COAST：用于空间转录组学中基因表达预测的上下文感知差分学习
COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics
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中文总结 AI 辅助
针对空间转录组学基因表达预测受成本和通量限制的问题，提出COAST框架，通过特定调制调节上下文特征，用Transformer编码器聚合斑点令牌，结合绝对与差分回归训练，实验证明该方法在多数据集上能有效提升预测效果。
中文摘要 AI 辅助
空间转录组学能够对空间基因表达进行分析，但受高成本和低通量限制，促使从苏木精和伊红（H&E）组织病理学图像进行预测。现有上下文感知方法主要监督绝对表达，很少明确使用斑点间的相对表达关系。我们提出了COAST，一种用于空间基因表达预测的上下文感知差分学习框架。COAST通过特定类型调制来调节局部和全局上下文特征，并使用Transformer编码器聚合目标和上下文斑点令牌，以捕获细粒度局部模式和玻片级结构。它通过联合目标进行训练，该联合目标将绝对表达回归与目标和上下文斑点之间的有符号差分回归相结合。在多个空间转录组学数据集上的实验表明，基于相关性和分布的指标有一致的改进，证明了上下文感知差分学习对基于组织学的空间基因表达预测的有效性。
英文摘要
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.
发表机构
- School of Electrical Engineering, Korea University（韩国大学电气工程学院）
机构由 AI 辅助整理，请以论文原文为准。
