Apply a flag to an arrayCGH

Description

Function flag$FUN is applied to a flag object for normalization

Usage

flag.arrayCGH(flag, arrayCGH)

Arguments

Details

Optional arguments in flag$args are passed to flag$FUN

Value

An object of class arrayCGH, which corresponds to the return value of flag$FUN if flag$char is null, and to the input arrayCGH object with spots given by flag$FUN flagged with flag$char

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.

See Also

to.flag, norm.arrayCGH

Examples

data(spatial)
data(flags)

gradient$arrayValues$LogRatioNorm <- gradient$arrayValues$LogRatio
## flag spots with no available position on the genome
gradient <- flag.arrayCGH(position.flag, gradient)

## flag spots corresponding to low poor quality clones
gradient <- flag.arrayCGH(val.mark.flag, gradient)

## flag spots excluded by Genepix pro
gradient <- flag.arrayCGH(spot.flag, gradient)

## flag local spatial bias zones
## Not run: gradient <- flag.arrayCGH(local.spatial.flag, gradient)

## correct global spatial bias
gradient <- flag.arrayCGH(global.spatial.flag, gradient)

## flag spots with low signal to noise
gradient <- flag.arrayCGH(SNR.flag, gradient)

## flag spots with extremely high log-ratios
gradient <- flag.arrayCGH(amplicon.flag, gradient)

## flag spots with poor within replicate consistency
gradient <- flag.arrayCGH(replicate.flag, gradient)

## flag spots corresponding to clones for which all other spot
## replicates have already been flagged  
gradient <- flag.arrayCGH(unique.flag, gradient)

summary.factor(gradient$arrayValues$Flag)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(MANOR)
Loading required package: GLAD

######################################################################################

Have fun with GLAD

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,

If you use the package with AWS, please cite:
Hupe et al. (Bioinformatics, 2004, and Polzehl and Spokoiny (2002,

If you use the package with HaarSeg, please cite:
Hupe et al. (Bioinformatics, 2004, and (Ben-Yaacov and Eldar, Bioinformatics, 2008,

For fast computation it is recommanded to use
the daglad function with smoothfunc=haarseg

######################################################################################

New options are available in daglad: see help for details.


Attaching package: 'MANOR'

The following object is masked from 'package:base':

    norm

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MANOR/flag.arrayCGH.Rd_%03d_medium.png", width=480, height=480)
> ### Name: flag.arrayCGH
> ### Title: Apply a flag to an arrayCGH
> ### Aliases: flag.arrayCGH flag
> ### Keywords: misc
> 
> ### ** Examples
> 
> data(spatial)
> data(flags)
> 
> gradient$arrayValues$LogRatioNorm <- gradient$arrayValues$LogRatio
> ## flag spots with no available position on the genome
> gradient <- flag.arrayCGH(position.flag, gradient)
> 
> ## flag spots corresponding to low poor quality clones
> gradient <- flag.arrayCGH(val.mark.flag, gradient)
> 
> ## flag spots excluded by Genepix pro
> gradient <- flag.arrayCGH(spot.flag, gradient)
> 
> ## flag local spatial bias zones
> ## Not run: gradient <- flag.arrayCGH(local.spatial.flag, gradient)
> 
> ## correct global spatial bias
> gradient <- flag.arrayCGH(global.spatial.flag, gradient)
> 
> ## flag spots with low signal to noise
> gradient <- flag.arrayCGH(SNR.flag, gradient)
> 
> ## flag spots with extremely high log-ratios
> gradient <- flag.arrayCGH(amplicon.flag, gradient)
> 
> ## flag spots with poor within replicate consistency
> gradient <- flag.arrayCGH(replicate.flag, gradient)
> 
> ## flag spots corresponding to clones for which all other spot
> ## replicates have already been flagged  
> gradient <- flag.arrayCGH(unique.flag, gradient)
> 
> summary.factor(gradient$arrayValues$Flag)
        E    G    V 
9937  598   62  203 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>