scAdaptAnno: Target graph domain adaptation for cross-patient single-cell annotation transfer in tumor microenvironments
*Correspondence to:
Zhu-Hong You, School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.
E-mail: zhuhongyou@gmail.com
Yu-An Huang, School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China. E-mail: zhuhongyou@gmail.com; yuanhuang@nwpu.edu.cn
Yu-An Huang, School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China. E-mail: zhuhongyou@gmail.com; yuanhuang@nwpu.edu.cn
Comput Biomed. 2026;1:202612. 10.70401/cbm.2026.0018
Received: March 31, 2026Accepted: June 12, 2026Published: June 12, 2026
This manuscript is made available in its unedited form to allow early access to the
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Abstract
Aims: Single-cell RNA sequencing has emerged as a cornerstone technology in tumor microenvironment research. Accurate cell-type annotation is fundamental to downstream scRNA-seq analysis. However, automated tools are often highly sensitive to dataset noise and show limited adaptability in cross-patient scenarios. To address these challenges, we propose scAdaptAnno, a graph-based target domain adaptation framework for cross-patient single-cell annotation.
Methods: In the graph construction phase, scAdaptAnno integrates both gene expression similarity and biological prior knowledge to build a more biologically meaningful cell graph. By leveraging cell representations enriched with biological priors to mitigate noise in gene expression data and by implementing a bidirectional adaptation mechanism, the model achieves source-free target domain alignment.
Results: We performed comprehensive benchmarking against nine leading methods across multiple datasets spanning various cancer types. The results demonstrate that scAdaptAnno achieves state-of-the-art performance.
Conclusion: scAdaptAnno is a robust and accurate single-cell annotation tool that excels in cross-patient cell-type annotation transfer. By integrating biologically informed graph construction and bidirectional source-free domain adaptation, it delivers reliable, noise-resistant performance across diverse tumor microenvironments, providing an effective solution for automated cell-type annotation in multi-patient scRNA-seq studies.
Keywords
scRNA-seq, cell-type annotation, tumor microenvironment, graph domain adaptation
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Cao XY, Zeng ZY, You ZH, Huang YA. scAdaptAnno: Target graph domain adaptation for cross-patient single-cell annotation transfer in tumor microenvironments. Comput Biomed. 2026;1:202612. https://doi.org/10.70401/cbm.2026.0018
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Cao XY, Zeng ZY, You ZH, Huang YA. scAdaptAnno: Target graph domain adaptation for cross-patient single-cell annotation transfer in tumor microenvironments. Comput Biomed. 2026;1:202612. https://doi.org/10.70401/cbm.2026.0018
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