Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

中文翻译：

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STASCAN 通过深度学习破译空间转录组学中的精细分辨率细胞分布图


空间转录组学技术已被广泛应用于通过解析组织中的基因表达谱来解码细胞分布。但是，排序技术仍然限制了创建精细分辨率空间像元类型图的能力。为此，我们开发了一种基于深度学习的新型方法 STASCAN，通过整合基因表达谱和组织学图像的细胞特征学习来预测只有组织学图像可用的捕获或未知区域的空间细胞分布。STASCAN 已成功应用于来自不同空间转录组学技术的不同数据集，并在破译更高分辨率的细胞分布和解析增强的组织结构方面显示出显着优势。