segmentData {CGHcall}R Documentation

Breakpoint detection for arrayCGH data.

Description

A wrapper function to run existing breakpoint detection algorithms on arrayCGH data. Currently only DNAcopy is implemented.

Usage

segmentData(input, type = "dataframe", method = "DNAcopy", ...)

Arguments

input Either the name of a file or a dataframe. See details for the format.
type What kind of data format is used as input? Either 'dataframe' or 'file'.
method The method to be used for breakpoint detection. Currently only 'DNAcopy' is supported, which will run the segment function.
... Arguments for segment.

Details

The input should be either a dataframe or a tabseparated textfile (textfiles must contain a header). The first three columns should contain the name, chromosome and position in bp for each array target respectively. The chromosome and position column must contain numbers only. Following these is a column with normalized log2 ratios for each of your samples. If the input type is a textfile, missing values should be represented as 'NA' or an empty field.

Value

This function returns a dataframe in the same format as the input with segmented arrayCGH data.

Author(s)

Sjoerd Vosse & Mark van de Wiel

References

Venkatraman, A.S., Olshen, A.B. (2007). A faster circulary binary segmentation algorithm for the analysis of array CGH data. Bioinformatics, 23, 657-663.

Examples

  data(Wilting)
  ## First preprocess the data
  raw.data <- preprocess(Wilting, type="dataframe")
  ## Simple global median normalization for samples with 75% tumor cells
  perc.tumor <- rep(0.75, 3)
  normalized.data <- normalize(raw.data, cellularity=perc.tumor)
  ## 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)

[Package CGHcall version 1.0.1 Index]