Produce a "nacho" object from RCC NanoString files

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 = NULL,
  normalisation_method = "GEO",
  background = "none",
  background_mode = "threshold",
  ruv_k = NULL,
  instrument = NULL,
  preset = "nsolver",
  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. NACHO computes the columns Date, ID, BD, ScannerID, StagePosition, CartridgeID, FoV, PCL, LoD, MC, MedC, Positive_factor, Negative_factor, Detection_rate, Background, House_factor, Housekeeping_detected, Ligation_order, Ligation_R2, Ligation_NEG, Haemolysis, is_outlier, PC1, PC2, ... and W_1, W_2, .... A sample sheet column with one of these names is replaced, with a warning.
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. Prediction picks the five most stable genes by geNorm on the raw counts. It is skipped for the miRNA methods of normalisation_method.
housekeeping_norm: [logical] Boolean to indicate whether the housekeeping normalisation should be performed. NULL (the default) normalises with housekeeping genes for mRNA panels that have them. On miRNA panels, whose housekeeping mRNAs sit at background, it does so only when you pass housekeeping_genes or set housekeeping_predict = TRUE. Pass TRUE or FALSE to override this. The miRNA methods of normalisation_method ignore it.
normalisation_method: [character] "GEO" (the default) or "GLM" scale samples by their positive controls, with the geometric mean or a Poisson model, then by the housekeeping genes; "RUVg" scales by the positive controls with the geometric mean, then removes ruv_k factors of unwanted variation estimated from the housekeeping genes, which nacho_samples() returns as W_1, W_2, ... for use as covariates. For miRNA panels, "stable_mirna", "total_mirna", "spike_in" and "ligation" scale by the five most stable miRNAs, the miRNAs above 50 counts, the spike-in controls or the ligation positive controls, in the order Bruker recommends for plasma and serum. These methods ignore housekeeping_genes, housekeeping_predict and housekeeping_norm, and give their scaling factor as House_factor. When a GLM fit fails, NACHO warns and uses the geometric mean instead, as normalise() describes.
background: [character] How to estimate each sample's background from its negative controls: "none" (the default), "mean", "mean_2sd" (mean plus two standard deviations), "median", "max" or "geo" (geometric mean).
background_mode: [character] What to do with that background: "threshold" (the default) raises lower counts to it, which Bruker recommends when fold changes matter, and "subtract" removes it and floors at 0.
ruv_k: [numeric] The number of unwanted factors RUVg removes; NULL uses suggest_ruv_k(). A ruv_k above two less than the number of samples, or one less than the number of control genes, is lowered with a nacho_warning_ruv_k_reduced warning. Other methods ignore it.
instrument: [character] The nCounter instrument, "max", "flex", "pro" or "sprint", which sets the binding density limits. NULL (the default) reads it from the RCC files when they say, and otherwise uses the MAX/FLEX limits with a warning.
preset: [character] "nsolver" (the default) or "legacy"; see nacho_thresholds().
n_comp: [numeric] Number indicating the number of principal components to compute. Cannot be more than n-1 samples. Default is 10.

Value

A nacho object; read its content with nacho_counts(), nacho_samples(), nacho_probes() and nacho_qc().

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"
  )
}