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.
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
>