xyplot: Plot copy number and physical position for a set of genomic...
In SNPchip: Visualizations for copy number alterations

Description Usage Arguments Details Value Author(s) See Also Examples

Plot copy number and physical position given by a CNSet object for a set of genomic intervals stored in a RangedDataCVN object.

1	xyplot2(x, data, range, frame=50e3L, ...)

`x`	A `formula`. Currently, the formula must be one of `cn~x`, `cn ~ x \| id` or `cn ~ x \| range` when `data` is a `CNSet`. If `data` is a `BeadStudioSet`, the formula has the form `lrr ~ x\| range` or `baf ~ x \| range`.
`data`	A `CNSet`, `BeadStudioSet`, or `SnpSet` object.
`...`	A `RangedDataCNV` object must be passed by the name 'range'. Arguments for `xyplot` are passed to `xyplot2`. Additional arguments are passed to `xypanel` and `panel.xyplot`.
`range`	A `RangedDataCNV` object.
`frame`	The genomic distance (basepairs) to the left and right of the start and stop coordinates in the `range` object.

These functions plot copy number estimates versus physical position. The function is particularly useful for multi-panel displays in which the copy number estimates for a single range of a GRanges object appears in one panel. The size of the multi-panel display depends on the number of ranges (rows) in the GRanges object.

An object of class trellis.

R. Scharpf

xyplot, xypanel

To modify the plot appearance from the default, additional arguments can be passed to panel.xyplot, lpoints, and lrect.

  ## simulated data
  library(oligoClasses)
  library(IRanges)
  library(VanillaICE)
  data(oligoSetExample, package="oligoClasses")
  ## The oligoSnpSet class will likely be deprecated and made defunct
  ## in a future release.  Instead, we favor
  ## RangedSummarizedExperiment-derived classes defined in VanillaICE
  oligoSet <- oligoSet[chromosome(oligoSet) == 1, ]
  cn <- copyNumber(oligoSet)/100
  cn <- log2((2^cn)/2)
  gt <- calls(oligoSet)[,]
  ## simulate BAFs
  bf <- rep(NA, length(gt))
  u <- runif(length(gt))
  bf[gt==1 & u > 0.5] <- runif(sum(gt==1 & u > 0.5), 0, 0.05)
  bf[gt==1 & u <= 0.5] <- runif(sum(gt==1 & u <= 0.5), 0.95, 1)
  bf[gt==2] <- runif(sum(gt==2), 0.45, 0.55)
  bf[900:1200] <- runif(length(900:1200), 0, 0.03)
  gr <- GRanges(paste0("chr", chromosome(featureData(oligoSet))),
                IRanges(position(oligoSet), width=1))
  cn <- as.matrix(cn)
  bf <- as.matrix(bf)
  dimnames(cn) <- dimnames(bf) <- list(featureNames(oligoSet), sampleNames(oligoSet))
  se <- SnpArrayExperiment(cn=cn,
                           baf=bf,
                           rowRanges=gr,
                           isSnp=rep(TRUE, length(gr)))
  fit <- hmm2(se)
  g <- as(segs(fit), "GRanges")
  ## To visualize each range in it's own panel surrounded by a
  ## frame of 2,000,000 bases:
  ## (here the frames are overlapping, but the method could be
  ## applied more generally to a collection of ranges from
  ## different chromsomes and samples)
  xyplot2(cn~x | range, data=oligoSet,
	       range=g,
	       frame=2e6, panel=xypanel,
	       cex=2,
	       pch=".",
	       col.het="salmon",
	       fill.het="salmon",
	       col.hom="royalblue",
	       fill.hom="royalblue",
	       state.cex=0.5,
	       border="orange", scales=list(x="free"),
	       par.strip.text=list(cex=0.5),
	       xlab="Mb", ylab=expression(log[2]("copy number")))