Detection of allele-specific expression in spatial transcriptomics with spASE
Luli S. Zou, Dylan Cable, Irving Barrera, Tongtong Zhao, Evan Murray, Martin J. Aryee, Fei Chen, Rafael A. Irizarry
Abstract
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.