normalizeSegmentedBins {QDNAseq} | R Documentation

## Normalize segmented bins

### Description

Normalize segmented bins.

### Usage

    normalizeSegmentedBins(object, inter=c(-0.1, 0.1), force=FALSE)

### Arguments

`object` | An object of class QDNAseqCopyNumbers.
`inter` | The interval in which the function should search for the normal level.
`force` | Whether to force execution when it causes removal of downstream calling results.

### Details

This function recursively searches for the interval containing the most segmented data, decreasing the interval length in each recursion. The recursive search makes the post-segmentation normalization robust against local maxima. This function is particularly useful for profiles for which, after segmentation, the 0-level does not coincide with many segments. It is more or less harmless to other profiles. We advise to keep the search interval (inter) small, in particular at the positive (gain) side to avoid that the 0-level is set to a common gain level.

### Value

Returns an object of class QDNAseqCopyNumbers with re-normalized data.

### Author(s)

Ilari Scheinin

### See Also

Internally, `postsegnormalize` of the CGHcall package is used.

### Examples

    data(LGG150)
    readCounts <- LGG150
    readCountsFiltered <- applyFilters(readCounts)
    readCountsFiltered <- estimateCorrection(readCountsFiltered)
    copyNumbers <- correctBins(readCountsFiltered)
    copyNumbersNormalized <- normalizeBins(copyNumbers)
    copyNumbersSmooth <- smoothOutlierBins(copyNumbersNormalized)
    copyNumbersSegmented <- segmentBins(copyNumbersSmooth)
    copyNumbersSegmented <- normalizeSegmentedBins(copyNumbersSegmented)

[Package QDNAseq version 1.24.0 Index]