空间分辨转录组学将高通量转录组测量与保留的空间细胞组织信息相结合。然而，许多技术无法达到单细胞分辨率。我们提出了 STdGCN，这是一种利用单细胞 RNA 测序 (scRNA-seq) 作为空间转录组 (ST) 数据中细胞类型反卷积参考的图模型。 STdGCN 结合了 scRNA-seq 的表达谱和 ST 数据的空间定位以进行反卷积。对多个数据集的广泛基准测试表明 STdGCN 优于 17 个最先进的模型。在人类乳腺癌 Visium 数据集中，STdGCN 描绘了基质、淋巴细胞和癌细胞，有助于肿瘤微环境分析。在人类心脏 ST 数据中，STdGCN 识别组织发育过程中内皮-心肌细胞通讯的变化。© 2024。作者。

Spatially resolved transcriptomics integrates high-throughput transcriptome measurements with preserved spatial cellular organization information. However, many technologies cannot reach single-cell resolution. We present STdGCN, a graph model leveraging single-cell RNA sequencing (scRNA-seq) as reference for cell-type deconvolution in spatial transcriptomic (ST) data. STdGCN incorporates expression profiles from scRNA-seq and spatial localization from ST data for deconvolution. Extensive benchmarking on multiple datasets demonstrates that STdGCN outperforms 17 state-of-the-art models. In a human breast cancer Visium dataset, STdGCN delineates stroma, lymphocytes, and cancer cells, aiding tumor microenvironment analysis. In human heart ST data, STdGCN identifies changes in endothelial-cardiomyocyte communications during tissue development.© 2024. The Author(s).