postsegnormalize: Post-segmentation normalization
In CGHcall: Calling aberrations for array CGH tumor profiles.

Description Usage Arguments Details Value Author(s) Examples

View source: R/preprocess.R

This function normalizes arrayCGH data after segmentation in order to find a better 0-level.

1	postsegnormalize(segmentData, inter=c(-0.1,0.1))

`segmentData`	Object of class `cghSeg`.
`inter`	Interval in which the function should search for the normal level.

This function recursively searches for the interval containing the most segmented data, decreasing the interval length in each recursion. The recursive search makes the post-segmentation normalization robust against local maxima. This function is particularly useful for profiles for which, after segmentation, the 0-level does not coincide with many segments. It is more or less harmless to other profiles. We advise to keep the search interval (inter) small, in particular at the positive (gain) side to avoid that the 0-level is set to a common gain level.

This function returns a cghSeg object in the same format as the input with post-segmentation-normalized adjusted log2 ratios and segmented values.

Mark van de Wiel

  data(Wilting)
  ## Convert to \code{\link{cghRaw}} object
  cgh <- make_cghRaw(Wilting)
  ## First preprocess the data
  raw.data <- preprocess(cgh)
  ## Simple global median normalization for samples with 75% tumor cells
  normalized.data <- normalize(raw.data)  
  ## Segmentation with slightly relaxed significance level to accept change-points.
  ## Note that segmentation can take a long time.
  ## Not run: segmented.data <- segmentData(normalized.data, alpha=0.02)
  ## Not run: postsegnormalized.data <- postsegnormalize(segmented.data, inter=c(-0.1,0.1))