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Spatial studies of transcriptome provide biologists with gene expression mapsof heterogeneous and complex tissues. However, most experimental protocols forspatial transcriptomics suffer from the need to select beforehand a smallfraction of genes to be quantified over the entire transcriptome. Standardsingle-cell RNA sequencing (scRNA-seq) is more prevalent, easier to implementand can in principle capture any gene but cannot recover the spatial locationof the cells. In this manuscript, we focus on the problem of imputation ofmissing genes in spatial transcriptomic data based on (unpaired) standardscRNA-seq data from the same biological tissue. Building upon domain adaptationwork, we propose gimVI, a deep generative model for the integration of spatialtranscriptomic data and scRNA-seq data that can be used to impute missinggenes. After describing our generative model and an inference procedure for it,we compare gimVI to alternative methods from computational biology or domainadaptation on real datasets and outperform Seurat Anchors, Liger and CORAL toimpute held-out genes.

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