plot.DNAcopy {DNAcopy}R Documentation

Plot the data and results from segment of a CNA object

Description

Plots the data from a copy number array experiment (aCGH, ROMA etc.) along with the results of segmenting it into regions of equal copy numbers.

Usage

  plot.DNAcopy(x, plot.type=c("whole","plateau","samplebychrom",
               "chrombysample"), sbyc.layout = NULL,cbys.nchrom=1,
               cbys.layout=NULL,include.means = TRUE, ...)

Arguments

x an object of class DNAcopy resulting from analyzing data from copy number array experiments
plot.type the type of plot.
sbyc.layout layout settings for the multifigure grid layout for the `samplebychrom' type. It should be specified as a vector of two integers which are the number of rows and columns. The default values are chosen based on the number of chromosomes to produce a near square graph. For normal genome it is 4x6 (24 chromosomes) plotted by rows.
cbys.layout layout settings for the multifigure grid layout for the `chrombysample' type. As above it should be specified as number of rows and columns and the default chosen based on the number of samples.
cbys.nchrom the number of chromosomes per page in the layout. The default is 1.
include.means logical flag to indicate whether segment means are to be drawn.
... other arguments which will be passed to plot commands.

Details

There are four possible plot types. For the type `whole' the data are plotted for the entire genome. For the `samplebychrom' type a graph with each chromosome (of a given sample) is drawn in a separate figure on a multi-figure grid. For the `plateau' type the graph is drawn with the chromosome segments re-ordered by the segment means. For the `chrombysample' type the samples for a given chromosome are drawn in a 4x6 multi-figure grid in multiples of 24. By default the segments means are drawn. For multisample data each sample or chromosome is drawn on a separate sheet. When invoked interactively the user is prompted before advancing to the next sample.

Examples


#Read in two examples from Snijders et al.

data(coriell)

#Combine into one CNA object to prepare for analysis on Chromosomes 1-23

CNA.object <- CNA(cbind(coriell$Coriell.05296,coriell$Coriell.13330),
                  coriell$Chromosome,coriell$Position,
                  data.type="logratio",sampleid=c("c05296","c13330"))

#We generally recommend smoothing single point outliers before analysis
#Make sure to check that the smoothing is proper

smoothed.CNA.object <- smooth.CNA(CNA.object)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

#Plot whole studies

plot(segment.smoothed.CNA.object, plot.type="w")

#Plot each study by chromosome

plot(segment.smoothed.CNA.object, plot.type="s")

#Plot each chromosome across studies (6 per page)

plot(segment.smoothed.CNA.object, plot.type="c", cbys.layout=c(2,1), cbys.nchrom=6)

#Plot by plateaus

plot(segment.smoothed.CNA.object, plot.type="p")


[Package DNAcopy version 1.1.0 Index]