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Last data update: 2014.03.03

R: Divide binned read counts with those of reference samples

compareToReference

R Documentation

Divide binned read counts with those of reference samples

Description

Divide binned read counts with those of reference samples.

Usage

compareToReference(object, references, force=FALSE)

Arguments

`object`	An object of class `QDNAseqCopyNumbers`.
`references`	A numeric vector of indexes of the reference sample. Must be the same length as there are samples in object. When `NA`, the sample will be kept as is. When `FALSE`, the sample will be removed from the output. As an example, object contains three samples: tumor1, tumor2, and normal2. There is no reference for tumor1, but normal2 is a matched normal sample from the same patient as tumor2. The keep tumor1 as is, but to divide tumor2 with normal2, argument references should be `c(NA, 3, FALSE)`.
`force`	Whether to force the operation even when downstream data will be lost.

Value

Returns a QDNAseqCopyNumbers object with the desired samples divided by the signal of their reference samples.

Author(s)

Ilari Scheinin

Examples

data(LGG150)
readCounts <- LGG150
readCountsFiltered <- applyFilters(readCounts)
readCountsFiltered <- estimateCorrection(readCountsFiltered)
copyNumbers <- correctBins(readCountsFiltered)
copyNumbersNormalized <- normalizeBins(copyNumbers)
copyNumbersSmooth <- smoothOutlierBins(copyNumbersNormalized)
# Note: the following command will compare the sample to itself, which
# does not really make sense:
tumorVsNormal <- compareToReference(copyNumbersSmooth, c(1))

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(QDNAseq)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/QDNAseq/compareToReference.Rd_%03d_medium.png", width=480, height=480)
> ### Name: compareToReference
> ### Title: Divide binned read counts with those of reference samples
> ### Aliases: compareToReference
> ###   compareToReference,QDNAseqCopyNumbers,numeric-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 ...
> # Note: the following command will compare the sample to itself, which
> # does not really make sense:
> tumorVsNormal <- compareToReference(copyNumbersSmooth, c(1))
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>