{
  "_id": "6a1acd3f1d7bb097a09dee51",
  "Package": "goSTAG",
  "Type": "Package",
  "Title": "A tool to use GO Subtrees to Tag and Annotate Genes within a set",
  "Version": "1.37.0",
  "Date": "2017-06-02",
  "Author": "Brian D. Bennett and Pierre R. Bushel",
  "Maintainer": "Brian D. Bennett <brian.bennett@nih.gov>",
  "Description": "Gene lists derived from the results of genomic analyses\nare rich in biological information. For instance,\ndifferentially expressed genes (DEGs) from a microarray or\nRNA-Seq analysis are related functionally in terms of their\nresponse to a treatment or condition. Gene lists can vary in\nsize, up to several thousand genes, depending on the robustness\nof the perturbations or how widely different the conditions are\nbiologically. Having a way to associate biological relatedness\nbetween hundreds and thousands of genes systematically is\nimpractical by manually curating the annotation and function of\neach gene. Over-representation analysis (ORA) of genes was\ndeveloped to identify biological themes. Given a Gene Ontology\n(GO) and an annotation of genes that indicate the categories\neach one fits into, significance of the over-representation of\nthe genes within the ontological categories is determined by a\nFisher's exact test or modeling according to a hypergeometric\ndistribution. Comparing a small number of enriched biological\ncategories for a few samples is manageable using Venn diagrams\nor other means for assessing overlaps. However, with hundreds\nof enriched categories and many samples, the comparisons are\nlaborious. Furthermore, if there are enriched categories that\nare shared between samples, trying to represent a common theme\nacross them is highly subjective. goSTAG uses GO subtrees to\ntag and annotate genes within a set. goSTAG visualizes the\nsimilarities between the over-representation of DEGs by\nclustering the p-values from the enrichment statistical tests\nand labels clusters with the GO term that has the most paths to\nthe root within the subtree generated from all the GO terms in\nthe cluster.",
  "License": "GPL-3",
  "VignetteBuilder": "knitr",
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  "biocViews": "GeneExpression, DifferentialExpression, GeneSetEnrichment,\nClustering, Microarray, mRNAMicroarray, RNASeq, Visualization,\nGO, ImmunoOncology",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:45:00 UTC",
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      "tojson": true
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      "title": "Rat Cancer Therapeutics Gene Lists",
      "object": "rat_cancer_therapeutics_gene_lists",
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      ],
      "fields": [],
      "table": false,
      "tojson": true
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      "title": "goSTAG Package",
      "topics": [
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        "goSTAG"
      ]
    },
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      "title": "Annotate Clusters",
      "topics": [
        "annotateClusters"
      ]
    },
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      "title": "goSTAG Example Gene Lists",
      "topics": [
        "goSTAG_example_gene_lists"
      ]
    },
    {
      "page": "goSTAG_go_genes_human",
      "title": "goSTAG GO Genes (Human)",
      "topics": [
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      ]
    },
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      "title": "goSTAG GO Genes (Mouse)",
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