{ "_id": "6a1afe941d7bb097a09fe887", "Package": "SVP", "Title": "Predicting cell states and their variability in single-cell or\nspatial omics data", "Version": "1.5.0", "Authors@R": "c(\nperson(\"Shuangbin\", \"Xu\", email = \"xshuangbin@163.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-3513-5362\")),\nperson(\"Guangchuang\", \"Yu\", email = \"guangchuangyu@gmail.com\", role = c(\"aut\", \"ctb\"), comment = c(ORCID = \"0000-0002-6485-8781\")))", "Description": "SVP uses the distance between cells and cells, features\nand features, cells and features in the space of MCA to build\nnearest neighbor graph, then uses random walk with restart\nalgorithm to calculate the activity score of gene sets (such as\ncell marker genes, kegg pathway, go ontology, gene modules,\ntranscription factor or miRNA target sets, reactome pathway,\n...), which is then further weighted using the hypergeometric\ntest results from the original expression matrix. 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