NACHO (version 2.0.6)

normalise: (re)Normalise a "nacho" object

Description

This function creates a list in which your settings, the raw counts and normalised counts are stored, using the result from a call to load_rcc().

Usage

normalise(
  nacho_object,
  housekeeping_genes = nacho_object[["housekeeping_genes"]],
  housekeeping_predict = nacho_object[["housekeeping_predict"]],
  housekeeping_norm = nacho_object[["housekeeping_norm"]],
  normalisation_method = nacho_object[["normalisation_method"]],
  n_comp = nacho_object[["n_comp"]],
  remove_outliers = nacho_object[["remove_outliers"]],
  outliers_thresholds = nacho_object[["outliers_thresholds"]]
)

Value

[list] A list containing parameters and data.

access

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

housekeeping_genes

[character] Value passed to load_rcc() or normalise().

housekeeping_predict

[logical] Value passed to load_rcc().

housekeeping_norm

[logical] Value passed to load_rcc() or normalise().

normalisation_method

[character] Value passed to load_rcc() or normalise().

remove_outliers

[logical] Value passed to normalise().

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 quality-control thresholds used.

raw_counts

[data.frame] Raw counts with probes as rows and samples as columns. With "CodeClass" (first column), the type of the probes and "Name" (second column), the Name of the probes.

normalised_counts

[data.frame] Normalised counts with probes as rows and samples as columns. With "CodeClass" (first column)), the type of the probes and "Name" (second column), the name of the probes.

Arguments

nacho_object

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

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.

remove_outliers

[logical] A boolean to indicate if outliers should be excluded.

outliers_thresholds

[list] List of thresholds to exclude outliers.

Details

Outliers definition (remove_outliers = TRUE):

• Binding Density (BD) < 0.1

• Binding Density (BD) > 2.25

• Field of View (FoV) < 75

• Positive Control Linearity (PCL) < 0.95

• Limit of Detection (LoD) < 2

• Positive normalisation factor (Positive_factor) < 0.25

• Positive normalisation factor (Positive_factor) > 4

• Housekeeping normalisation factor (house_factor) < 1/11

• Housekeeping normalisation factor (house_factor) > 11

Examples

data(GSE74821)
GSE74821_norm <- normalise(
  nacho_object = GSE74821,
  housekeeping_norm = TRUE,
  normalisation_method = "GEO",
  remove_outliers = TRUE
)

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"
  )

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

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