\name{preprocessSWAN}
\alias{preprocessSWAN}

\title{Subset-quantile Within Array Normalisation for Illumina Infinium
  HumanMethylation450 BeadChips}

\description{Subset-quantile Within Array Normalisation (SWAN) is a
  within array normalisation method for the Illumina Infinium
  HumanMethylation450 platform. It allows Infinium I and II type probes
  on a single array to be normalized together.} 
 
\usage{
  preprocessSWAN(rgSet, mSet = NULL)
}

\arguments{
  \item{rgSet}{An object of class \code{RGChannelSet}.}
  \item{mSet}{An optional object of class \code{MethylSet}.  If set to
  \code{NULL} preprocessSwan uses \code{preprocessRaw} on the
  \code{rgSet} argument.  In case \code{mSet} is supplied, make sure it
  is the result of preprocessing the \code{rgSet} argument.}
}

\details{The SWAN method has two parts. First, an average quantile
distribution is created using a subset of probes defined to be
biologically similar based on the number of CpGs underlying the probe
body. This is achieved by randomly selecting N Infinium I and II probes
that have 1, 2 and 3 underlying CpGs, where N is the minimum number of
probes in the 6 sets of Infinium I and II probes with 1, 2 or 3 probe
body CpGs. If no probes have previously been filtered out e.g. sex
chromosome probes, etc. N=11,303. This results in a pool of 3N Infinium
I and 3N Infinium II probes. The subset for each probe type is then
sorted by increasing intensity. The value of each of the 3N pairs of
observations is subsequently assigned to be the mean intensity of the
two probe types for that row or \dQuote{quantile}. This is the standard
quantile procedure. The intensities of the remaining probes are then
separately adjusted for each probe type using linear interpolation
between the subset probes.}

\value{an object of class \code{MethylSet}}

\author{Jovana Maksimovic\email{jovana.maksimovic@mcri.edu.au}}

\seealso{\code{\linkS4class{RGChannelSet}} and
\code{\linkS4class{MethylSet}} as well as
\code{\linkS4class{IlluminaMethylationManifest}}.}

\examples{
if (require(minfiData)) {
  dat <- preprocessRaw(RGsetEx)
  slot(name="preprocessMethod", dat)[1]
  datSwan <- preprocessSWAN(RGsetEx, mSet = dat)
  datIlmn <- preprocessIllumina(RGsetEx)
  slot(name="preprocessMethod", datIlmn)[1]
  datIlmnSwan <- preprocessSWAN(RGsetEx, mSet = datIlmn)
}
}