细胞类型原型引导的神经网络用于病理图像基因表达估计
Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images
将单细胞RNA测序数据构建的细胞类型原型作为结构正则，引导从病理图像预测基因表达，为计算病理中的弱监督回归与可解释性提供了新思路。
原文依据 we propose a Cell-type Prototype-informed Neural Network (CPNN) that leverages publicly available single-cell RNA-sequencing datasets.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从病理图像估计切片级和斑块级基因表达谱，能够实现快速、低成本的分子分析，具有广泛的临床影响。尽管现有方法取得了显著成果，但它们将基因表达仅视为切片级或斑点级信号，未考虑到测量的表达源于底层细胞级表达的聚合。为了明确引入这一缺失的细胞分辨率引导，我们提出了细胞类型原型引导的神经网络（CPNN），利用公开的单细胞RNA测序数据集。由于单细胞测量存在噪声且未与组织学图像配对，我们首先估计细胞类型原型——即捕获稳定基因-基因共变异模式的平均表达谱。然后，CPNN直接从图像中学习细胞类型组成权重，并建模原型与观察到的批量或空间表达之间的关系，提供了一个具有生物学基础且结构正则化的预测框架。我们在三个切片级数据集和三个斑块级空间转录组学数据集上评估了CPNN。在所有设置中，CPNN在Spearman相关性方面均取得了最高性能。此外，通过可视化推断的组成权重，我们的框架提供了关于哪些细胞类型驱动预测表达的可解释性见解。代码已公开于https://github.com/naivete5656/CPNN。
英文原文摘要 1443 字符
Estimating slide- and patch-level gene expression profiles from pathology images enables rapid and low-cost molecular analysis with broad clinical impact. Despite strong results, existing approaches treat gene expression as a mere slide- or spot-level signal and do not incorporate the fact that the measured expression arises from the aggregation of underlying cell-level expression. To explicitly introduce this missing cell-resolved guidance, we propose a Cell-type Prototype-informed Neural Network (CPNN) that leverages publicly available single-cell RNA-sequencing datasets. Since single-cell measurements are noisy and not paired with histology images, we first estimate cell-type prototypes--mean expression profiles that capture stable gene-gene co-variation patterns. CPNN then learns cell-type compositional weights directly from images and models the relationship between prototypes and observed bulk or spatial expression, providing a biologically grounded and structurally regularized prediction framework. We evaluate CPNN on three slide-level datasets and three patch-level spatial transcriptomics datasets. Across all settings, CPNN achieves the highest performance in terms of Spearman correlation. Moreover, by visualizing the inferred compositional weights, our framework provides interpretable insights into which cell types drive the predicted expression.Code is publicly available at https://github.com/naivete5656/CPNN.