RawCopyNumbers {aroma.core} | R Documentation |
The RawCopyNumbers class
Description
Package: aroma.core
Class RawCopyNumbers
data.frame
~~|
~~+--
RichDataFrame
~~~~~~~|
~~~~~~~+--
RawGenomicSignals
~~~~~~~~~~~~|
~~~~~~~~~~~~+--
RawCopyNumbers
Directly known subclasses:
SegmentedCopyNumbers
public class RawCopyNumbers
extends RawGenomicSignals
Usage
RawCopyNumbers(cn=NULL, ...)
Arguments
cn |
A |
... |
Arguments passed to |
Fields and Methods
Methods:
cnRange | - | |
extractRawCopyNumbers | - | |
getSignals | - | |
plot | - | |
Methods inherited from RawGenomicSignals:
*, +, -, addBy, append, applyBinaryOperator, as.character, as.data.frame, assertOneChromosome, binnedSmoothing, binnedSmoothingByField, clearCache, clone, divideBy, drawDensity, estimateStandardDeviation, extractChromosome, extractChromosomes, extractDataForSegmentation, extractRegion, extractRegions, extractSubset, gaussianSmoothing, getBasicField, getCXY, getChromosome, getChromosomes, getDefaultLocusFields, getLocusFields, getPositions, getSigma, getSignalColumnName, getSignalColumnNames, getSignals, getWeights, getXScale, getXY, getYScale, hasWeights, kernelSmoothing, lines, multiplyBy, nbrOfChromosomes, nbrOfLoci, plot, points, print, segmentByCBS, segmentByGLAD, segmentByHaarSeg, segmentByMPCBS, setBasicField, setSigma, setSignals, setWeights, setXScale, setYScale, signalRange, sort, subtractBy, xMax, xMin, xRange, xSeq, yMax, yMin, yRange
Methods inherited from RichDataFrame:
$, $<-, [, [[, [[<-, as.data.frame, as.list, dim, dropVirtualColumn, getColumnNames, getColumnNamesTranslator, getFullName, getName, getTags, getVirtualColumn, getVirtualColumnFunction, getVirtualColumnNames, hasColumn, hasColumns, hasVirtualColumn, hasVirtualColumns, length, names, newInstance, print, rbind, setAttributes, setColumnNamesMap, setColumnNamesTranslator, setName, setTags, setVirtualColumn, subset, translateColumnNames
Methods inherited from data.frame:
$<-,data.frame-method, $<-, Math, Ops,nonStructure,vector-method, Ops,structure,vector-method, Ops,vector,nonStructure-method, Ops,vector,structure-method, Ops, Summary, [, [<-,data.frame-method, [<-, [[, [[<-,data.frame-method, [[<-, aggregate, anyDuplicated, anyNA, as.NonPairedPSCNData, as.PairedPSCNData, as.data.frame, as.list, as.matrix, as.vector, attachLocally, by, callSegmentationOutliers, cbind, coerce,ANY,list-method, coerce,oldClass,S3-method, dim, dimnames, dimnames<-, dropSegmentationOutliers, droplevels, duplicated, edit, findLargeGaps, format, formula, head, initialize,oldClass-method, is.na, merge, na.exclude, na.omit, plot, plotDensity, print, prompt, rbind, row.names, row.names<-, rowsum, segmentByCBS, segmentByPairedPSCBS, show,oldClass-method, slotsFromS3,data.frame-method, split, split<-, stack, str, subset, summary, t, tail, transform, type.convert, unique, unstack, unwrap, within, wrap, writeDataFrame, xtfrm
Author(s)
Henrik Bengtsson
Examples
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 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")