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. 



Data: Included in this repository are raw counts uniquely aligned to the maternal (CAST) and paternal (129) alleles for each sample, RCTD objects, and an example visualization of the first hippocampus sample. Note that in this visualization, beads that are not confident singlets are included – for more accurate cell type visualizations, please see the manuscript or plot from the RCTD objects manually.


See the source code and analysis reproducibility details here: https://github.com/lulizou/spASE . For raw data please see https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE268519 .