做空间转录组学或计算病理学的研究者可以直接用GC-MoE替代昂贵的单细胞测序，从常规组织切片中预测单细胞基因表达，省成本又提精度。
该研究提出了一种名为GC-MoE的新方法，用于从组织学图像和细胞位置预测单个细胞的基因表达，从而降低单细胞空间转录组学测量的成本。与现有方法不同，GC-MoE通过路由网络估计细胞类型概率，并软性地组合细胞类型特异性专家来预测基因表达，从而捕捉细胞间的表达变异性。该方法还引入了细胞类型特异性共表达感知预测器和轻量级细胞间交互注意力模块，以编码细胞类型依赖的基因程序。在公共单细胞空间转录组数据集上的实验表明，GC-MoE在性能上优于现有的单细胞和基于点的基线方法。这项工作为从组织学图像推断单细胞基因表达提供了更精确的工具，对生物医学研究具有重要意义。
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing the need for costly single-cell ST measurements. Unlike existing histology-to-ST methods that mainly predict spot-level profiles for local regions containing multiple cells, this task requires modeling cell-to-cell expression variability, which is strongly structured by cell type. We propose Genomics-Guided Cell-Type-Specific Mixture-of-Experts (GC-MoE), which estimates cell-type probabilities with a routing network and softly combines cell-type-specific experts for gene expression prediction. To further encode cell-type-dependent gene programs, we introduce the Cell-Type-Specific Co-Expression-Aware Predictor (CAP), together with a lightweight Cell-to-Cell Interaction Attention (C2CA) module for neighboring-cell context. Experiments and ablations on public single-cell ST datasets show consistent improvements over existing single-cell and adapted spot-level baselines.