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44 lines
1.2 KiB
R
44 lines
1.2 KiB
R
# Using changepoint to compute binary segmentation models up to 5 segments
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# Plot segement means on top of data
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if(!require(changepoint)) {
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install.packages("changepoint")
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}
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if(!require(neuroblastoma)) {
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install.packages("neuroblastoma")
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}
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# import and load neuroblastoma dataset
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library('neuroblastoma')
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data('neuroblastoma')
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# using np as neuro.profiles for profile data
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np = neuroblastoma$profiles
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xy = np[np$profile.id == 4 & np$chromosome == 2, ]
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x = xy$position
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y = xy$logratio
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# calculate changepoints
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model <- changepoint::cpt.meanvar(y,penalty="Manual",pen.value='2*log(n)',method='BinSeg', Q=10)
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cm = coef(model)
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change.points = cpts(model)
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segment.mean = cm[1]
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# detected changepoints
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print(change.points)
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# segment means
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print(segment.mean)
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# plot changepoint and segment mean over data
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svg('Medium/task2.svg')
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plot(model, xlab="Segments", ylab="logpoint ratios of the probe", main="Neuroblastoma changepoint detection using 'changepoint'")
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grid()
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legend(x=5, y=-0.5,legend=c("position vs logpoint data", "segment mean", "changepoint"), col=c("black","red","blue"), lty=c(1,1,NA), pch=c(NA,NA,'X'), cex=0.8, bg='lightblue')
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# add changepoints
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points(change.points, as.numeric(segment.mean[[1]])[1:5], col='blue', pch='X')
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dev.off()
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