makeRobustCNVR: Calculates robust CNV regions.

Description Usage Arguments Details Value Author(s) Examples

Description

This generic function calculates robust CNV regions by segmenting the I/NI call per genomic region of an object CNVDetectionResult-class.

Usage

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## S4 method for signature 'CNVDetectionResult'
makeRobustCNVR(object, robust = 0.5,
  minWidth = 4, ...)

Arguments

object

An instance of "CNVDetectionResult"

robust

Robustness parameter. The higher the value, the more samples are required to have a CNV that confirms the CNV region. Setting this parameter to 0 restores the original CNV regions. (Default=0.5)

minWidth

The minimum length measured in genomic regions a CNV region has to span in order to be called. A parameter of the segmentation algorithm. (Default=4).

...

Additional parameters passed to the segmentation algorithm.

Details

This generic function calculates robust CNV regions by segmenting the I/NI call per genomic region of an object CNVDetectionResult-class.

cn.mops usually reports a CNV region if at least one individual has a CNV in this region. For some applications it is useful to find more common CNV regions, i.e., regions in which more than one sample has a CNV. The I/NI call measures both signal strength and how many sample show an abnormal copy number, therefore segmentation of the I/NI call can provide robust CNV regions.

Value

makeRobustCNVR returns a "CNVDetectionResult" object containing new values in the slot "cnvr".

Author(s)

Guenter Klambauer [email protected]

Examples

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data(cn.mops)
r <- cn.mops(X[1:100,1:5])
rr <- calcIntegerCopyNumbers(makeRobustCNVR(r,robust=0.1,minWidth=3))

cn.mops documentation built on Nov. 1, 2018, 3:01 a.m.