, Feng Liu, Junqiu Yue, Tiantian Wu, Junhua Zheng, Gaoliang Ouyang, Chaoyong Yang and Jia Song ()
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Zhixing Zhong: Xiamen University
Junchen Hou: Xiamen University
Zhixian Yao: Shanghai Jiao Tong University
Lei Dong: Shanghai Jiao Tong University
Feng Liu: The University of Melbourne, Carlton
Junqiu Yue: Huazhong University of Science and Technology
Tiantian Wu: Xiamen University
Junhua Zheng: Shanghai Jiao Tong University
Gaoliang Ouyang: Xiamen University
Chaoyong Yang: Xiamen University
Jia Song: Shanghai Jiao Tong University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.

Date: 2024
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DOI: 10.1038/s41467-024-46413-6

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