A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

Understand

Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous and complex tissues.

• However, most experimental protocols for spatial transcriptomics suffer from the need to select beforehand a small fraction of genes to be quantified over the entire transcriptome.
• Standard single-cell RNA sequencing (scRNA-seq) is more prevalent, easier to implement and can in principle capture any gene but cannot recover the spatial location of the cells.
• In this manuscript, we focus on the problem of imputation of missing genes in spatial transcriptomic data based on (unpaired) standard scRNA-seq data from the same biological tissue.