用上下文差异学习提升病理图像预测基因表达精度
COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics
- 通过局部与全局上下文调制，融合目标与参考点位特征
- 联合回归绝对表达与点对间相对差异，提升预测相关性
- 适合需高精度空间基因表达重建的研究者使用
空间转录组学可实现空间基因表达谱的分析，但受限于高昂成本与低通量，推动从H&E病理图像中进行预测。现有上下文感知方法多以绝对表达为监督信号，而点间相对表达关系常被忽略。本文提出COAST框架，通过类型特异性调制条件化局部与全局上下文特征，并利用Transformer编码器聚合目标点与上下文点的标记，捕捉细粒度局部模式与整体切片结构。模型采用联合目标函数，同时优化绝对表达回归与目标点与上下文点间的符号差异回归。在多个空间转录组数据集上的实验表明，该方法在相关性与分布匹配指标上均取得一致提升，验证了上下文感知差异学习在基于病理图像的空间基因表达预测中的有效性。
原文摘要 · Abstract (English)
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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