#Lymphoma data
data(lymphoma)
#Take out a smaller subset of 6 samples (using subsetData):
sub.lymphoma <- subsetData(lymphoma,sample=1:6)
#Winsorize data:
wins.data <- winsorize(data=sub.lymphoma,return.outliers=TRUE)
#Use pcf to find segments:
uni.segments <- pcf(data=wins.data,gamma=12)
#Use multipcf to find segments as well:
multi.segments <- multipcf(data=wins.data,gamma=12)
#Plot data and pcf-segments over entire genome for all six samples (one page
#for each sample):
plotGenome(data=sub.lymphoma,segments=uni.segments)
#Let each sample define its own range, and adjust range to fit all observations:
plotGenome(data=sub.lymphoma,segments=uni.segments,equalRange=FALSE,q=0)
#Add results from multipcf on top for four of the samples and let all plots
#show on one page:
plotGenome(data=sub.lymphoma,segments=list(uni.segments,multi.segments),
layout=c(2,2),sample=c(1:4))
#Change segment-colors, line widths, and legend:
plotGenome(data=sub.lymphoma,segments=list(uni.segments,multi.segments),layout=c(2,2),
seg.col=c("red","blue"),seg.lwd=c(3,2),legend=c("uni","multi")
,sample=c(1:4))
#Aberration calling may be done by defining thresholds that determines the cuf-off
#for what should be considered biologically significant aberrations. In this
#example segments which are above 0.2 or below -0.2 are considered aberrated
#regions:
plotGenome(segments=uni.segments,sample=5,connect=FALSE)
abline(h=0.2,col="blue",lty=5)
abline(h=-0.2,col="blue",lty=5)
Run the code above in your browser using DataLab