Visualise quality-control metrics of a "nacho" object

This function allows to visualise results from load_rcc() or normalise() several quality-control metrics in an interactive shiny application, in which thresholds can be customised and exported.

Usage

visualise(nacho_object)

Arguments

nacho_object

[list] A list object of class "nacho" obtained from load_rcc() or normalise().

Examples

if (interactive()) {
  data(GSE74821)
  # Must be run in an interactive R session!
  visualise(GSE74821)
}

if (interactive()) {
  library(GEOquery)
  library(NACHO)

  # Import data from GEO
  gse <- GEOquery::getGEO(GEO = "GSE74821")
  targets <- Biobase::pData(Biobase::phenoData(gse[[1]]))
  GEOquery::getGEOSuppFiles(GEO = "GSE74821", baseDir = tempdir())
  utils::untar(
    tarfile = file.path(tempdir(), "GSE74821", "GSE74821_RAW.tar"),
    exdir = file.path(tempdir(), "GSE74821")
  )
  targets$IDFILE <- list.files(
    path = file.path(tempdir(), "GSE74821"),
    pattern = ".RCC.gz$"
  )
  targets[] <- lapply(X = targets, FUN = iconv, from = "latin1", to = "ASCII")
  utils::write.csv(
    x = targets,
    file = file.path(tempdir(), "GSE74821", "Samplesheet.csv")
  )

  # Read RCC files and format
  nacho <- load_rcc(
    data_directory = file.path(tempdir(), "GSE74821"),
    ssheet_csv = file.path(tempdir(), "GSE74821", "Samplesheet.csv"),
    id_colname = "IDFILE"
  )
  visualise(nacho)

  # (re)Normalise data by removing outliers
  nacho_norm <- normalise(
    nacho_object = nacho,
    remove_outliers = TRUE
  )
  visualise(nacho_norm)

  # (re)Normalise data with "GLM" method and removing outliers
  nacho_norm <- normalise(
    nacho_object = nacho,
    normalisation_method = "GLM",
    remove_outliers = TRUE
  )
  visualise(nacho_norm)
}