cghMCR (version 1.30.0)

cghMCR-class: Class "cghMCR" is a S4 class for the identification of minimum common regions of gains or losses across samples

Description

Objects of this class provides the functionalities to detecting chromosome regions that show gains or losses across differnet samples

Objects from the Class

Objects can be created by calls of the form new("cghMCR", ...). A constructor cghMCR may be used to instantiate object of this class

Slots

DNASeg:

Object of class "data.frame" containing segmentation data derived from segmentation analysis using segment

DNAData:

Object of class "data.frame" containing raw data derived used for the segmentation analysisfrom segmentation analysis

altered:

Object of class "data.frame" containing data for the altered regions

gapAllowed:

Object of class gapAllowed is an integer specifying low threshold of base pair number to separate two adjacent segments, belower which the two segments will be joined as an altered span

alteredLow:

Object of class alteredLow is a positive number between 0 and 1 specifying the lower reshold percential value. Only segments with values falling below this threshold are considered as altered span

alteredHigh:

Object of class alteredHigh is a positive number between 0 and 1 specifying the upper reshold percential value. Only segments with values falling over this threshold are considered as altered span

recurrence:

Object of class recurrence is an integer between 1 and 100 that specifies the rate of occurrence for a gain or loss that are observed across sample. Only gains or losses with ocurrence rate grater than the threshold values are declared as MCRs

spanLimit:

Object of class spanLimit is an integer that defines the leangh of altered spans that can be considered as locus. It is not of any use at this time

thresholdType:

A character string that can be either "quantile", "value" to indicate the type of the value for recurrence

Methods

MCR

signature(object = "cghMCR"): identifies minimum common regions of gains/losses across samples

See Also

cghMCR

Examples

  require("CNTools")
  data("sampleData")
  cghmcr <- cghMCR(sampleData[sampleData[, "ID"] %in%
         sample(unique(sampleData[, "ID"]), 20), ], gapAllowed = 500,
         alteredLow = 0.20, alteredHigh = 0.80, recurrence = 50)