\name{clusterAlignment}

\alias{clusterAlignment}
\alias{clusterAlignment-show}
\alias{clusterAlignment-class}
\alias{clusterAlignment-plot}
\alias{show,clusterAlignment-method}
\alias{plot,clusterAlignment-method}

\title{Data Structure for a collection of all pairwise alignments of GCMS runs}

\description{Store the raw data and optionally, information regarding signal peaks for a number of GCMS runs}
	
\usage{clusterAlignment(pD,runs=1:length(pD@rawdata),timedf=NULL,usePeaks=TRUE,verbose=TRUE,...)}

\arguments{

\item{pD}{a \code{peaksDataset} object.}

\item{runs}{vector of integers giving the samples to calculate set of pairwise alignments over.}
 
\item{timedf}{list (length = the number of pairwise alignments) of matrices giving the expected time differences expected at each pair of peaks (used with \code{usePeaks}=\code{TRUE}, passed to \code{peaksAlignment}}

\item{usePeaks}{logical, \code{TRUE} uses \code{peakdata} list, \code{FALSE} uses \code{rawdata} list for computing similarity.}

\item{verbose}{logical, whether to print out info.}

\item{...}{other arguments passed to \code{peaksAlignment}}

}


\details{

clusterAlignment computes the set of pairwise alignments.

}

\value{

\code{clusterAlignment} object

}


\author{Mark Robinson}


\references{

Mark D Robinson (2008).  Methods for the analysis of gas chromatography - mass spectrometry data \emph{PhD dissertation} University of Melbourne.

}


\seealso{
\code{\link{peaksDataset}}, \code{\link{peaksAlignment}}
}

\examples{
require(gcspikelite)

# paths and files
gcmsPath<-paste(.find.package("gcspikelite"),"data",sep="/")
cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
eluFiles<-dir(gcmsPath,"ELU",full=TRUE)

# read data, peak detection results
pd<-peaksDataset(cdfFiles[1:2],mz=seq(50,550),rtrange=c(7.5,8.5))
pd<-addAMDISPeaks(pd,eluFiles[1:2])

ca<-clusterAlignment(pd, gap = .5,D=.05,df=30)
}

\keyword{classes}