# NOT RUN {
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# Simulating copy-number data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Number of loci
J <- 1000
mu <- double(J)
mu[200:300] <- mu[200:300] + 1
mu[650:800] <- mu[650:800] - 1
eps <- rnorm(J, sd=1/2)
y <- mu + eps
x <- sort(runif(length(y), max=length(y)))
cn <- RawCopyNumbers(y, x)
print(cn)
cn2 <- extractSubset(cn, subset=xSeq(cn, by=5))
print(cn2)
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# Plot along genome
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plot(cn, ylim=c(-3,3))
title(main="Complete and subsetted loci")
points(cn2, col="red", pch=176, cex=2)
legend("topright", pch=c(19,176), col=c("#999999", "red"),
sprintf(c("raw [n=%d]", "every 5th [n=%d]"),
c(nbrOfLoci(cn), nbrOfLoci(cn2))), bty="n")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Binned smoothing
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plot(cn, col="#999999", ylim=c(-3,3))
title(main="Binned smoothing")
cnSa <- binnedSmoothing(cn, by=3)
lines(cnSa, col="blue")
points(cnSa, col="blue")
cnSb <- binnedSmoothing(cn, by=9)
lines(cnSb, col="red")
points(cnSb, col="red")
legend("topright", pch=19, col=c("#999999", "blue", "red"),
sprintf(c("raw [n=%d]", "Bin(w=3) [n=%d]", "Bin(w=9) [n=%d]"),
c(nbrOfLoci(cn), nbrOfLoci(cnSa), nbrOfLoci(cnSb))), bty="n")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Binned smoothing (by count)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
plot(cn, col="#999999", ylim=c(-3,3))
title(main="Binned smoothing (by count)")
cnSa <- binnedSmoothing(cn, by=3, byCount=TRUE)
lines(cnSa, col="blue")
points(cnSa, col="blue")
cnSb <- binnedSmoothing(cn, by=9, byCount=TRUE)
lines(cnSb, col="red")
points(cnSb, col="red")
legend("topright", pch=19, col=c("#999999", "blue", "red"),
sprintf(c("raw [n=%d]", "BinO(w=3) [n=%d]", "BinO(w=9) [n=%d]"),
c(nbrOfLoci(cn), nbrOfLoci(cnSa), nbrOfLoci(cnSb))), bty="n")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kernel smoothing (default is Gaussian)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
plot(cn, col="#999999", ylim=c(-3,3))
title(main="Kernel smoothing w/ Gaussian kernel")
cnSa <- kernelSmoothing(cn, h=2)
points(cnSa, col="blue")
cnSb <- kernelSmoothing(cn, h=5)
points(cnSb, col="red")
legend("topright", pch=19, col=c("#999999", "blue", "red"),
sprintf(c("raw [n=%d]", "N(.,sd=2) [n=%d]", "N(.,sd=5) [n=%d]"),
c(nbrOfLoci(cn), nbrOfLoci(cnSa), nbrOfLoci(cnSb))), bty="n")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kernel smoothing
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
plot(cn, col="#999999", ylim=c(-3,3))
title(main="Kernel smoothing w/ uniform kernel")
xOut <- xSeq(cn, by=10)
cnSa <- kernelSmoothing(cn, xOut=xOut, kernel="uniform", h=2)
lines(cnSa, col="blue")
points(cnSa, col="blue")
cnSb <- kernelSmoothing(cn, xOut=xOut, kernel="uniform", h=5)
lines(cnSb, col="red")
points(cnSb, col="red")
legend("topright", pch=19, col=c("#999999", "blue", "red"),
sprintf(c("raw [n=%d]", "U(w=2) [n=%d]", "U(w=5) [n=%d]"),
c(nbrOfLoci(cn), nbrOfLoci(cnSa), nbrOfLoci(cnSb))), bty="n")
# }
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