SpaceExpress enables differential spatial transcriptomics with natural coordinate systems

Abstract

Spatial transcriptomics (ST) technologies have enabled new explorations into the spatial organization of tissues and their functional implications. However, one of the most fundamental analyses - comparative analysis of spatial gene expression across phenotypes - remains a formidable challenge. We introduce SpaceExpress, a novel statistical tool for detecting phenotype-associated changes in spatial expression patterns. SpaceExpress employs a neural network to embed multiple ST samples in a common latent space, enabling robust cross-sample comparisons despite structural and technical variations. It then uses spline regression to test differential spatial expression of genes between conditions, identifying specific regions of the tissue where expression patterns diverge and quantifying the magnitude of those differences, with rigorous false discovery control and handling of multiple replicates per condition. It includes visualization tools to help interpret spatial pattern differences. We demonstrate the tools effectiveness on synthetic and real ST datasets, revealing mechanistic insights into behavior- and development-related neurogenomic changes in honey bees and mice. Our work extends the highly influential paradigm of differential gene expression analysis to spatial omics.