ArticleGenome biology2025
High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Ambiguity-Aware Multi-Stage Cell-Type Annotation for Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Mapping immunosuppressive terrain in high-grade serous ovarian cancer: tumor-intrinsic, stromal, and spatial drivers of therapy response.NPJ precision oncology · 2026Review
- Bridging the Precision Gap in Rheumatoid Arthritis: Spatial Transcriptomics, Spatial Proteomics, and Artificial Intelligence in Precision Health.Biomedicines · 2026Review
- Highly accurate reference and method selection for universal cross-data set cell type annotation with CAMUS.Genome research · 2025Article
- High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper.Genome biology · 2025Article
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Authors and funding
4 authors.
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Abstract
In this paper, we develop a heterogeneous graph neural network, STAMapper, to transfer the cell-type labels from single-cell RNA-sequencing (scRNA-seq) data to single-cell spatial transcriptomics (scST) data. We collect 81 scST datasets consisting of 344 slices and 16 paired scRNA-seq datasets from eight technologies and five tissues to validate the efficiency of STAMapper. STAMapper achieves the best performance on 75 out of 81 datasets compared to competing methods in accuracy. STAMapper demonstrates enhanced performance over manual annotations, particularly at the boundaries of cell clusters, enables the unknown cell-type detection in scST data, and exhibits precise cell subtype annotations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.