SegmentedCopyNumbers | R Documentation |
Package: aroma.core
Class SegmentedCopyNumbers
data.frame
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~~+--
RichDataFrame
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~~~~~~~+--
RawGenomicSignals
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~~~~~~~~~~~~+--
RawCopyNumbers
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~~~~~~~~~~~~~~~~~+--
SegmentedGenomicSignalsInterface
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~~~~~~~~~~~~~~~~~~~~~~+--
SegmentedCopyNumbers
Directly known subclasses:
public class SegmentedCopyNumbers
extends SegmentedGenomicSignalsInterface
SegmentedCopyNumbers(..., states=NULL)
... |
Arguments passed to |
states |
A |
Methods:
No methods defined.
Methods inherited from SegmentedGenomicSignalsInterface:
as.data.frame, binnedSmoothingByState, extractSubsetByState, findChangePointsByState, getStateColorMap, getStateColors, getStates, getUniqueStates, getVirtualField, getVirtualLocusFields, kernelSmoothingByState, plot, points, setStateColorMap, setStates
Methods inherited from RawCopyNumbers:
cnRange, extractRawCopyNumbers, getCNs, getCn, 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.profileCGH, 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
Henrik Bengtsson
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # 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, verbose=-1) lines(cnSa, col="blue") points(cnSa, col="blue") cnSb <- binnedSmoothingByState(cn, by=9, verbose=-1) 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")
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