Skip to contents

Produce a "nacho" object from RCC NanoString files

Source: R/load_rcc.R
load_rcc.Rd

This function is used to preprocess the data from NanoString nCounter.

Usage

load_rcc(
  data_directory,
  ssheet_csv,
  id_colname = NULL,
  housekeeping_genes = NULL,
  housekeeping_predict = FALSE,
  housekeeping_norm = TRUE,
  normalisation_method = "GEO",
  n_comp = 10
)

Arguments

data_directory

[character] A character string of the directory where the data are stored.

ssheet_csv

[character] or [data.frame] Either a string with the name of the CSV of the samplesheet or the samplesheet as a data.frame. Should contain a column that matches the file names in the folder.

id_colname

[character] Character string of the column in ssheet_csv that matches the file names in data_directory.

housekeeping_genes

[character] A vector of names of the miRNAs/mRNAs that should be used as housekeeping genes. Default is NULL.

housekeeping_predict

[logical] Boolean to indicate whether the housekeeping genes should be predicted (TRUE) or not (FALSE). Default is FALSE.

housekeeping_norm

[logical] Boolean to indicate whether the housekeeping normalisation should be performed. Default is TRUE.

normalisation_method

[character] Either "GEO" or "GLM". Character string to indicate normalisation using the geometric mean ("GEO") or a generalized linear model ("GLM"). Default is "GEO".

n_comp

[numeric] Number indicating the number of principal components to compute. Cannot be more than n-1 samples. Default is 10.

Value

[list] A list object of class "nacho":

access

[character] Value passed to load_rcc() in id_colname.

housekeeping_genes

[character] Value passed to load_rcc().

housekeeping_predict

[logical] Value passed to load_rcc().

housekeeping_norm

[logical] Value passed to load_rcc().

normalisation_method

[character] Value passed to load_rcc().

remove_outliers

[logical] FALSE.

n_comp

[numeric] Value passed to load_rcc().

data_directory

[character] Value passed to load_rcc().

pc_sum

[data.frame] A data.frame with n_comp rows and four columns: "Standard deviation", "Proportion of Variance", "Cumulative Proportion" and "PC".

nacho

[data.frame] A data.frame with all columns from the sample sheet ssheet_csv and all computed columns, i.e., quality-control metrics and counts, with one sample per row.

outliers_thresholds

[list] A list of the (default) quality-control thresholds used.

Examples


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"
  )
}