ArticleGenome medicine2026
MGCL-ST: multi-view graph contrastive learning for spatial transcriptomics imputation.
Article in Genome medicine, 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
High-resolution, continuous spatial gene expression profiling is critical for dissecting complex tissue microenvironments, yet current spatial transcriptomics (ST) platforms suffer from spatial gaps and insufficient resolution. Here, we present MGCL-ST, a multi-view graph contrastive learning method for super-resolution ST imputation. By jointly modeling local and global spatial graphs with histological features derived from a pathology foundation model, MGCL-ST accurately imputes unmeasured gene expression across the tissue space. Evaluated across three diverse platforms, MGCL-ST outperforms state-of-the-art methods in imputation accuracy and spatial clustering, significantly enhancing biological interpretability and enabling the precise mapping of tumor microenvironments. MGCL-ST is available at https://github.com/guxin2002/MGCL-ST .
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