glad {GLAD} | R Documentation |
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.
glad.profileCGH(profileCGH, mediancenter=FALSE, smoothfunc="lawsglad", bandwidth=10, round=2, model="Gaussian", lkern="Exponential", qlambda=0.999, base=FALSE, sigma, lambdabreak=8, lambdacluster=8, lambdaclusterGen=40, type="tricubic", param=c(d=6), alpha=0.001, msize=5, method="centroid", nmax=8, verbose=FALSE, ...)
profileCGH |
Object of class profileCGH |
mediancenter |
If TRUE , LogRatio are center on their median. |
smoothfunc |
Type of algorithm used to smooth LogRatio by a
piecewise constant function. Choose either lawsglad , aws or
laws . |
bandwidth |
Set the maximal bandwidth hmax in the
aws or laws function. For
example, if bandwidth=10 then the hmax value is set
to 10*X_N where X_N is the position of the last clone. |
round |
The smoothing results are rounded or not depending on
the round argument. The round value is passed to the
argument digits of the round function. |
model |
Determines the distribution type of the LogRatio. Keep
always the model as "Gaussian" (see aws or
laws ). |
lkern |
Determines the location kernel to be used (see aws or
laws ). |
qlambda |
Determines the scale parameter for the
stochastic penalty (see aws or
laws ) |
base |
If TRUE , the position of clone is the physical position onto
the chromosome, otherwise the rank position is used. |
sigma |
Value to be passed to either argument sigma2
of aws function or shape of
laws . If NULL , sigma is calculated from
the data. |
lambdabreak |
Penalty term (λ') used during the Optimization of the number of breakpoints step. |
lambdacluster |
Penalty term (λ*) used during the MSHR clustering by chromosome step. |
lambdaclusterGen |
Penalty term (λ*) used during the HCSR clustering throughout the genome step. |
type |
Type of kernel function used in the penalty term during the Optimization of the number of breakpoints step, the MSHR clustering by chromosome step and the HCSR clustering throughout the genome step. |
param |
Parameter of kernel used in the penalty term. |
alpha |
Risk alpha used for the Outlier detection step. |
msize |
The outliers MAD are calculated on regions with a cardinality greater or equal to msize. |
method |
The agglomeration method to be used during the MSHR clustering by chromosome and the HCSR clustering throughout the genome clustering steps. |
nmax |
Maximum number of clusters (N*max) allowed during the the MSHR clustering by chromosome and the HCSR clustering throughout the genome clustering steps. |
verbose |
If TRUE some information are printed |
... |
The function glad
implements the methodology which
is 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 principle of the GLAD algorithm: First, the detection of breakpoints is based on the estimation of a piecewise constant function with the Adaptive Weights Smoothing (AWS) procedure (Polzehl and Spokoiny, 2002). Thus, a procedure based on penalyzed maximum likelihood optimizes the number of breakpoints allows the undesirable breakpoints to be removed. Finally, based on the regions previously identified, a two-step unsupervised classification (MSHR clustering by chromosome and the HCSR clustering throughout the genome) with model selection criteria allows a status to be assigned for each region (gain, loss or normal).
Main parameters to be tuned:
qlambda | if you want the smoothing to fit some very local effect, choose a smaller qlambda . |
bandwidth | choose a bandwidth not to small otherwise you will have a lot of little discontinuities. |
lambdabreak | More the parameter is high more the number of undesirable breakpoints is high. |
lambdacluster | More the parameter is high more the regions within a chromosome are supposed to belong to the same cluster. |
lambdaclusterGen | More the parameter is high more the regions over the whole genome are supposed to belong to the same cluster.
|
|
An object of class "profileCGH" with the following attributes: |
profileValues: |
a data.frame with the following added information:
|
BkpInfo: |
the data.frame attribute BkpInfo which gives
the list of breakpoints:
|
SigmaC: |
the data.frame attribute SigmaC gives the estimation of the LogRatio standard-deviation for each chromosome:
|
People interested in tools dealing with array CGH analysis can visit our web-page http://bioinfo.curie.fr.
Philippe Hupé, glad@curie.fr.
profileCGH
, as.profileCGH
, plotProfile
.
data(snijders) ### Creation of "profileCGH" object profileCGH <- as.profileCGH(gm13330) ########################################################### ### ### glad function as described in Hupé et al. (2004) ### ########################################################### res <- glad(profileCGH, mediancenter=FALSE, smoothfunc="lawsglad", bandwidth=10, round=2, model="Gaussian", lkern="Exponential", qlambda=0.999, base=FALSE, lambdabreak=8, lambdacluster=8, lambdaclusterGen=40, type="tricubic", param=c(d=6), alpha=0.001, msize=5, method="centroid", nmax=8, verbose=FALSE) ### Genomic profile on the whole genome plotProfile(res, unit=3, Bkp=TRUE, labels=FALSE, Smoothing="Smoothing") ###Genomic profile for chromosome 1 plotProfile(res, unit=3, Bkp=TRUE, labels=TRUE, Chromosome=1, Smoothing="Smoothing") ### The standard-deviation of LogRatio are: res$SigmaC ### The list of breakpoints is: res$BkpInfo