RawCopyNumbers {aroma.core} | R Documentation |
Package: aroma.core
Class RawCopyNumbers
Object
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~~+--
RawGenomicSignals
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RawCopyNumbers
Directly known subclasses:
SegmentedCopyNumbers
public static class RawCopyNumbers
extends RawGenomicSignals
RawCopyNumbers(cn=NULL, ...)
cn |
A numeric vector of length J specifying the copy number
at each loci. |
... |
Arguments passed to RawGenomicSignals . |
Methods:
as.data.frame | - | |
cnRange | - | |
extractRawCopyNumbers | - | |
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, setName, 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 # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 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) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # Plot along genome # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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 # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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")