normalizeBetweenSamples {charm} | R Documentation |
Between-sample normalization for two-color DNA methylation microarray data.
normalizeBetweenSamples (dat, copy=TRUE, m="allQuantiles", untreated="none", enriched="none", controlProbes=NULL, controlIndex=NULL, excludeIndex=NULL, verbose=FALSE)
dat |
a TilingFeatureSet object |
copy |
Only relevant when using disk-backed objects. If TRUE a copy will be made leaving the original object (dat) unchanged. The input object will not be preserved if copy=FALSE |
m |
normalization method for log-ratios. "allQuantiles" for full quantile normalization, or "none" |
untreated |
normalization method for the untreated channel. "complete", "allQuantiles" or "none" |
enriched |
normalization method for the untreated channel. "sqn", "allQuantiles" or "none" |
controlProbes |
character string of the label assigned to non-CpG control probes in the annotation file (i.e. the container column of the .ndf file). |
controlIndex |
a vector of non-CpG control probe indices |
excludeIndex |
a vector indicating which pm probes to ignore when creating normalization target distributions. Can be a vector of probe indices or a boolean vector of length(pmindex(dat)). |
verbose |
boolean: Verbose output? |
This function is used by methp
performs between-sample normalization. It is normally not used directly by the user.
a TilingFeatureSet
Martin Aryee <aryee@jhu.edu>
if (require(charmData) & require(BSgenome.Hsapiens.UCSC.hg18)) { phenodataDir <- system.file("extdata", package="charmData") pd <- read.delim(file.path(phenodataDir, "phenodata.txt")) pd <- subset(pd, sampleID=="441_liver") dataDir <- system.file("data", package="charmData") setwd(dataDir) rawData <- readCharm(files=pd$filename, sampleKey=pd) # Correct spatial artifacts dat <- spatialAdjust(rawData) # Remove background signal dat <- bgAdjust(dat) # Find non-CpG control probes ctrlIdx <- getControlIndex(rawData, subject=Hsapiens) # Within-sample normalization dat <- normalizeWithinSamples(dat, controlIndex=ctrlIdx) # Within-sample normalization dat <- normalizeBetweenSamples(dat) }