SpatialDEG: Identification of differentially expressed genes by leveraging spatial information in spatially resolved transcriptomic studies

Abstract

MotivationSpatially resolved transcriptomics (SRT) technologies have been developed to simultaneously profile gene expression while retaining physical information. To explore differentially expressed genes using SRT in the context of various conditions, statistical methods are needed to perform spatial differential expression analysis. ResultsWe propose that a new probabilistic framework, spatialDEG, can perform differential expression analysis by leveraging spatial information on gene expression with spatial information. SpatialDEG utilizes the average information algorithm and can be scalable to tens of thousands of genes. Comprehensive simulations demonstrated that spatialDEG can identify genes differentially expressed in tissues across different conditions with a controlled type-I error rate. We further applied spatialDEG to analyze datasets for human dorsolateral prefrontal cortex and mouse whole liver. AvailabilityThe R package spatialDEG can be downloaded from https://github.com/Shufeyangyi2015310117/spatialDEG.