\inputencoding{latin1}
\HeaderA{daglad}{Analysis of array CGH data}{daglad}
\methaliasA{daglad.profileCGH}{daglad}{daglad.profileCGH}
\begin{Description}\relax
This function allows the detection of breakpoints in genomic profiles
obtained by array CGH technology and affects a status (gain, normal
or lost) to each clone.
\end{Description}
\begin{Usage}
\begin{verbatim}

daglad.profileCGH(profileCGH, mediancenter=FALSE, normalrefcenter=FALSE, genomestep=FALSE,
                  smoothfunc="lawsglad", lkern="Exponential", model="Gaussian",
                  qlambda=0.999,  bandwidth=10, sigma=NULL, base=FALSE, round=1.5,
                  lambdabreak=8, lambdaclusterGen=40, param=c(d=6), alpha=0.001, msize=5,
                  method="centroid", nmin=1, nmax=8,
                  amplicon=1, deletion=-5, deltaN=0.10,  forceGL=c(-0.15,0.15), nbsigma=3,
                  MinBkpWeight=0.35, CheckBkpPos=TRUE,
                  verbose=FALSE, ...)

\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{profileCGH}] Object of class \code{\LinkA{profileCGH}{profileCGH}}
\item[\code{mediancenter}] If \code{TRUE}, LogRatio are center on their median.
\item[\code{genomestep}] If \code{TRUE}, a smoothing step over the whole
genome is performed and a "clustering throughout the genome" allows
to identify a cluster corresponding to the Normal DNA level. The threshold used in the \code{daglad}
function (\code{deltaN, forceGL, amplicon, deletion}) and then
compared to the median of this cluster.
\item[\code{normalrefcenter}] If \code{TRUE}, the LogRatio are centered
through the median of the cluster identified during the \code{genomestep}.
\item[\code{smoothfunc}] Type of algorithm used to smooth \code{LogRatio} by a
piecewise constant function. Choose either \code{\LinkA{aws}{aws}} or
\code{\LinkA{laws}{laws}}.
\item[\code{lkern}] lkern determines the location kernel to be used
(see \code{\LinkA{laws}{laws}} for details).
\item[\code{model}] model determines the distribution type of LogRatio
(see \code{\LinkA{laws}{laws}} for details).
\item[\code{qlambda}] qlambda determines the scale parameter qlambda for the
stochastic penalty (see \code{\LinkA{laws}{laws}} for details).
\item[\code{base}] If TRUE, the position of clone is the physical position onto
the chromosome, otherwise the rank position is used.
\item[\code{sigma}] Value to be passed to either argument \code{sigma2}   
of \code{\LinkA{aws}{aws}} function or \code{shape} of
\code{\LinkA{laws}{laws}}. If \code{NULL}, sigma is calculated from
the data.
\item[\code{bandwidth}] Set the maximal bandwidth \code{hmax} in the
\code{\LinkA{aws}{aws}} or  \code{\LinkA{laws}{laws}} function. For
example, if \code{bandwidth=10} then the \code{hmax} value is set
to 10*\eqn{X_N}{} where \eqn{X_N}{} is the position of the last clone.
\item[\code{round}] The smoothing results of either \code{\LinkA{aws}{aws}}
or \code{\LinkA{laws}{laws}} function are rounded or not depending on
the \code{round} argument. The \code{round} value is passed to the
argument \code{digits} of the \code{\LinkA{round}{round}} function.
\item[\code{lambdabreak}] Penalty term (\eqn{\lambda'}{}) used during the 
"Optimization of the number of breakpoints" step.
\item[\code{lambdaclusterGen}] Penalty term (\eqn{\lambda*}{}) used during the "clustering throughout the genome" step.
\item[\code{param}] Parameter of kernel used in the penalty term.
\item[\code{alpha}] Risk alpha used for the "Outlier detection" step.
\item[\code{msize}] The outliers MAD are calculated on regions with a
cardinality greater or equal to msize.
\item[\code{method}] The agglomeration method to be used during the "clustering throughout the genome" steps.
\item[\code{nmin}] Minimum number of clusters (N*max) allowed during
the "clustering throughout the genome" clustering step.
\item[\code{nmax}] Maximum number of clusters (N*max) allowed during
the "clustering throughout the genome" clustering step.
\item[\code{amplicon}] Level (and outliers) with a smoothing value (log-ratio
value) greater than this threshold are consider as amplicon. Note
that first, the data are centered on the normal reference value
computed during the "clustering throughout the genome" step.
\item[\code{deletion}] Level (and outliers) with a smoothing value (log-ratio
value) lower than this threshold are consider as deletion. Note
that first, the data are centered on the normal reference value
computed during the "clustering throughout the genome" step.
\item[\code{deltaN}] Region with smoothing values in between the interval
[-deltaN,+deltaN] are supposed to be normal.
\item[\code{forceGL}] Level with smoothing value greater (lower) than
\code{rangeGL[1]} (\code{rangeGL[2]}) are considered as gain
(lost). Note that first, the data are centered on the normal reference value
computed during the "clustering throughout the genome" step.
\item[\code{nbsigma}] For each breakpoints, a weight is calculated which is
a function of absolute value of the Gap between the smoothing values
of the two consecutive regions. Weight = 1-
kernelpen(abs(Gap),param=c(d=nbsigma*Sigma)) where Sigma is the
standard deviation of the LogRatio.
\item[\code{MinBkpWeight}] Breakpoints which \code{GNLchange}==0 and
\code{Weight} less than \code{MinBkpWeight} are discarded.
\item[\code{CheckBkpPos}] If \code{TRUE}, the accuracy position of each
breakpoints is checked.
\item[\code{verbose}] If \code{TRUE} some information are printed.
\item[\code{...}] 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The function \code{daglad} implements a slightly modified
version of the methodology described in the article : Analysis of array CGH data: from signal
ratio to gain and loss of DNA regions (Hupé et al., Bioinformatics 2004 20(18):3413-3422).
The \code{daglad} function allows to choose some threshold to help the algorithm to identify the status of the genomic regions. The threshodls are given in the following parameters:
\Itemize{
\item deltaN
\item forceGL
\item deletion
\item amplicon
}
\end{Details}
\begin{Value}
\begin{ldescription}
\item[\code{ }] An object of class "profileCGH" with the following
attributes:
\item[\code{\bsl{}bold\{profileValues\}}] a data.frame with the following added information:

\Itemize{

\item[\bold{Smoothing}] The smoothing values correspond to the median
of each Level


\item[\bold{Breakpoints}] The last position of a region with identical amount
of DNA is flagged by 1 otherwise it is 0. Note that during the
"Optimization of the number of breakpoints" step, removed
breakpoints are flagged by -1.


\item[\bold{Level}] Each position with equal smoothing value are labelled the
same way with an integer value starting from one. The label is
incremented by one when a new level occurs or when moving to the
next chromosome.

\item[\bold{OutliersAws}] Each AWS outliers are flagged by -1 (if it is
in the \eqn{\alpha/2}{} lower tail of the distribution) or 1 (if it is
in the \eqn{\alpha/2}{} upper tail of the distribution)
otherwise  it is 0.


\item[\bold{OutliersMad}] Each MAD outliers are flagged by -1 (if it is
in the \eqn{\alpha/2}{} lower tail of the distribution) or 1 (if it is
in the \eqn{\alpha/2}{} upper tail of the distribution)
otherwise  it is 0.

\item[\bold{OutliersTot}] OutliersAws + OutliersMad.


\item[\bold{NormalRef}] Clusters which have been used to set the normal
reference during the "clustering throughout the genome" step are
code by 0. Note that if \code{genomestep=FALSE}, all the value
are set to 0.

\item[\bold{ZoneGNL}] Status of each clone: Gain is coded by 1, Loss by -1,
Amplicon by 2, deletion by -10  and
Normal by 0.

}


\item[\code{\bsl{}bold\{BkpInfo\}}] a data.frame sum up the information for each
breakpoint:

\Itemize{
\item[\bold{Chromosome}] Chromosome name.
\item[\bold{Smoothing}] Smoothing value for the breakpoint.
\item[\bold{Gap}] absolute value of the gap between the smoothing values
of the two consecutive regions.
\item[\bold{Sigma}] The estimation of the standard-deviation of the  chromosome.
\item[\bold{Weight}] 1 - \code{kernelpen}(Gap, type, param=c(d=nbsigma*Sigma))
\item[\bold{ZoneGNL}] Status of the level where is the breakpoint.
\item[\bold{GNLchange}] Takes the value 1 if the ZoneGNL of the two
consecutive regions are different.
\item[\bold{LogRatio}] Test over Reference log-ratio.   
}



\item[\code{\bsl{}bold\{NormalRef\}}] If \code{genomestep=TRUE} and
\code{normalrefcenter=FALSE}, then NormalRef is the median of the cluster which has been used to set the normal
reference during the "clustering throughout the genome"
step. Otherwise NormalRef is 0.
\end{ldescription}
\end{Value}
\begin{Note}\relax
People interested in tools dealing with array CGH analysis can
visit our web-page \url{http://bioinfo.curie.fr}.
\end{Note}
\begin{Author}\relax
Philippe Hupé, \email{glad@curie.fr}.
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{glad}{glad}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}

data(snijders)
gm13330$Clone <- gm13330$BAC
profileCGH <- as.profileCGH(gm13330)

###########################################################
###
###  daglad function
###
###########################################################

res <- daglad(profileCGH, mediancenter=FALSE, normalrefcenter=FALSE, genomestep=FALSE,
              smoothfunc="lawsglad", lkern="Exponential", model="Gaussian",
              qlambda=0.999,  bandwidth=10, base=FALSE, round=1.5,
              lambdabreak=8, lambdaclusterGen=40, param=c(d=6), alpha=0.001, msize=5,
              method="centroid", nmin=1, nmax=8,
              amplicon=1, deletion=-5, deltaN=0.10,  forceGL=c(-0.15,0.15), nbsigma=3,
              MinBkpWeight=0.35, CheckBkpPos=TRUE)

### Genomic profile on the whole genome
plotProfile(res, unit=3, Bkp=TRUE, labels=FALSE, Smoothing="Smoothing",
main="Breakpoints detection: DAGLAD analysis")


###Genomic profile for chromosome 1
plotProfile(res, unit=3, Bkp=TRUE, labels=TRUE, Chromosome=1,
Smoothing="Smoothing", main="Chromosome 1: DAGLAD analysis")

### The standard-deviation of LogRatio are:
res$SigmaC

### The list of breakpoints is:
res$BkpInfo

\end{ExampleCode}
\end{Examples}

