A unified approach to spatial domain detection and cell-type deconvolution in spot-based spatial transcriptomics
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909915825569792 |
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| author | Koo, Hyun Jung Molstad, Aaron J. |
| author_facet | Koo, Hyun Jung Molstad, Aaron J. |
| contents | Popular technologies for generating spatially resolved transcriptomic data measure gene expression at the resolution of a "spot", i.e., a small tissue region 55 microns in diameter. Each spot can contain many cells of different types. In typical analyses, researchers are interested in using these data to identify and profile discrete spatial domains in the tissue. In this paper, we propose a new method, DUET, that simultaneously identifies discrete spatial domains and estimates each spot's cell-type proportion. This allows the identified spatial domains to be characterized in terms of the cell type proportions, which affords interpretability and biological insight. DUET utilizes a constrained version of model-based convex clustering, and as such, can accommodate Poisson, negative binomial, normal, and other types of expression data. Through simulation studies and multiple applications, we show that DUET can achieve better clustering and deconvolution performance than existing methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_06204 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A unified approach to spatial domain detection and cell-type deconvolution in spot-based spatial transcriptomics Koo, Hyun Jung Molstad, Aaron J. Applications Methodology Popular technologies for generating spatially resolved transcriptomic data measure gene expression at the resolution of a "spot", i.e., a small tissue region 55 microns in diameter. Each spot can contain many cells of different types. In typical analyses, researchers are interested in using these data to identify and profile discrete spatial domains in the tissue. In this paper, we propose a new method, DUET, that simultaneously identifies discrete spatial domains and estimates each spot's cell-type proportion. This allows the identified spatial domains to be characterized in terms of the cell type proportions, which affords interpretability and biological insight. DUET utilizes a constrained version of model-based convex clustering, and as such, can accommodate Poisson, negative binomial, normal, and other types of expression data. Through simulation studies and multiple applications, we show that DUET can achieve better clustering and deconvolution performance than existing methods. |
| title | A unified approach to spatial domain detection and cell-type deconvolution in spot-based spatial transcriptomics |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2511.06204 |