Detection of differential spatial patterns in spatial omics data

Spatially resolved transcriptomics (SRT) is a recent technology that allows studying the spatial structure of gene expression. Several methods have been proposed to identify, from SRT data, genes with mRNA abundance that varies across the tissue, named spatially variable genes. However, at present, no method allows comparing spatial patterns between experimental conditions. Here we present an approach, based on a negative binomial model of gene-expression data and a likelihood ratio test, to identify genes that exhibit changes in the spatial expression structure. We designed simulation studies, where our framework exhibits strong performance, in terms of sensitivity, specificity and runtime. The method will be freely distributed within DESpace Bioconductor R package.