cnvs-CNVDetectionResult-method: This generic function returns CNVs of a CNV detection method...
In cn.mops: cn.mops - Mixture of Poissons for CNV detection in NGS data

Description Usage Arguments Value Author(s) Examples

This generic function returns CNVs of a CNV detection method stored in an instance of CNVDetectionResult-class.

1 2	## S4 method for signature 'CNVDetectionResult' cnvs(object)

object

An instance of "CNVDetectionResult"

cnvs returns a eturns a "GRanges" object containing the CNVs.

Guenter Klambauer klambauer@bioinf.jku.at

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data(cn.mops)
r <- cn.mops(X[1:100,1:5])
cnvs(r)

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.
GRanges object with 1 range and 4 metadata columns:
      seqnames    ranges strand | sampleName             median
         <Rle> <IRanges>  <Rle> |   <factor>          <numeric>
  [1]    undef     72-74      * |        S_1 -0.999999999999997
                    mean        CN
               <numeric> <logical>
  [1] -0.999999999987477      <NA>
  -------
  seqinfo: 1 sequence from an unspecified genome; no seqlengths