Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However, the high costs and methodological limitations of ST necessitate a more robust predictive model. In response, this paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features, capturing cellular morphology at individual spots, the local context around these spots, and the global tissue organization. By integrating these features through an effective fusion strategy, TRIPLEX achieves accurate gene expression prediction. Our comprehensive benchmark study, conducted on three public ST datasets and supplemented with Visium data from 10X Genomics, demonstrates that TRIPLEX outperforms current state-of-the-art models in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC). The model's predictions align closely with ground truth gene expression profiles and tumor annotations, underscoring TRIPLEX's potential in advancing cancer diagnosis and treatment.

翻译：近期空间转录组学技术的进展促进了组织背景下基因表达细节分析。然而，该技术的高成本和方法局限性要求开发更稳健的预测模型。为此，本文提出TRIPLEX——一种用于从全切片图像预测空间基因表达的新型深度学习框架。TRIPLEX独特地利用了多分辨率特征，捕获单个位点的细胞形态、位点周围的局部组织环境及全局组织架构。通过有效的融合策略整合这些特征，TRIPLEX实现了精确的基因表达预测。我们在三个公开空间转录组数据集上进行的全面基准研究（辅以10X Genomics的Visium数据）表明，TRIPLEX在均方误差、平均绝对误差和皮尔逊相关系数指标上均优于当前最优模型。模型预测结果与真实基因表达谱及肿瘤注释高度吻合，凸显了TRIPLEX在推动癌症诊断与治疗发展中的潜力。