Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. Modeling spatial correlation allows us to borrow the cell-type composition information across locations, improving accuracy of deconvolution even with a mismatched scRNA-seq reference. CARD can also impute cell-type compositions and gene expression levels at unmeasured tissue locations to enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study and can perform deconvolution without an scRNA-seq reference. Applications to four datasets, including a pancreatic cancer dataset, identified multiple cell types and molecular markers with distinct spatial localization that define the progression, heterogeneity and compartmentalization of pancreatic cancer.

许多空间分辨的转录组学技术不具有单细胞分辨率，而是测量来自潜在异质细胞类型的细胞混合物中每个点的平均基因表达。在这里，我们介绍了一种反卷积方法，即基于条件自回归的反卷积 (CARD)，它将来自单细胞 RNA 测序 (scRNA-seq) 的细胞类型特异性表达信息与跨组织位置的细胞类型组成的相关性相结合。建模空间相关性允许我们跨位置借用细胞类型组成信息，即使在 scRNA-seq 参考不匹配的情况下也能提高反卷积的准确性。CARD 还可以估算未测量组织位置的细胞类型组成和基因表达水平，以构建精细的空间组织图，其分辨率任意高于原始研究中测量的分辨率，并且可以在没有 scRNA-seq 参考的情况下进行反卷积。对包括胰腺癌数据集在内的四个数据集的应用，确定了多种细胞类型和具有不同空间定位的分子标记，这些标记定义了胰腺癌的进展、异质性和区室化。