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, assignGNLOut=TRUE,
                      breaksFdrQ = 0.0001, haarStartLevel = 1, haarEndLevel = 5,
                      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 `lawsglad`, `haarseg`, `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.
`assignGNLOut` | If `FALSE` the status (gain/normal/loss) is not assigned for outliers.
`breaksFdrQ` | breaksFdrQ for HaarSeg algorithm.
`haarStartLevel` | haarStartLevel for HaarSeg algorithm.
`haarEndLevel` | for HaarSeg algorithm.
`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). For smoothing, it is possible to use either the AWS algorithm (Polzehl and Spokoiny, 2002) or the HaarSeg algorithm (Ben-Yaacov and Eldar, Bioinformatics, 2008). 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.

### References

Hupé et al. (Bioinformatics, 2004)
  Analysis of array CGH data: from signal ratio to gain and loss of DNA regions.
Polzehl and Spokoiny (WIAS-Preprint 787, 2002)
  Local likelihood modelling by adaptive weights smoothing.
Ben-Yaacov and Eldar (Bioinformatics, 2008)
  A fast and flexible method for the segmentation of aCGH data.

### 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 2.0.0 Index]