Identification of spatial expression trends in single-cell gene expression data

Methods for spatial gene expression analyses at single-cell resolution are becoming available, whereas computational strategies for spatial gene expression analyses are lacking. We present a computational method (trendsceek) based on marked point processes that identifies genes with significant spatial expression trends. Trendsceek identifies significant genes in spatial transcriptomics and sequential FISH data and also reveal significant gene expression gradients and hotspots in low-dimensional projections of dissociated single-cell RNA-seq data.