glad {GLAD}R Documentation

Analysis of array CGH data

Description

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 BAC.

Usage

glad.profileCGH(profileCGH, smoothfunc="aws", base=FALSE, sigma,
                   bandwidth=10, round=2, lambdabreak=8, lambdacluster=8,
                   lambdaclusterGen=40, type="tricubic",
                   param=c(d=6),alpha=0.001, method="centroid", nmax=8, verbose=FALSE, ...)

Arguments

profileCGH Object of class profileCGH
smoothfunc Type of algorithm used to smooth LogRatio by a piecewise constant function. Choose either aws or laws.
base If TRUE, the position of BAC 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.
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 BAC.
round The smoothing results of either aws or laws function are rounded or not depending on the round argument. The round value is passed to the argument digits of the round function.
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.
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
... parameters to be passed to chrBreakpoints function. Typically, you will have to specify the following arguments : lkern="exponential", model="Gaussian", qlambda=0.999.

Details

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., 2004 submitted).

First, chrBreakpoints detects breakpoints and detectOutliers allows the detection of MAD outliers. Then, the number of breakpoints is optimized with removeBreakpoints. The two-step clustering ("MSHR clustering by chromosome" and the "HCSR clustering throughout the genome") is performed with findCluster. The function affectationGNL give a status to each BAC.

Value

Smoothing Smoothing results of either aws or laws function after being rounded or not depending on the round argument.
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.
Region Each position between two breakpoints are labelled the same way with an integer value starting from one. The label is incremented by one when a new breakpoints occurs or when moving to the next chromosome. The variable region is what we call MSHR.
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.
OutliersAws Each AWS outliers are flagged by -1 (if it is in the α/2 lower tail of the distribution) or 1 (if it is in the α/2 upper tail of the distribution) otherwise it is 0.
OutliersMad Each MAD outliers are flagged by -1 (if it is in the α/2 lower tail of the distribution) or 1 (if it is in the α/2 upper tail of the distribution) otherwise it is 0.
OutliersTot OutliersAws + OutliersMad.
ZoneChr Clusters identified after MSHR (i.e. Region) clustering by chromosome.
ZoneGen Clusters identified after HCSR clustering throughout the genome.
ZoneGNL Status of each BAC : Gain is coded by 1, Loss by -1 and Normal by 0.

Author(s)

Philippe Hupé, Philippe.Hupe@curie.fr.

See Also

chrBreakpoints, removeBreakpoints,detectOutliers, findCluster, affectationGNL.

Examples


data(snijders)
profileCGH <- list(profileValues=gm13330)
class(profileCGH) <- "profileCGH"

res <- glad(profileCGH, smoothfunc="laws", base=FALSE,
               bandwidth=10, round=2, lambdabreak=8, lambdacluster=8,
               lambdaclusterGen=40, alpha=0.001, method="centroid",
               nmax=8, lkern="exponential", model="Gaussian",
               qlambda=0.999)

# color code for region status

col <- rep("yellow",length(res$profileValues$PosOrder))
col[which(res$profileValues$ZoneGNL==-1)] <- "green"
col[which(res$profileValues$ZoneGNL==1)] <- "red"

# outliers

outliers <- rep(20,length(res$profileValues$PosOrder))
outliers[which(res$profileValues$OutliersTot!=0)] <- 13

plot(LogRatio ~ PosOrder, data=res$profileValues, col=col, pch=outliers)

# Limit between chromosomes

LimitChr <- unique(res$profileValues$LimitChr)+0.5
abline(v=LimitChr, col="grey", lty=2)

lines(res$profileValues$Smoothing ~ res$profileValues$PosOrder, col="black")

# Breakpoints identified

indexBP <- which(res$profileValues$Breakpoints==1)
BP <- res$profileValues$PosOrder[indexBP]+0.5
abline(v=BP, col="red", lty=2)


[Package GLAD version 1.0.1 Index]