Calculate mBAF from BAF

Description

Calculate Mirrored B-Allele Frequence (mBAF) from B-Allele Frequency (BAF) as in Staaf et al., Genome Biology, 2008. BAF is converted to mBAF by folding around 0.5 so that is then between 0.5 and 1. HOM value are then made NA to leave only HET values that can be easily segmented. Values > hom.cutoff are made NA. Then, if genotypes (usually from a matched normal) are provided as the matrix 'calls' additional HOMs can be set to NA. The argument 'call.pairs' is used to match columns in 'calls' to columns in 'baf'.

Usage

baf2mbaf(baf, hom.cutoff = 0.95, calls = NULL, call.pairs = NULL)

Arguments

Value

numeric matix of mBAF values

Examples

   data(genoset,package="genoset")
   mbaf = baf2mbaf( genoset.ds[, , "baf"], hom.cutoff=0.9 )
   calls = matrix(sample(c("AT","AA","CG","GC","AT","GG"),(nrow(genoset.ds) * 2),replace=TRUE),ncol=2,dimnames=list(rownames(genoset.ds),c("K","L")))
   mbaf = baf2mbaf( genoset.ds[, , "baf"], hom.cutoff=0.9, calls = calls, call.pairs = list(K="L",L="L") ) # Sample L is matched normal for tumor sample K, M only uses hom.cutoff
   genoset.ds[, ,"mbaf"] = baf2mbaf( genoset.ds[, , "baf"], hom.cutoff=0.9 ) # Put mbaf back into the BAFSet object as a new element

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)

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> library(genoset)
Loading required package: BiocGenerics
Loading required package: parallel

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, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, 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, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: GenomicRanges
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


*** Genoset API Changes ***
The genoset class has transitioned to the
RangedSummarizedExperiment API from the eSet API (e.g. use colnames instead of sampleNames). ***

Attaching package: 'genoset'

The following object is masked from 'package:GenomicRanges':

    pos

The following objects are masked from 'package:S4Vectors':

    colMeans, colSums, rowMeans, rowSums

The following objects are masked from 'package:base':

    colMeans, colSums, rowMeans, rowSums

Warning messages:
1: multiple methods tables found for 'colMeans' 
2: multiple methods tables found for 'colSums' 
3: multiple methods tables found for 'rowMeans' 
4: multiple methods tables found for 'rowSums' 
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/genoset/baf2mbaf.Rd_%03d_medium.png", width=480, height=480)
> ### Name: baf2mbaf
> ### Title: Calculate mBAF from BAF
> ### Aliases: baf2mbaf
> 
> ### ** Examples
> 
>    data(genoset,package="genoset")
>    mbaf = baf2mbaf( genoset.ds[, , "baf"], hom.cutoff=0.9 )
>    calls = matrix(sample(c("AT","AA","CG","GC","AT","GG"),(nrow(genoset.ds) * 2),replace=TRUE),ncol=2,dimnames=list(rownames(genoset.ds),c("K","L")))
>    mbaf = baf2mbaf( genoset.ds[, , "baf"], hom.cutoff=0.9, calls = calls, call.pairs = list(K="L",L="L") ) # Sample L is matched normal for tumor sample K, M only uses hom.cutoff
>    genoset.ds[, ,"mbaf"] = baf2mbaf( genoset.ds[, , "baf"], hom.cutoff=0.9 ) # Put mbaf back into the BAFSet object as a new element
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>