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  "Description": "Intuitive framework for identifying spatially variable\ngenes (SVGs) and differential spatial variable pattern (DSP)\nbetween conditions via edgeR, a popular method for performing\ndifferential expression analyses. Based on pre-annotated\nspatial clusters as summarized spatial information, DESpace\nmodels gene expression using a negative binomial (NB), via\nedgeR, with spatial clusters as covariates. SVGs are then\nidentified by testing the significance of spatial clusters. For\nmulti-sample, multi-condition datasets, we again fit a NB model\nvia edgeR, incorporating spatial clusters, conditions and their\ninteractions as covariates. DSP genes-representing differences\nin spatial gene expression patterns across experimental\nconditions-are identified by testing the interaction between\nspatial clusters and conditions. See Cai et al. (2024)\n<doi:10.1093/bioinformatics/btae027> and Cai et al. (2026)\n<doi:10.1093/bioinformatics/btag450>.",
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