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Last data update: 2014.03.03

R: Call aberrations from segmented copy number data

callBins

R Documentation

Call aberrations from segmented copy number data

Description

Call aberrations from segmented copy number data.

Usage

callBins(object, organism=c("human", "other"), method=c("CGHcall", "cutoff"),
  cutoffs=log2(c(deletion = 0.5, loss = 1.5, gain = 2.5, amplification = 10)/2), ...)

Arguments

`object`	An object of class QDNAseqCopyNumbers
`organism`	Either “human” or “other”, see manual page for `CGHcall` for more details. This is only used for chromosome arm information when “prior” is set to “all” or “auto” (and samplesize > 20). Ignored when `method` is not “CGHcall”.
`method`	Calling method to use. Options currently implemented are: “CGHcall” or “cutoff”.
`cutoffs`	When method=“cutoff”, a numeric vector of (log2-transformed) thresholds to use for calling. At least one positive and one negative value must be provided. The smallest positive value is used as the cutoff for calling gains, and the negative value closest to zero is used as the cutoff for losses. If a second positive value is provided, it is used as the cutoff for amplifications. And if a second negative value is provided, it is used as the cutoff for homozygous deletions.
`...`	Additional arguments passed to `CGHcall`.

Details

By default, chromosomal aberrations are called with CGHcall. It has been developed for the analysis of series of cancer samples, and uses a model that contains both gains and losses. If used on a single sample, or especially only on a subset of chromosomes, or especially on a single non-cancer sample, it may fail, but method “cutoff” can be used instead.

When using method “cutoff”, the default values assume a uniform cell population and correspond to thresholds of (assuming a diploid genome) 0.5, 1.5, 2.5, and 10 copies to distinguish between homozygous deletions, (hemizygous) losses, normal copy number, gains, and amplifications, respectively. When using with cancer samples, these values might require adjustments to account for tumor cell percentage.

Value

Returns an object of class QDNAseqCopyNumbers with calling results added.

Author(s)

Ilari Scheinin

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)
copyNumbersCalled <- callBins(copyNumbersSegmented)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(QDNAseq)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/QDNAseq/callBins.Rd_%03d_medium.png", width=480, height=480)
> ### Name: callBins
> ### Title: Call aberrations from segmented copy number data
> ### Aliases: callBins callBins,QDNAseqCopyNumbers-method
> ### Keywords: manip
> 
> ### ** Examples
> 
> data(LGG150)
> readCounts <- LGG150
> readCountsFiltered <- applyFilters(readCounts)
38,819	total bins
38,819	of which in selected chromosomes
36,722	of which with reference sequence
33,347	final bins
> readCountsFiltered <- estimateCorrection(readCountsFiltered)
Calculating correction for GC content and mappability
    Calculating fit for sample LGG150 (1 of 1) ...
Done.
> copyNumbers <- correctBins(readCountsFiltered)
> copyNumbersNormalized <- normalizeBins(copyNumbers)
Applying median normalization ...
> copyNumbersSmooth <- smoothOutlierBins(copyNumbersNormalized)
Smoothing outliers ...
> copyNumbersSegmented <- segmentBins(copyNumbersSmooth)
Performing segmentation:
    Segmenting: LGG150 (1 of 1) ...
> copyNumbersSegmented <- normalizeSegmentedBins(copyNumbersSegmented)
> copyNumbersCalled <- callBins(copyNumbersSegmented)
EM algorithm started ... 

[1] "Total number of segments present in the data: 16"
[1] "Number of segments used for fitting the model: 13"
21260653048817113.623.332054524701432171.235.924955904669738133.335.7
Calling iteration1:

optim results
time: 2
minimum: 16440.1358811675
116440.1156813435-1.11506058878143-0.6511447768520950.001148304813960850.3075292319462840.5257246944328131.040782914303450.161094049715017-0.01913116657745530.0193307990291194-0.0397793945356270.07730294582386270.196802474074343
21271623050489113.723.332054524701432171.235.932054524669738171.235.7
Calling iteration2:

optim results
time: 1
minimum: 16440.087930575
116439.9749789855-1.05479070177207-0.651452243764630.0003129552409922090.3188930618887660.5451512900324841.060336867134650.140015863961494-0.01881944629335520.018961062084617-0.0390095348884650.04655236735530310.17630808675607
EM algorithm done ...

Computing posterior probabilities for all segments ...

Total time:0minutes

Adjusting segmented data for cellularity ... 

Cellularity sample1: 1

Adjusting normalized data for cellularity ... 

Cellularity sample1: 1

1
21288053283236113.725.132054525721718171.243.732054524669738171.235.7
21288193449981113.726.432054525721718171.243.732054525710228171.243.6
21288173449976113.726.432054525721718171.243.732054525710228171.243.6
21288503783750113.728.932054525721718171.243.732054525710228171.243.6
21292103717675113.828.432054525721718171.243.732054525710228171.243.6
21292153717677113.828.432054525721718171.243.732054525710228171.243.6
21292213717680113.828.432054525721718171.243.732054525710228171.243.6
21292273717683113.828.432054525721718171.243.732054525710228171.243.6
21292333717686113.828.432054525721718171.243.732054525710228171.243.6
21292363717689113.828.432054525721718171.243.732054525710228171.243.6
21292543817765113.829.232054525721718171.243.732054525710228171.243.6
21296813817937113.829.232054525721718171.243.732054525710228171.243.6
FINISHED!

Total time:0minutes

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> 
> dev.off()
null device 
          1 
>