SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution

Abstract

AbstractIntegrating single cell RNAseq (scRNAseq) and spatial transcriptomics (ST) data is still challenging especially when the spatial resolution is poor. For cellular resolution spatial mapping, we have developed deep learning-based SC2Spa to learn the intricate spatial mapping rules from the transcriptome to its location from ST data. Benchmarking tests show that SC2Spa uniquely recapitulates tissue architecture from scRNAseq. SC2Spa successfully mapped scRNAseq even to various low resolution Visium data. SC2Spa identified spatially variable genes and suggested negative regulatory relationships between genes. SC2Spa armored with deep learning provides a new way to map the transcriptome to its spatial location and perform subsequent analyses.