Detection of allele-specific expression in spatial transcriptomics with spASE

Spatial transcriptomics technologies permit the study of the spatial distribution of RNA at near-single-cell resolution genome-wide. However, the feasibility of studying spatial allele-specific expression (ASE) from these data remains uncharacterized. Here, we introduce spASE, a computational framework for detecting and estimating spatial ASE. To tackle the challenges presented by cell type mixtures and a low signal to noise ratio, we implement a hierarchical model involving additive mixtures of spatial smoothing splines. We apply our method to allele-resolved Visium and Slide-seq from the mouse cerebellum and hippocampus and report new insight into the landscape of spatial and cell type-specific ASE therein.空间转录组技术（Spatial transcriptomics）可实现在全基因组范围内以接近单细胞分辨率解析RNA的空间分布特征。然而，依托此类数据开展空间等位基因特异性表达（allele-specific expression, ASE）研究的可行性仍未得到系统阐明。本文提出spASE——一款用于检测与定量空间ASE的计算框架。针对细胞类型混杂与信噪比较低带来的研究挑战，我们构建了包含空间平滑样条加性混合的层级模型。我们将该方法应用于取自小鼠小脑与海马体的等位基因解析型Visium与Slide-seq数据集，并为该体系中空间及细胞类型特异性ASE的整体分布图谱提供了全新的认知。