plot.DNAcopy {DNAcopy} | R Documentation |
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.
plot.DNAcopy(x, plot.type=c("whole","plateau","samplebychrom", "chrombysample"), altcol=TRUE, sbyc.layout = NULL, cbys.nchrom=1, cbys.layout=NULL,include.means = TRUE, ...)
x |
an object of class DNAcopy resulting from analyzing
data from copy number array experiments. |
plot.type |
the type of plot. |
altcol |
logical flag to indicate if chromosomes should be plotted in alternating colors in the whole genome 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. |
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.
#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")