segmentBins {QDNAseq} | R Documentation

## Segments normalized copy number data

### Description

Segments normalized copy number data.

### Usage

    segmentBins(object, smoothBy=FALSE, alpha=1e-10, undo.splits="sdundo", undo.SD=1,
      force=FALSE, transformFun="log2", ...)

### Arguments

`object` | An object of class QDNAseqCopyNumbers.
`smoothBy` | An optional integer value to perform smoothing before segmentation by taking the mean of every smoothBy bins, and then segment those means. Default (`FALSE`) is to perform no smoothing. `smoothBy=1L` is a special case that will not perform smoothing, but will split the segmentation process by chromosome instead of by sample.
`alpha` | Significance levels for the test to accept change-points. Default is 1e-10.
`undo.splits` | A character string specifying how change-points are to be undone, if at all. Default is "sdundo", which undoes splits that are not at least this many SDs apart. Other choices are "prune", which uses a sum of squares criterion, and "none".
`undo.SD` | The number of SDs between means to keep a split if undo.splits="sdundo". Default is 1.0.
`force` | Whether to force execution when it causes removal of downstream calling results.
`transformFun` | A function to transform the data with. This can be the default "log2" for log2(x + .Machine$double.xmin), "sqrt" for the Anscombe transform of sqrt(x * 3/8) which stabilizes the variance, "none" for no transformation, or any R function that performs the desired transformation and also its inverse when called with parameter `inv=TRUE`.

`...` | Additional arguments passed to `segment`.

### Value

Returns an object of class QDNAseqCopyNumbers with segmentation results added.

### Parallel processing

This function uses future to segment samples in parallel.

### Author(s)

Ilari Scheinin

### See Also

Internally, `segment` of the DNAcopy package, which implements the CBS method, is used to segment the data.

### Examples

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

[Package QDNAseq version 1.24.0 Index]