scAdaptAnno: Target graph domain adaptation for cross-patient single-cell annotation transfer in tumor microenvironments

Abstract

Aims:Single-cell RNA sequencing (scRNA-seq) 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

Authors contribution

Cao XY: Methodology, software, formal analysis, writing-original draft, writing review & editing. Zeng ZY: Methodology, software, formal analysis, data curation, writing review & editing. You ZH, Huang YA: Conceptualization, supervision, project administration.

Conflicts of interest

Zhu-Hong You is an Editorial Board Member of Computational Biomedicine . The other authors declare no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used in this study were obtained from the NCBI Gene Expression Omnibus (GEO) and ArrayExpress repositories. For leukemia, three datasets were included: samples from patients AYL050 and OX1164 (GEO accession GSE142213[35]), patients PBMMC_1, PBMMC_2, and PBMMC_3 (GEO accession GSE132509[36]), and patients P6 and P7 (GEO accession GSE154109[37]). Breast cancer data consisted of three datasets sourced from GEO or ArrayExpress, specifically patients tnbc1 and tnbc6k (GEO accession GSE143423[38]), patients sc5rJUQ024, sc5rJUQ026, sc5rJUQ033, sc5rJUQ050, and sc5rJUQ060 (ArrayExpress accession E-MTAB-8107[39]), and patients corresponding to GSM4555888 and GSM4555891 (GEO accession GSE150660[40]). In addition, one colorectal cancer dataset containing samples from patients scrEXT009, scrEXT010, and scrEXT020 was retrieved from ArrayExpress under accession E-MTAB-8107[39]. The source code of scAdaptAnno, together with the scripts used for data preprocessing, model training, and evaluation, is publicly available at: https://github.com/ChrisOliver2345/scAdaptAnno.

Funding

None.

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