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

2026-07-10

AI Summary

Spatial transcriptomics remains limited in widespread adoption due to high costs and low throughput, creating an urgent need for methods that can accurately predict gene expression from routine H&E images. To address this challenge, this work proposes COAST, a novel framework that explicitly models relative expression differences between spatial locations. COAST integrates type-specific contextual modulation with a Transformer encoder to jointly capture local fine-grained patterns and whole-slide structural information. A tailored joint loss function is introduced to simultaneously optimize both absolute expression values and signed differential relationships. Evaluated across multiple datasets, COAST significantly improves prediction correlation and distributional consistency, demonstrating the efficacy of context-aware differential learning for spatial gene expression inference.

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.

Problem

• spatial transcriptomics
• gene expression prediction
• H&E histopathology
• context-aware learning
• differential expression

Innovation

• context-aware learning
• differential regression
• spatial transcriptomics
• Transformer encoder
• gene expression prediction