daglad {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

    ## S3 method for class 'profileCGH':
    daglad(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, ...)

### Arguments

`profileCGH` | Object of class `profileCGH`
`mediancenter` | If `TRUE`, LogRatio are center on their median.
`genomestep` | If `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 `daglad` function (`deltaN, forceGL, amplicon, deletion`) and then compared to the median of this cluster.
`normalrefcenter` | If `TRUE`, the LogRatio are centered through the median of the cluster identified during the `genomestep`.
`smoothfunc` | Type of algorithm used to smooth `LogRatio` by a piecewise constant function. Choose either `aws` or `laws`.
`lkern` | lkern determines the location kernel to be used (see `laws` for details).
`model` | model determines the distribution type of LogRatio (see `laws` for details).
`qlambda` | qlambda determines the scale parameter qlambda for the stochastic penalty (see `laws` for details).
`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.
`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 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.
`lambdaclusterGen` | Penalty term (λ*) used during the "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 "clustering throughout the genome" steps.
`nmin` | Minimum number of clusters (N*max) allowed during the "clustering throughout the genome" clustering step.
`nmax` | Maximum number of clusters (N*max) allowed during the "clustering throughout the genome" clustering step.
`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.
`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.
`deltaN` | Region with smoothing values in between the interval [-deltaN,+deltaN] are supposed to be normal.
`forceGL` | Level with smoothing value greater (lower) than `rangeGL[1]` (`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.
`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.
`MinBkpWeight` | Breakpoints which `GNLchange`==0 and `Weight` less than `MinBkpWeight` are discarded.
`CheckBkpPos` | If `TRUE`, the accuracy position of each breakpoints is checked.
`verbose` | If `TRUE` some information are printed.
`...` |

### Details

The function `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 `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:

- deltaN
- forceGL
- deletion
- amplicon

### 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 Level 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. 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. 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 `genomestep=FALSE`, all the value are set to 0. ZoneGNL Status of each clone: Gain is coded by 1, Loss by -1, Amplicon by 2, deletion by -10 and Normal by 0.
`BkpInfo` | a data.frame sum up the information for each breakpoint: Chromosome Chromosome name. Smoothing Smoothing value for the breakpoint. Gap absolute value of the gap between the smoothing values of the two consecutive regions. Sigma The estimation of the standard-deviation of the chromosome. Weight 1 - `kernelpen`(Gap, type, param=c(d=nbsigma*Sigma)) ZoneGNL Status of the level where is the breakpoint. GNLchange Takes the value 1 if the ZoneGNL of the two consecutive regions are different. LogRatio Test over Reference log-ratio.
`NormalRef` | If `genomestep=TRUE` and `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.

### 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

`glad`.

### Examples

    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

[Package GLAD version 1.16.0 Index]