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arXiv · 2607.09166

COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics

Keunho Byeon·Sunhong Park·Jeewoo Lim·Jin Tae Kwak
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Abstract

Spatial transcriptomics enables profiling of spatial gene expression but is limited by high cost and low throughput, motivating prediction from H&E histopathology images. Existing context-aware methods mainly supervise absolute expression, while relative expression relationships between spots are rarely used explicitly. We propose COAST, a context-aware differential learning framework for spatial gene expression prediction. COAST conditions the local and global context features with type-specific modulation and aggregates the target and context spot tokens using a Transformer encoder to capture both fine-grained local patterns and slide-level structure. It is trained with a joint objective that combines absolute expression regression with signed differential regression between the target and context spots. Experiments on multiple spatial transcriptomics datasets show consistent improvements in correlation- and distribution-based metrics, demonstrating the effectiveness of context-aware differential learning for histology-based spatial gene expression prediction.

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Keunho Byeon, Sunhong Park, Jeewoo Lim, Jin Tae Kwak. 2026-07-10. COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics. https://arxiv.org/abs/2607.09166

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