HEXST applies a hexagonal shifted-window Transformer with rotary positional encodings, contrast-sensitive training objectives, and single-cell priors to predict gene expression from histology slides, outperforming prior models on seven datasets while preserving spatial heterogeneity.
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JASPER is a new joint Bayesian regression model for spatial transcriptomics that accounts for correlations between genes to better identify spatially varying genes.
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HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction
HEXST applies a hexagonal shifted-window Transformer with rotary positional encodings, contrast-sensitive training objectives, and single-cell priors to predict gene expression from histology slides, outperforming prior models on seven datasets while preserving spatial heterogeneity.
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JASPER: Joint Bayesian Analysis of Spatial Expression via Regression
JASPER is a new joint Bayesian regression model for spatial transcriptomics that accounts for correlations between genes to better identify spatially varying genes.