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  "Type": "Package",
  "Title": "DESpace: a framework to discover spatially variable genes and\ndifferential spatial patterns across conditions",
  "Version": "2.5.0",
  "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.",
  "Authors@R": "c(\nperson(given = \"Peiying\",\nfamily = \"Cai\",\nrole = c(\"aut\", \"cre\"),\nemail = \"peiying.cai@uzh.ch\",\ncomment = c(ORCID = \"0009-0001-9229-2244\")),\nperson(given = \"Simone\",\nfamily = \"Tiberi\",\nrole = c(\"aut\"),\nemail = \"simone.tiberi@unibo.it\",\ncomment = c(ORCID = \"0000-0002-3054-9964\")))",
  "biocViews": "Spatial, SingleCell, RNASeq, Transcriptomics,\nGeneExpression, Sequencing,\nDifferentialExpression,StatisticalMethod, Visualization",
  "License": "GPL-3",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 13:00:19 UTC",
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      "buildurl": "https://github.com/r-universe/bioc/actions/runs/26558310055"
    }
  ]
}