QuantileNorm.normalize: QuantileNorm.normalize

In saeyslab/CytoNorm: Normalisation of cytometry data measured across multiple batches

QuantileNorm.normalize

Description

Normalize data, given the batch effects learned from control samples per cell type/cluster (output from QuantileNorm.train). New fcs files are written to the given output directory.

Usage

QuantileNorm.normalize(
  model,
  files,
  labels,
  transformList,
  transformList.reverse,
  outputDir = ".",
  prefix = "Norm_",
  removeOriginal = FALSE,
  verbose = FALSE,
  ...
)

Arguments

model	Model of the batch effercts, as computed by QuantileNorm.train
files	Full paths of to the fcs files of the samples to normalize
labels	A label for every file, indicating to which batch it belongs, e.g. the plate ID.
transformList	Transformation list to pass to the flowCore transform function
transformList.reverse	Transformation list with the reverse functions, so the normalized files can be saved in the untransformed space
outputDir	Directory to put the temporary files in. Default = "."
prefix	Prefix to put in front of the normalized file names. Default = "Norm_"
removeOriginal	Should the original fcs be removed? Default = FALSE.
verbose	If TRUE, progress updates are printed. Default = FALSE.
...	Additional arguments to pass to read.FCS

Value

Nothing is returned, but the new FCS files are written to the output directory

See Also

QuantileNorm.train

Examples

dir <- system.file("extdata", package = "CytoNorm")
files <- list.files(dir, pattern = "fcs$")
data <- data.frame(File = files,
                   Path = file.path(dir, files),
                   Type = stringr::str_match(files, "_([12]).fcs")[,2],
                   Batch = stringr::str_match(files, "PTLG[0-9]*")[,1],
                   stringsAsFactors = FALSE)
data$Type <- c("1" = "Train", "2" = "Validation")[data$Type]
train_data <- dplyr::filter(data, Type == "Train")
validation_data <- dplyr::filter(data, Type == "Validation")

ff <- flowCore::read.FCS(data$Path[1])
channels <- grep("Di$", flowCore::colnames(ff), value = TRUE)
transformList <- flowCore::transformList(channels,
                                         cytofTransform)
transformList.reverse <- flowCore::transformList(channels,
                                                 cytofTransform.reverse)

model_nQ_99 <- QuantileNorm.train(
  files = train_data$Path,
  labels = train_data$Batch,
  channels = channels,
  transformList = transformList,
  nQ = 99)

QuantileNorm.normalize(model_nQ_99,
                       validation_data$Path,
                       validation_data$Batch,
                       transformList = transformList,
                       transformList.reverse = transformList.reverse)

saeyslab/CytoNorm documentation built on Nov. 2, 2024, 12:39 p.m.