CytoNorm.normalize: normalizeClustered

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

normalizeClustered

Description

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

Usage

CytoNorm.normalize(
  model,
  files,
  labels,
  transformList,
  transformList.reverse,
  outputDir = ".",
  prefix = "Norm_",
  clean = TRUE,
  verbose = FALSE,
  normMethod.normalize = QuantileNorm.normalize,
  write = TRUE,
  ...
)

Arguments

model	Model of the batch effercts, as computed by CytoNorm.train
files	Full paths of the fcs files or a flowSet of the samples.
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_"
clean	If FALSE, temporary files describing the FlowSOM clusters seperately are not removed at the end. Default = TRUE.
verbose	If TRUE, progress updates are printed. Default = FALSE.
normMethod.normalize	Normalization method to use.
write	logical indicating whether the normalised samples should be written to new FCS files in a Normalized directory within outputDir, set to TRUE by default.
...	Additional arguments to pass to read.FCS

Value

a flowSet containing the normalised samples and optionally write FCS files to Normalized directory in outputDir.

See Also

CytoNorm.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 <- CytoNorm.train(files = train_data$Path,
                        labels = train_data$Batch,
                        channels = channels,
                        transformList = transformList,
                        FlowSOM.params = list(nCells = 10000, #1000000
                                              xdim = 15,
                                              ydim = 15,
                                              nClus = 10,
                                              scale = FALSE),
                        normParams = list(nQ = 101),
                        seed = 1,
                        verbose = TRUE)

CytoNorm.normalize(model = model,
                   files = validation_data$Path,
                   labels = validation_data$Batch,
                   transformList = transformList,
                   transformList.reverse = transformList.reverse,
                   verbose = TRUE)

saeyslab/CytoNorm documentation built on March 19, 2024, 6:43 p.m.