{
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  "Package": "TCC",
  "Type": "Package",
  "Title": "TCC: Differential expression analysis for tag count data with\nrobust normalization strategies",
  "Version": "1.53.0",
  "Author": "Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji\nKadota",
  "Maintainer": "Jianqiang Sun <sun@bitdessin.dev>, Tomoaki Nishiyama\n<tomoakin@staff.kanazawa-u.ac.jp>",
  "Description": "This package provides a series of functions for performing\ndifferential expression analysis from RNA-seq count data using\nrobust normalization strategy (called DEGES). The basic idea of\nDEGES is that potential differentially expressed genes or\ntranscripts (DEGs) among compared samples should be removed\nbefore data normalization to obtain a well-ranked gene list\nwhere true DEGs are top-ranked and non-DEGs are bottom ranked.\nThis can be done by performing a multi-step normalization\nstrategy (called DEGES for DEG elimination strategy). A major\ncharacteristic of TCC is to provide the robust normalization\nmethods for several kinds of count data (two-group with or\nwithout replicates, multi-group/multi-factor, and so on) by\nvirtue of the use of combinations of functions in depended\npackages.",
  "License": "GPL-2",
  "Copyright": "Authors listed above",
  "biocViews": "ImmunoOncology, Sequencing, DifferentialExpression, RNASeq",
  "Config/pak/sysreqs": "zlib1g-dev",
  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:38:07 UTC",
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  "NeedsCompilation": "no",
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    "User": "root"
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  "MD5sum": "09f6cd459a1c37a6e7379fa391c2036c",
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    "author": "A Wokaty <andres.wokaty@sph.cuny.edu>",
    "committer": "A Wokaty <andres.wokaty@sph.cuny.edu>",
    "message": "bump x.y.z version to odd y following creation of RELEASE_3_23 branch\n",
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    "email": "tomoakin@staff.kanazawa-u.ac.jp",
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    "differentialexpression",
    "rnaseq"
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    "type": "organization",
    "name": "Bioconductor",
    "description": "Software for the analysis and comprehension of high-throughput genomic data"
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    "estimateDE",
    "filterLowCountGenes",
    "getNormalizedData",
    "getResult",
    "length",
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    "plot.TCC",
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    "ROKU",
    "show.TCC",
    "simulateReadCounts",
    "TCC",
    "WAD"
  ],
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        "matrix",
        "array"
      ],
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        "mock1",
        "mock2",
        "mock3",
        "hrcc1",
        "hrcc2",
        "hrcc3"
      ],
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      "table": true,
      "tojson": true
    },
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      "object": "hypoData",
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      "class": [
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        "array"
      ],
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        "G1_rep2",
        "G1_rep3",
        "G2_rep1",
        "G2_rep2",
        "G2_rep3"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
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      "name": "hypoData_mg",
      "title": "A simulation dataset for comparing three-group tag count data, focusing on RNA-seq",
      "object": "hypoData_mg",
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        "array"
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        "G1_rep2",
        "G1_rep3",
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        "G2_rep2",
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        "G3_rep1",
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        "G3_rep3"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "hypoData_ts",
      "title": "A sample microarray data for detecting tissue-specific patterns.",
      "object": "hypoData_ts",
      "file": "hypoData_ts.RData",
      "class": [
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        "array"
      ],
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        "tissue2",
        "tissue3",
        "tissue4",
        "tissue5",
        "tissue6",
        "tissue7",
        "tissue8",
        "tissue9",
        "tissue10"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
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      "title": "DNA microarray data set",
      "object": "nakai",
      "file": "nakai.RData",
      "class": [
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        "array"
      ],
      "fields": [
        "Liver_fed",
        "Liver_fed",
        "Liver_fed",
        "Liver_fed",
        "Liver_fasted",
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        "Liver_fasted",
        "Liver_fasted"
      ],
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      "table": true,
      "tojson": true
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  "_help": [
    {
      "page": "arab",
      "title": "Arabidopsis RNA-Seq data set",
      "topics": [
        "arab"
      ]
    },
    {
      "page": "calcAUCValue",
      "title": "Calculate AUC value from a TCC-class object",
      "topics": [
        "calcAUCValue"
      ]
    },
    {
      "page": "calcNormFactors",
      "title": "Calculate normalization factors",
      "topics": [
        "calcNormFactors",
        "calcNormFactors,DGEList-method",
        "calcNormFactors,TCC-method"
      ]
    },
    {
      "page": "clusterSample",
      "title": "Perform hierarchical clustering for samples from expression data",
      "topics": [
        "clusterSample"
      ]
    },
    {
      "page": "estimateDE",
      "title": "Estimate degrees of differential expression (DE) for individual genes",
      "topics": [
        "estimateDE"
      ]
    },
    {
      "page": "filterLowCountGenes",
      "title": "Filter genes from a TCC-class object",
      "topics": [
        "filterLowCountGenes"
      ]
    },
    {
      "page": "getNormalizedData",
      "title": "Obtain normalized count data",
      "topics": [
        "getNormalizedData"
      ]
    },
    {
      "page": "getResult",
      "title": "Obtain the summaries of results after the differential expression analysis",
      "topics": [
        "getResult"
      ]
    },
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      "title": "A simulation dataset for comparing two-group tag count data, focusing on RNA-seq",
      "topics": [
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    {
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      "title": "A simulation dataset for comparing three-group tag count data, focusing on RNA-seq",
      "topics": [
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      ]
    },
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      "title": "A sample microarray data for detecting tissue-specific patterns.",
      "topics": [
        "hypoData_ts"
      ]
    },
    {
      "page": "makeFCMatrix",
      "title": "Generate the fold change matrix for simulating count data",
      "topics": [
        "makeFCMatrix"
      ]
    },
    {
      "page": "nakai",
      "title": "DNA microarray data set",
      "topics": [
        "nakai"
      ]
    },
    {
      "page": "plot.TCC",
      "title": "Plot a log fold-change versus log average expression (so-called M-A plot)",
      "topics": [
        "plot",
        "plot.TCC"
      ]
    },
    {
      "page": "plotFCPseudocolor",
      "title": "Create a pseudo-color image of simulation data",
      "topics": [
        "plotFCPseudocolor"
      ]
    },
    {
      "page": "ROKU",
      "title": "detect tissue-specific (or tissue-selective) patterns from microarray data with many kinds of samples",
      "topics": [
        "ROKU"
      ]
    },
    {
      "page": "simulateReadCounts",
      "title": "Generate simulation data from negative binomial (NB) distribution",
      "topics": [
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      ]
    },
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      "page": "TCC",
      "title": "A package for differential expression analysis from tag count data with robust normalization strategies",
      "topics": [
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        "TCC"
      ]
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      "title": "A container for storing information used in TCC",
      "topics": [
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        "show",
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        "show.TCC",
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        "[,TCC,ANY,ANY-method",
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        "[,TCC-method"
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      "topics": [
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