| SegmentedCopyNumbers {aroma.core} | R Documentation |
Package: aroma.core
Class SegmentedCopyNumbers
Object
~~|
~~+--RawGenomicSignals
~~~~~~~|
~~~~~~~+--RawCopyNumbers
~~~~~~~~~~~~|
~~~~~~~~~~~~+--SegmentedGenomicSignalsInterface
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--SegmentedCopyNumbers
Directly known subclasses:
public static class SegmentedCopyNumbers
extends SegmentedGenomicSignalsInterface
SegmentedCopyNumbers(..., states=NULL)
... |
Arguments passed to RawCopyNumbers. |
states |
A function returning the copy-number states given a
vector of locus positions. |
Methods:
No methods defined.
Methods inherited from SegmentedGenomicSignalsInterface:
as.data.frame, binnedSmoothingByState, extractSubsetByState, getStateColorMap, getStateColors, getStates, getUniqueStates, kernelSmoothingByState, plot, points, setStateColorMap, setStates
Methods inherited from RawCopyNumbers:
as.data.frame, cnRange, getCn, getCNs, plot
Methods inherited from RawGenomicSignals:
addBy, addLocusFields, append, applyBinaryOperator, as.data.frame, binnedSmoothing, divideBy, estimateStandardDeviation, extractDataForSegmentation, extractRegion, extractSubset, gaussianSmoothing, getChromosome, getLocusFields, getName, getPositions, getSigma, getSignals, getWeights, getXScale, getXY, getYScale, hasWeights, kernelSmoothing, lines, multiplyBy, nbrOfLoci, plot, points, setLocusFields, setSigma, setWeights, setXScale, setYScale, signalRange, sort, subtractBy, summary, xMax, xMin, xRange, xSeq, yMax, yMin, yRange
Methods inherited from Object:
asThis, $, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, registerFinalizer, save
Henrik Bengtsson (http://www.braju.com/R/)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Simulating copy-number data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# True CN states
stateFcn <- function(x, ...) {
states <- integer(length(x))
states[200 <=x & x <= 300] <- -1L
states[650 <=x & x <= 800] <- +1L
states
}
# Number of loci
J <- 1000
y <- rnorm(J, sd=1/2)
x <- 1:length(y)
for (state in c(-1,+1)) {
idxs <- (stateFcn(x) == state)
y[idxs] <- y[idxs] + state
}
cn <- SegmentedCopyNumbers(y, x, states=stateFcn)
print(cn)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Subsetting
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
plot(cn, ylim=c(-4,4))
title("Copy numbers annotated by state (and subset by state)")
cnS <- extractSubsetByState(cn, states=c(0,+1L))
print(cnS)
points(cnS, pch=21, cex=1.2, lwd=2, col="purple")
legend("topright", pch=c(19, 21), col=c("#999999", "purple"), sprintf(c("raw [n=%d]", "CN in {0,1} [n=%d]"), c(nbrOfLoci(cn), nbrOfLoci(cnS))), bty="n")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Kernel smoothing stratified by state
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
plot(cn, col="#999999", ylim=c(-3,3))
title(main="Kernel smoothing stratified by state w/ Gaussian kernel")
cnSa <- kernelSmoothingByState(cn, h=2)
points(cnSa, col="blue")
cnSb <- kernelSmoothingByState(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")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Binned smoothing stratified by state
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
plot(cn, col="#999999", ylim=c(-3,3))
title(main="Binned smoothing stratified by state")
cnSa <- binnedSmoothingByState(cn, by=3)
lines(cnSa, col="blue")
points(cnSa, col="blue")
cnSb <- binnedSmoothingByState(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")