SpCAST: Decoding spatial transcriptomics at single-cell resolution with fast and interpretable analysis
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| Format: | Preprint |
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2026
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| _version_ | 1866911719953006592 |
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| author | Zhang, Yiyang Zhao, Bokai Zhang, Xiaoru Du, Zongchang Sun, Xiaojuan Jiang, Tianzi |
| author_facet | Zhang, Yiyang Zhao, Bokai Zhang, Xiaoru Du, Zongchang Sun, Xiaojuan Jiang, Tianzi |
| contents | Single-cell-resolution spatial transcriptomics profiles gene expression at cellular locations in native tissues, yet accurate cell-type annotation remains challenging: imaging-based platforms are constrained by targeted gene panels, whereas sequencing-based platforms often suffer from sparse molecular capture and dropout. Reliable transfer of cell-type labels from single-cell RNA sequencing references is therefore critical for interpreting targeted and sparse spatial datasets. Here, we present SpCAST, a Kolmogorov--Arnold network-based framework for reference-guided spatial transcriptomics analysis. SpCAST captures nonlinear mappings between reference and spatial expression profiles and uses feature attribution to prioritize genes supporting cell-type predictions. Within a unified framework, SpCAST performs cell-type label transfer, spatial gene-expression reconstruction and marker-gene candidate prioritization. We benchmarked SpCAST on 53 datasets comprising 413,376 spatial cells across five technologies and diverse tissue contexts. SpCAST achieved competitive annotation performance with reduced runtime relative to representative existing methods. Case studies demonstrated that SpCAST supports cross-species label transfer and candidate assignment of originally unlabeled cells. It also reconstructs marker-gene expression patterns with improved spatial concordance and prioritizes cell-type-associated marker genes. Together, these results support SpCAST as an efficient and interpretable framework for extracting cell-type and gene-level information from targeted and sparse single-cell-resolution spatial transcriptomics data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26904 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SpCAST: Decoding spatial transcriptomics at single-cell resolution with fast and interpretable analysis Zhang, Yiyang Zhao, Bokai Zhang, Xiaoru Du, Zongchang Sun, Xiaojuan Jiang, Tianzi Cell Behavior Single-cell-resolution spatial transcriptomics profiles gene expression at cellular locations in native tissues, yet accurate cell-type annotation remains challenging: imaging-based platforms are constrained by targeted gene panels, whereas sequencing-based platforms often suffer from sparse molecular capture and dropout. Reliable transfer of cell-type labels from single-cell RNA sequencing references is therefore critical for interpreting targeted and sparse spatial datasets. Here, we present SpCAST, a Kolmogorov--Arnold network-based framework for reference-guided spatial transcriptomics analysis. SpCAST captures nonlinear mappings between reference and spatial expression profiles and uses feature attribution to prioritize genes supporting cell-type predictions. Within a unified framework, SpCAST performs cell-type label transfer, spatial gene-expression reconstruction and marker-gene candidate prioritization. We benchmarked SpCAST on 53 datasets comprising 413,376 spatial cells across five technologies and diverse tissue contexts. SpCAST achieved competitive annotation performance with reduced runtime relative to representative existing methods. Case studies demonstrated that SpCAST supports cross-species label transfer and candidate assignment of originally unlabeled cells. It also reconstructs marker-gene expression patterns with improved spatial concordance and prioritizes cell-type-associated marker genes. Together, these results support SpCAST as an efficient and interpretable framework for extracting cell-type and gene-level information from targeted and sparse single-cell-resolution spatial transcriptomics data. |
| title | SpCAST: Decoding spatial transcriptomics at single-cell resolution with fast and interpretable analysis |
| topic | Cell Behavior |
| url | https://arxiv.org/abs/2605.26904 |