{
  "_id": "6ab231653d719c1f8408a189",
  "Package": "POWSC",
  "Type": "Package",
  "Title": "Simulation, power evaluation, and sample size recommendation for\nsingle cell RNA-seq",
  "Version": "1.20.0",
  "Authors@R": "c(person(\"Kenong\",\"Su\", email =\"kenong.su@emory.edu\", role = c(\"aut\",\"cre\")),person(\"Hao\",\"Wu\", email =\"hao.wu@emory.edu\", role =\"aut\"))",
  "Description": "Determining the sample size for adequate power to detect\nstatistical significance is a crucial step at the design stage\nfor high-throughput experiments. Even though a number of\nmethods and tools are available for sample size calculation for\nmicroarray and RNA-seq in the context of differential\nexpression (DE), this topic in the field of single-cell RNA\nsequencing is understudied. Moreover, the unique data\ncharacteristics present in scRNA-seq such as sparsity and\nheterogeneity increase the challenge. We propose POWSC, a\nsimulation-based method, to provide power evaluation and sample\nsize recommendation for single-cell RNA sequencing DE analysis.\nPOWSC consists of a data simulator that creates realistic\nexpression data, and a power assessor that provides a\ncomprehensive evaluation and visualization of the power and\nsample size relationship.",
  "License": "GPL-2",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "biocViews": "DifferentialExpression, ImmunoOncology, SingleCell, Software",
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  "Repository": "Bioconductor 3.23",
  "Date/Publication": "2026-04-28 12:56:01 UTC",
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    "Date": "2026-09-22 07:35:21 UTC",
    "User": "root"
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  "Author": "Kenong Su [aut, cre],\nHao Wu [aut]",
  "Maintainer": "Kenong Su <kenong.su@emory.edu>",
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    "immunooncology",
    "singlecell",
    "software"
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    "type": "organization",
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    "description": "Software for the analysis and comprehension of high-throughput genomic data"
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    "plot_POWSC",
    "Power_Cont",
    "Power_Disc",
    "runDE",
    "runPOWSC",
    "Simulate2SCE",
    "SimulateMultiSCEs",
    "summary_POWSC"
  ],
  "_datasets": [
    {
      "name": "es_mef_sce",
      "title": "sample data for POWSC",
      "object": "es_mef_sce",
      "class": [
        "SingleCellExperiment"
      ],
      "fields": [],
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      "tojson": false
    },
    {
      "name": "sce",
      "title": "sample data for GSE67835",
      "object": "GSE67835_AB_S7_sce",
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      ],
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      "table": false,
      "tojson": false
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  "_help": [
    {
      "page": "es_mef_sce",
      "title": "sample data for POWSC",
      "topics": [
        "es_mef_sce"
      ]
    },
    {
      "page": "Est2Phase",
      "title": "Estimate characterized parameters for a given scRNA-seq data (SingleCellExperiment object or a count matrix).",
      "topics": [
        "Est2Phase"
      ]
    },
    {
      "page": "plot_POWSC",
      "title": "plot the result use visualization.",
      "topics": [
        "plot_POWSC"
      ]
    },
    {
      "page": "Power_Cont",
      "title": "Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.",
      "topics": [
        "Power_Cont"
      ]
    },
    {
      "page": "Power_Disc",
      "title": "Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.",
      "topics": [
        "Power_Disc"
      ]
    },
    {
      "page": "runDE",
      "title": "A wrapper function for calling DE genes. This contains two methods: MAST and SC2P",
      "topics": [
        "runDE"
      ]
    },
    {
      "page": "runMAST",
      "title": "Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.",
      "topics": [
        "runMAST"
      ]
    },
    {
      "page": "runPOWSC",
      "title": "Estimate characterized parameters for a given scRNA-seq data (SingleCellExperiment object or a count matrix).",
      "topics": [
        "runPOWSC"
      ]
    },
    {
      "page": "runSC2P",
      "title": "Run DE analysis by using SC2P. Here we output two result tables corresponding to two forms of DE genes.",
      "topics": [
        "runSC2P"
      ]
    },
    {
      "page": "sce",
      "title": "sample data for GSE67835",
      "topics": [
        "sce"
      ]
    },
    {
      "page": "Simulate2SCE",
      "title": "Simulate the data for two-group comparison; e.g., treatment v.s. control It simulates the DE changes in two forms corresponding two types of DE genes",
      "topics": [
        "Simulate2SCE"
      ]
    },
    {
      "page": "SimulateMultiSCEs",
      "title": "Simulate the data for multiple-group comparisons; e.g., different cell types in blood It simulates the DE changes in two forms corresponding two types of DE genes",
      "topics": [
        "SimulateMultiSCEs"
      ]
    },
    {
      "page": "summary_POWSC",
      "title": "summary of the result",
      "topics": [
        "summary_POWSC"
      ]
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