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：近年来，空间转录组学（ST）技术的进步促进了组织内精细基因表达分析，然而其高昂成本和实验方法局限性亟需更稳健的预测模型。为此，本文提出TRIPLEX——一种新型深度学习框架，旨在从全切片图像（WSI）预测空间基因表达。TRIPLEX创新性地融合多分辨率特征，分别捕获单个位点的细胞形态、位点局部微环境以及全局组织结构。通过有效融合策略整合这些特征，TRIPLEX实现了精准的基因表达预测。我们在三个公开ST数据集及10X Genomics的Visium数据上开展的全面基准测试表明：TRIPLEX在均方误差（MSE）、平均绝对误差（MAE）和皮尔逊相关系数（PCC）指标上均超越当前最优模型。其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在推动癌症诊疗发展中的潜力。