madEvaluation: madEvaluation

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

madEvaluation

Description

Evaluate whether you lose biological information by checking whether the MAD stays similar before and after normalization.

Usage

madEvaluation(
  files,
  channels,
  transformList = NULL,
  prefix = "^Norm_",
  manual = NULL,
  return_all = FALSE
)

Arguments

files	Full paths of to the fcs files of the control samples.
channels	Channels to evaluate (corresponding with the column names of the flow frame)
transformList	Transformation list to pass to the flowCore transform function. Default NULL
prefix	Prefix present in the files, which will be removed to match the manual list.
manual	A list which contains for every file a factor array. These arrays contain a cell label for every cell in the files. All arrays should have the same levels. Default = NULL, all cells are evaluated together.
return_all	If TRUE, individual MAD values are returned as well. Default = FALSE.

Value

A matrix in which the rows represent the cell types, the columns reprents the markers and the values represent the median MAD values for the distributions of all files

Examples

   # Describe file names
   dir <- system.file("extdata",package="CytoNorm")
   fileNames <- c("Gates_PTLG021_Unstim_Control_1.fcs",
                   "Gates_PTLG028_Unstim_Control_1.fcs")
   labels <- c("PTLG021","PTLG028")
   ff <- flowCore::read.FCS(file.path(dir,fileNames[1]))
   channelsToNormalize <- flowCore::colnames(ff)[c(10, 11, 14, 16:35, 37, 39:51)]

   # Build transform list
   transformList <- flowCore::transformList(channelsToNormalize,
                                            cytofTransform)
   res <- madEvaluation(file.path(dir,fileNames),
                        transformList = transformList,
                        channelsToNormalize)

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