ArticleBriefings in bioinformatics2026
SpaJoint: a transfer learning method for spatial transcriptomics deconvolution.
Article in Briefings in bioinformatics, 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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Abstract
Currently, many widely used spatial transcriptomics (ST) technologies do not achieve single-cell resolution, with each spot capturing signals from multiple, potentially heterogeneous cells. As a result, a key challenge is to resolve the spatial distribution of distinct cell types within tissues, which is fundamental for understanding tissue architecture and biological function. Here we present a deconvolution method based on transfer learning, SpaJoint. This method integrates gene expression derived from single-cell RNA sequencing (scRNA-seq) and ST, taking into account the spatial correlation across locations of different spots. Comprehensive experiments demonstrate that SpaJoint achieves excellent performance in predicting the cell-type composition of spatial spots and identifying the spatial regions of cell types, thus highly effective and broadly applicable among various scRNA-seq and ST datasets. Additionally, it exhibits remarkable robustness to hyperparameters and provides significant advantage in computational efficiency.
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