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Last data update: 2014.03.03

R: Array image and a genomic representation of a normalized...

report.plot

R Documentation

Array image and a genomic representation of a normalized arrayCGH

Description

Displays an array image and a genomic representation of a normalized arrayCGH.

Usage

## S3 method for class 'arrayCGH'
report.plot(arrayCGH, x="PosOrder", y=c("LogRatioNorm",
  "LogRatio"), chrLim=NULL, layout=TRUE, main=NULL, zlim=NULL, ...)
## Default S3 method:
report.plot(spot.data, clone.data, design, x="PosOrder",
  y=c("LogRatioNorm", "LogRatio"), chrLim=NULL, layout=TRUE, main=NULL,
  zlim=NULL, ...)

Arguments

`arrayCGH`	an object of type `arrayCGH`.
`spot.data`	data.frame with spot-level information to be passed to arrayPlot.
`clone.data`	data.frame with clone-level information to be passed to genome.plot.
`design`	vector of length 4 with array design: number of blocks per column and per row, number of columns and rows per block.
`x`	a variable name from arrayCGH$cloneValues giving the order position of the clones along the genome.
`y`	a vector of one or two variable names to be plotted on the array and along the genome. The first one is taken from `arrayCGH$arrayValues` and is plotted on the array; the second one (or the first one if only one name was provided) is taken from `arrayCGH$cloneValues` and is plotted along the genome.
`chrLim`	an optional variable name from arrayCGH$cloneValues giving the limits of each chromosome.
`layout`	if `TRUE`, plot layout is set to a 1*2 matrix with relative column widths 1 and 4.
`main`	title for the genomic profile.
`zlim`	numeric vector of length 2 to be passed to `arrayPlot`: minimum and maximum signal values for array image display.
`...`	further arguments to be passed to `genome.plot`.

Details

This function successively calls arrayPlot and genome.plot.

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.

Examples

data(spatial)

### edge: local spatial bias
## aggregate arrayCGH without normalization for comparison with
## normalized array
edge.nonorm <- norm(edge, flag.list=NULL, FUN=median, na.rm=TRUE) 
edge.nonorm <- sort(edge.nonorm, position.var="PosOrder")

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
## aggregate arrayCGH without normalization for comparison with
## normalized array
gradient.nonorm <- norm(gradient, flag.list=NULL, FUN=median, na.rm=TRUE) 
gradient.nonorm <- sort(gradient.nonorm)

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))

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/report.plot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: report.plot
> ### Title: Array image and a genomic representation of a normalized
> ###   arrayCGH
> ### Aliases: report.plot report.plot.arrayCGH report.plot.default
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(spatial)
> 
> ### edge: local spatial bias
> ## aggregate arrayCGH without normalization for comparison with
> ## normalized array
> edge.nonorm <- norm(edge, flag.list=NULL, FUN=median, na.rm=TRUE) 
> edge.nonorm <- sort(edge.nonorm, position.var="PosOrder")
> 
> 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
> ## aggregate arrayCGH without normalization for comparison with
> ## normalized array
> gradient.nonorm <- norm(gradient, flag.list=NULL, FUN=median, na.rm=TRUE) 
> gradient.nonorm <- sort(gradient.nonorm)
> 
> 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)) 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>