norm.arrayCGH {MANOR} | R Documentation

## Normalize an object of type arrayCGH

### Description

Normalize an object of type `arrayCGH` using a list of criteria specified as (temporary or permanent) flags. If a replicate identifier (clone name) is provided, a single target value is computed for all the replicates.

The normalization coefficient is computed as a function, and is applied to all good quality spots of the array.

### Usage

      norm.arrayCGH(arrayCGH, flag.list=NULL, var="LogRatio", printTime=FALSE, FUN=median, ...)

### Arguments

`arrayCGH` | an object of type `arrayCGH`
`flag.list` | a list of objects of type flag
`var` | a variable name (from `arrayCGH$arrayValues`) from which normalization coefficient has to be computed; default is "LogRatio"
`printTime` | boolean value; if `TRUE`, the time taken by each step of the normalization process is displayed
`FUN` | an aggregation function (e.g. mean, median) to compute a normalization coefficient; default is median
`...` | further arguments to be passed to FUN

### Details

The two flag types are treated differently : - permanent flags detect poor quality spots, which are removed from further analysis - temporary flags detect good quality spots that would bias the normalization coefficient if they were not excluded from its computation, e.g. amplicons or sexual chromosomes. Thus they are not taken into account for the computation of the coefficient, but at the end of the analysis they are normalized as any good quality spots of the array.

The normalization coefficient is computed as a function (e.g. mean or median) of the target value (e.g. log-ratios); it is then applied to all good quality spots (including temporary flags), i.e. substracted from each target value.

If clone level information is available (i.e. if `arrayCGH$cloneValues` is not null), a normalized mean target value and basic statistics (such as the number of replicates per clone) are calculated for each clone.

### Value

an object of type `arrayCGH`

### Note

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

### Author(s)

Pierre Neuvial, manor@curie.fr.

### References

P. Neuvial, P. Hupé, I. Brito, S. Liva, E. Manié, C. Brennetot, A. Aurias, F. Radvanyi, and E. Barillot. Spatial normalization of array-CGH data. BMC Bioinformatics, 7(1):264. May 2006.

### See Also

`flag`

### Examples

    data(spatial)
    data(flags)

    ### 'edge': local spatial bias
    ## define a list of flags to be applied
    flag.list1 <- list(spatial=local.spatial.flag, spot=spot.corr.flag,
    ref.snr=ref.snr.flag, dapi.snr=dapi.snr.flag, rep=rep.flag,
    unique=unique.flag)
    flag.list1$spatial$args <- alist(var="ScaledLogRatio", by.var=NULL,
    nk=5, prop=0.25, thr=0.15, beta=1, family="symmetric")
    flag.list1$spot$args <- alist(var="SpotFlag")
    flag.list1$spot$char <- "O"
    flag.list1$spot$label <- "Image analysis"

    ## normalize arrayCGH
    ## Not run:
    edge.norm <- norm.arrayCGH(edge, flag.list=flag.list1,
    var="LogRatio", FUN=median, na.rm=TRUE)
    ## End(Not run)
    print(edge.norm$flags) ## spot-level flag summary (computed by flag.summary)

    ## aggregate arrayCGH without normalization
    edge.nonorm <- norm.arrayCGH(edge, flag.list=NULL, var="LogRatio",
    FUN=median, na.rm=TRUE)

    ## sort genomic informations
    edge.norm <- sort.arrayCGH(edge.norm, position.var="PosOrder")
    edge.nonorm <- sort.arrayCGH(edge.nonorm, position.var="PosOrder")

    ## plot genomic profiles
    layout(matrix(c(1,2,4,5,3,3,6,6), 4,2),width=c(1, 4), height=c(6,1,6,1))
    report.plot(edge.nonorm, chrLim="LimitChr", layout=FALSE,
    main="Pangenomic representation (before normalization)", zlim=c(-1,1),
    ylim=c(-3,1))
    report.plot(edge.norm, chrLim="LimitChr", layout=FALSE,
    main="Pangenomic representation (after normalization)", zlim=c(-1,1),
    ylim=c(-3,1))

    ### 'gradient': global array Trend
    ## define a list of flags to be applied
    flag.list2 <- list(
      spot=spot.flag, global.spatial=global.spatial.flag, SNR=SNR.flag,
      val.mark=val.mark.flag, position=position.flag, unique=unique.flag,
      amplicon=amplicon.flag, replicate=replicate.flag,
      chromosome=chromosome.flag)

    ## normalize arrayCGH
    ## Not run: gradient.norm <- norm.arrayCGH(gradient, flag.list=flag.list2, var="LogRatio", FUN=median, na.rm=TRUE)
    ## aggregate arrayCGH without normalization
    gradient.nonorm <- norm.arrayCGH(gradient, flag.list=NULL, var="LogRatio", FUN=median, na.rm=TRUE)

    ## sort genomic informations
    gradient.norm <- sort.arrayCGH(gradient.norm)
    gradient.nonorm <- sort.arrayCGH(gradient.nonorm)

    ## plot genomic profiles
    layout(matrix(c(1,2,4,5,3,3,6,6), 4,2),width=c(1, 4), height=c(6,1,6,1))
    report.plot(gradient.nonorm, chrLim="LimitChr", layout=FALSE,
    main="Pangenomic representation (before normalization)", zlim=c(-2,2),
    ylim=c(-3,2))
    report.plot(gradient.norm, chrLim="LimitChr", layout=FALSE,
    main="Pangenomic representation (after normalization)", zlim=c(-2,2),
    ylim=c(-3,2))

[Package MANOR version 1.14.0 Index]