TumorBoost  is a normalization method that normalizes the allele B fractions of a tumor sample given the allele B fractions and genotypes of a matched normal. The method is a single-sample (single-pair) method. It does not require total copy-number estimates. The normalization is done such that the total copy number is unchanged afterwards.
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Allele B fractions are defined as the ratio between the allele B signal and the sum of both (all) allele signals at the same locus. Allele B fractions are typically within [0,1], but may have a slightly wider support due to for instance negative noise. This is typically also the case for the returned normalized allele B fractions.
vector of length J containing the normalized
allele B fractions for the tumor.
modelFit is a
list containing model fit parameters.
This method provides a few different "flavors" for normalizing the
data. The following values of argument
flavor are accepted:
v4: (default) The TumorBoost method, i.e. Eqns. (8)-(9) in .
v3: Eqn (9) in  is applied to both heterozygous and homozygous SNPs, which effectively is v4 where the normalized allele B fractions for homozygous SNPs becomes 0 and 1.
v1: TumorBoost where correction factor is forced to one, i.e. η_j=1. As explained in , this is a suboptimal normalization method. See also the discussion in the paragraph following Eqn (12) in .
As of aroma.light v1.33.3 (March 30, 2014),
preserveScale no longer has a default value and has
to be specified explicitly. This is done in order to change the
FALSE in a future version, while minimizing the risk
Allele B fractions are more or less compressed toward a half, e.g. the signals for homozygous SNPs are slightly away from zero and one. The TumorBoost method decreases the correlation in allele B fractions between the tumor and the normal conditioned on the genotype. What it does not control for is the mean level of the allele B fraction conditioned on the genotype.
By design, most flavors of the method will correct the homozygous SNPs such that their mean levels get close to the expected zero and one levels. However, the heterozygous SNPs will typically keep the same mean levels as before. One possibility is to adjust the signals such as the mean levels of the heterozygous SNPs relative to that of the homozygous SNPs is the same after as before the normalization.
preserveScale=TRUE, then SNPs that are heterozygous
(in the matched normal) are corrected for signal compression using
an estimate of signal compression based on the amount of correction
performed by TumorBoost on SNPs that are homozygous
(in the matched normal).
The option of preserving the scale is not discussed in the
TumorBoost paper , which presents the
Henrik Bengtsson, Pierre Neuvial
 H. Bengtsson, P. Neuvial and T.P. Speed, TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays, BMC Bioinformatics, 2010, 11:245. [PMID 20462408]
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library(R.utils) # Load data pathname <- system.file("data-ex/TumorBoost,fracB,exampleData.Rbin", package="aroma.light") data <- loadObject(pathname) attachLocally(data) pos <- position/1e6 muN <- genotypeN layout(matrix(1:4, ncol=1)) par(mar=c(2.5,4,0.5,1)+0.1) ylim <- c(-0.05, 1.05) col <- rep("#999999", length(muN)) col[muN == 1/2] <- "#000000" # Allele B fractions for the normal sample plot(pos, betaN, col=col, ylim=ylim) # Allele B fractions for the tumor sample plot(pos, betaT, col=col, ylim=ylim) # TumorBoost w/ naive genotype calls betaTN <- normalizeTumorBoost(betaT=betaT, betaN=betaN, preserveScale=FALSE) plot(pos, betaTN, col=col, ylim=ylim) # TumorBoost w/ external multi-sample genotype calls betaTNx <- normalizeTumorBoost(betaT=betaT, betaN=betaN, muN=muN, preserveScale=FALSE) plot(pos, betaTNx, col=col, ylim=ylim)
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