makeRobustCNVR: Calculates robust CNV regions.
In cn.mops: cn.mops - Mixture of Poissons for CNV detection in NGS data

Description Usage Arguments Details Value Author(s) Examples

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

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

`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.

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.

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

Guenter Klambauer klambauer@bioinf.jku.at

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

Loading required package: parallel
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:base':

    expand.grid

Loading required package: IRanges
Loading required package: GenomeInfoDb

Attaching package: 'cn.mops'

The following object is masked from 'package:S4Vectors':

    params

Normalizing...
Starting local modeling, please be patient...  
Reference sequence:  undef
Starting segmentation algorithm...
Using "fastseg" for segmentation.