ReadInput: Read FCS-files or flowFrames

In saeyslab/FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data

Read FCS-files or flowFrames

Description

Take some input and return FlowSOM object containing a matrix with the preprocessed data (compensated, transformed, scaled)

Usage

ReadInput(
  input,
  pattern = ".fcs",
  compensate = FALSE,
  spillover = NULL,
  transform = FALSE,
  toTransform = NULL,
  transformFunction = flowCore::logicleTransform(),
  transformList = NULL,
  scale = FALSE,
  toScale = NULL,
  scaled.center = TRUE,
  scaled.scale = TRUE,
  silent = FALSE,
  ...
)

Arguments

input	a flowFrame, a flowSet, a matrix with column names or an array of paths to files or directories
pattern	if input is an array of file- or directorynames, select only files containing pattern
compensate	logical, does the data need to be compensated
spillover	spillover matrix to compensate with If NULL and compensate = TRUE, we will look for $SPILL description in FCS file.
transform	logical, does the data need to be transformed
toTransform	column names or indices that need to be transformed. Will be ignored if transformList is given. If NULL and transform = TRUE, column names of $SPILL description in FCS file will be used.
transformFunction	Defaults to logicleTransform()
transformList	transformList to apply on the samples.
scale	logical, does the data needs to be rescaled
toScale	column names or indices that need to be scaled If NULL and scale = TRUE, all columns will be scaled.
scaled.center	see scale
scaled.scale	see scale
silent	if TRUE, no progress updates will be printed. Default = FALSE
...	Additional arguments for read.FCS function

Value

FlowSOM object containing the data, which can be used as input for the BuildSOM function

See Also

scale, BuildSOM

Examples

# Read from file
fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM")
flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE,
                         scale = TRUE)

# Or read from flowFrame object
ff <- flowCore::read.FCS(fileName)
ff <- flowCore::compensate(ff, flowCore::keyword(ff)[["SPILL"]])
ff <- flowCore::transform(ff,
         flowCore::transformList(colnames(flowCore::keyword(ff)[["SPILL"]]),
                                flowCore::logicleTransform()))
flowSOM.res <- ReadInput(ff, scale = TRUE)

# Build the self-organizing map and the minimal spanning tree
flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18))
flowSOM.res <- BuildMST(flowSOM.res)

# Apply metaclustering
metacl <- MetaClustering(flowSOM.res$map$codes,
                         "metaClustering_consensus", max = 10)

# Get metaclustering per cell
flowSOM.clustering <- metacl[flowSOM.res$map$mapping[, 1]]    
 

saeyslab/FlowSOM documentation built on July 6, 2024, 10:59 a.m.