norm: Normalize an object of type arrayCGH

In MANOR: CGH Micro-Array NORmalization

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

  ## S3 method for class 'arrayCGH'
norm(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, [e-mail].

References

P. Neuvial, P. Hup?, I. Brito, S. Liva, E. Mani?, 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
edge.norm <- norm(edge, flag.list=flag.list1,
var="LogRatio", FUN=median, na.rm=TRUE)
print(edge.norm$flags) ## spot-level flag summary (computed by flag.summary)

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

## sort genomic informations 
edge.norm <- sort(edge.norm, position.var="PosOrder")
edge.nonorm <- sort(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
gradient.norm <- norm(gradient, flag.list=flag.list2, 
                      var="LogRatio", FUN=median, na.rm=TRUE)
## aggregate arrayCGH without normalization
gradient.nonorm <- norm(gradient, flag.list=NULL, var="LogRatio", FUN=median, na.rm=TRUE) 

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

MANOR documentation built on Nov. 8, 2020, 6:52 p.m.