Examples of flag objects to apply to CGH arrays

Description

This data set provides flag objects that can be applied to arrayCGH objects in order to normalize them.

Usage

data(flags)

Format

These flag objects typically take part to a normalization process:

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.

Source

Institut Curie, manor@curie.fr.

See Also

spatial, norm.arrayCGH, flag, flag.summary

Examples

data(flags)

### complete normalization of an arrayCGH object (with spatial gradient):
## Initialize flag$args

flag.list1 <- list(local.spatial=local.spatial.flag,
  global.spatial=global.spatial.flag, spot=spot.flag, SNR=SNR.flag,
  val.mark=val.mark.flag, unique=unique.flag,
  amplicon=amplicon.flag, chromosome=chromosome.flag,
  replicate=replicate.flag)

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

### complete normalization of an arrayCGH object (with local spatial bias):
## Initialize flag$args

flag.list2 <- 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.list2$spatial$args <- alist(var="ScaledLogRatio", by.var=NULL,
nk=5, prop=0.25, thr=0.15, beta=1, family="symmetric") 
flag.list2$spot$args <- alist(var="SpotFlag")
flag.list2$spot$char <- "O"
flag.list2$spot$label <- "Image analysis"

## Not run: edge.norm <- norm(edge, flag.list=flag.list2,
var="LogRatio", FUN=median, na.rm=TRUE)
## End(Not run) 
print(edge.norm$flags) ## spot-level flag summary (computed by flag.summary)

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
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
'help.start()' for an HTML browser interface to help.
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/flags.Rd_%03d_medium.png", width=480, height=480)
> ### Name: flags
> ### Title: Examples of flag objects to apply to CGH arrays
> ### Aliases: amplicon.flag chromosome.flag control.flag flags
> ###   global.spatial.flag local.spatial.flag spatial.flag position.flag
> ###   rep.flag replicate.flag SNR.flag ref.snr.flag dapi.snr.flag spot.flag
> ###   unique.flag val.mark.flag intensity.flag
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(flags)
> 
> ### complete normalization of an arrayCGH object (with spatial gradient):
> ## Initialize flag$args
> 
> flag.list1 <- list(local.spatial=local.spatial.flag,
+   global.spatial=global.spatial.flag, spot=spot.flag, SNR=SNR.flag,
+   val.mark=val.mark.flag, unique=unique.flag,
+   amplicon=amplicon.flag, chromosome=chromosome.flag,
+   replicate=replicate.flag)
> 
> data(spatial)
> ## Not run: 
> ##D gradient.norm <- norm(gradient, flag.list=flag.list1,
> ##D var="LogRatio", FUN=median, na.rm=TRUE)
> ## End(Not run)
> print(gradient.norm$flags) ## spot-level flag summary (computed by flag.summary)
  char                      label arg count
1    S                 Local bias  NA     0
2    G             Image analysis  NA    61
3    B  Low signal to noise ratio 3.0     0
4    V          Bad quality clone 2.0   204
5    P         No genome position  NA     0
6    U                    Singlet  NA     1
7    A                   Amplicon 1.0     0
8    E Poor replicate consistency 0.1   544
9   OK                not flagged  NA  9990
> 
> ### complete normalization of an arrayCGH object (with local spatial bias):
> ## Initialize flag$args
> 
> flag.list2 <- list(spatial=local.spatial.flag, spot=spot.corr.flag,
+ ref.snr=ref.snr.flag, dapi.snr=dapi.snr.flag, rep=rep.flag,
+ unique=unique.flag) 
> flag.list2$spatial$args <- alist(var="ScaledLogRatio", by.var=NULL,
+ nk=5, prop=0.25, thr=0.15, beta=1, family="symmetric") 
> flag.list2$spot$args <- alist(var="SpotFlag")
> flag.list2$spot$char <- "O"
> flag.list2$spot$label <- "Image analysis"
> 
> ## Not run: 
> ##D edge.norm <- norm(edge, flag.list=flag.list2,
> ##D var="LogRatio", FUN=median, na.rm=TRUE)
> ## End(Not run) 
> print(edge.norm$flags) ## spot-level flag summary (computed by flag.summary)
  char                            label  arg count
1    S                       Local bias   NA   127
2    O                   Image analysis   NA    37
3    B  Low signal to noise ratio (Ref) 1.25    19
4    D Low signal to noise ratio (Dapi) 1.25    85
5    E       Poor replicate consistency 0.10     0
6    U                          Singlet   NA     8
7   OK                      not flagged   NA  7116
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>