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 clone.

### Usage

    glad.profileCGH(profileCGH, mediancenter=FALSE,
                    smoothfunc="lawsglad", bandwidth=10, round=1.5,
                    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, ...)

### Arguments

`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 `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
`...` |

### 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., 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.

### Value

`` | An object of class "profileCGH" with the following attributes:
`profileValues:` | a data.frame with the following added information: Smoothing The smoothing values correspond to the median of each MSHR (i.e. `Region`). 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 or 1 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 clone : Gain is coded by 1, Loss by -1 and Normal by 0.
`BkpInfo:` | the data.frame attribute `BkpInfo` which gives the list of breakpoints: PosOrder The rank position of each clone on the genome. PosBase The base position of each clone on the genome. Chromosome Chromosome name.
`SigmaC:` | the data.frame attribute `SigmaC` gives the estimation of the LogRatio standard-deviation for each chromosome: Chromosome Chromosome name. Value The estimation is based on the Inter Quartile Range.

### Note

People interested in tools dealing with array CGH analysis can visit our web-page http://bioinfo.curie.fr.

### Author(s)

Philippe Hupé, glad@curie.fr.

### See Also

`profileCGH`, `as.profileCGH`, `plotProfile`.

### Examples

    data(snijders)

    ### Creation of "profileCGH" object
    gm13330$Clone <- gm13330$BAC
    profileCGH <- as.profileCGH(gm13330)

    ###########################################################
    ###
    ###  glad function as described in Hupé et al. (2004)
    ###
    ###########################################################

    res <- glad(profileCGH, mediancenter=FALSE,
                    smoothfunc="lawsglad", bandwidth=10, round=1.5,
                    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",
    main="Breakpoints detection: GLAD analysis")

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

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

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

[Package GLAD version 1.8.0 Index]