ArticlePeerJ2026
A deep learning representation and spatial Bayesian cell-type deconvolution for spatial transcriptomics.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Background: Spatial transcriptomics provides unprecedented insights into gene expression in the spatial context of tissues; however, some mainstream techniques lack quantitative analysis of uncertainty. Methods: In this study, we propose a computational framework-Spatial Deconvolution Results: We compared our method with existing approaches on two datasets: a pancreatic ductal adenocarcinoma (PDAC) dataset with matched single-cell RNA sequencing and spatial transcriptomics, and a human thymus dataset. Across both datasets, our method estimated cell type proportions more accurately than existing methods, particularly for rare cell types, and recovered biologically meaningful spatial patterns such as the distribution of T cells and plasma cells in PDAC. The Bayesian formulation also provides a quantification of uncertainty for each estimate, which aids the interpretation of spatial heterogeneity in complex tissues.
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