Description Usage Arguments Details Value Author(s) References Examples
This function is dedicated to transform BAF value into mirrored BAF (mBAF) value. Noninformative SNPs for CNV inference have been removed, while missing values for those removed SNPs are initialized with the average of nearest SNPs.
1 2  BAF.transform(x, gt = NULL, mBAF.thd = 0.97, win.thd = 0.8,
w = 1, k = 2, median.adjust = FALSE)

x 
A vector of BAF values to be transformed. 
gt 
In tumor data set, if the tumor sample under investigation has matched normal tissue sample,

mBAF.thd 
A criteria to remove noninformative SNPs if no information from matched normal tissue is supplied. See reference for more details. 
win.thd 
A further criteria to remove possible noninformative SNPs which might pass the 
w 
The window size used in computation of a quantity to be compared with 
k 
The number of nearest SNPs used to computed the initialized values of removed noninformative SNPs. 
median.adjust 
Logical. If it is 
More details about the transformation are referred to Staaf J., et al. (2008). The missing values for removed noninformative SNPs are initialized with the average of knearest SNPs plus a normal random noise in order to eliminate the dependence of adjacent SNPs.
All returned information is collected into a list
mBAF 
A vector of mirrored BAF values. Missing values of removed noninformative SNPs are initialized for downstream analysis. 
idx 
A vector of indices of those informative SNPs with values remaining after transformation. 
idx.na 
A vector of indices of those noninformative SNPs with orignal values removed. 
Zhongyang (Thomas) Zhang, zhangzy@ucla.edu
Staaf J., et al. (2008) Segmentationbased detection of allelic imbalance and lossofheterozygosity in cancer cells using whole genome SNP arrays. Genome Biology, 9: R136+.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## simulate a sequence of BAF values for 100 SNPs
xf < sample(x=c(0,0.5,1),size=100,replace=TRUE,prob=c(0.25,0.5,0.25)) + rnorm(100,0,0.02)
xf[xf<0] < 0
xf[xf>1] < 1
## insert the signal pattern of a duplcation in the middle of x1
xm < sample(x=c(0,1),size=20,replace=TRUE,prob=c(0.5,0.5)) + rnorm(20,0,0.02)
xm[xm<0] < 0
xm[xm>1] < 1
xf[41:60] < 2/3*xf[41:60] + 1/3*xm
BAF < xf
plot(BAF,xlab="SNP",ylab="BAF")
## tranform BAF to mBAF
res < BAF.transform(x=BAF, gt = NULL, mBAF.thd = 0.97, win.thd = 0.8,
w = 1, k = 2, median.adjust = FALSE)
plot(res$mBAF,type="n",xlab="SNP",ylab="mBAF")
points(res$idx,res$mBAF[res$idx])
points(res$idx.na,res$mBAF[res$idx.na],col="gray")

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