用细胞类型指导的专家网络，从病理图像预测单细胞基因表达。
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
- 按细胞类型分配专家，动态组合预测基因表达
- 在多个公开数据集上优于现有单细胞与点级基线方法
- 适合研究组织空间基因表达与细胞互作的生物学家
基于组织病理图像的单细胞空间转录组（ST）估计旨在从组织切片图像和细胞位置推断单个细胞的基因表达，从而减少对昂贵的单细胞ST检测的需求。与现有主要预测包含多个细胞的局部区域点级别表达的方法不同，该任务需建模细胞间表达差异，而这种差异强烈依赖于细胞类型。本文提出基因组引导的细胞类型特异性混合专家模型（GC-MoE），通过路由网络估计细胞类型概率，并软性组合细胞类型特异性专家进行基因表达预测。为进一步编码细胞类型相关的基因共表达程序，引入细胞类型特异性共表达感知预测器（CAP），并结合轻量级细胞间互作注意力模块（C2CA）捕捉邻近细胞上下文。在多个公开单细胞ST数据集上的实验与消融分析表明，该方法持续优于现有单细胞及适配的点级基线方法。
原文摘要 · Abstract (English)
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.
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