\name{normalizeWithinSamples} \alias{normalizeWithinSamples} \title{ Within-sample normalization for two-color data } \description{ Within-sample (between-channel) normalization for two-color DNA methylation microarray data. This function implements the control probe loess procedure described in Aryee et al., 2011 (PMID: 20858772). } \usage{ normalizeWithinSamples(dat, copy=TRUE, method = "loess", scale=c(0.99, 0.99), controlProbes = NULL, controlIndex = NULL, approx=TRUE, breaks=1000, verbose=FALSE) } \arguments{ \item{dat}{ a TilingFeatureSet } \item{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} \item{method}{ normalization method. "loess" or "none" } \item{scale}{ a numeric vector (x,y). The xth percentile of each sample is scaled to represent y\% methylation. The default c(0.99, 0.99) means probes in the 99\% percentile represent 99\% methylation. Set to NA for no scaling. } \item{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). } \item{controlIndex}{ a vector of non-CpG control probe indices } \item{approx}{ Bin probes by signal intensity when loess normalizing. Much faster when TRUE } \item{breaks}{ Number of bins to use when approx=TRUE } \item{verbose}{ boolean: Verbose output? } } \details{ This function is used by \code{\link{methp}} performs within-sample (between-channel) normalization. It is normally not used directly by the user. } \value{ a TilingFeatureSet } \author{ Martin Aryee , Rafael Irizarry } \examples{ # See normalizeBetweenSamples }