FlowSOM (version 1.4.0)

ReadInput: 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(), scale = FALSE, scaled.center = TRUE, scaled.scale = TRUE, silent = FALSE)

Arguments

input

a flowFrame, a flowSet 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. If NULL and transform=TRUE, column names of $SPILL description in fcs file will be used.

transformFunction

Defaults to logicleTransform()

scale

logical, does the data needs to be rescaled

scaled.center

see scale

scaled.scale

see scale

silent

if TRUE, no progress updates will be printed

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","lymphocytes.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,ff@description$SPILL)
ff <- flowCore::transform(ff,
                 flowCore::transformList(colnames(ff@description$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]]