SQN | R Documentation |
This function performs normalization based on a subset of negative controls whose distribution is expected to be unchanged in various samples. There is no restriction on the behavior of the rest of the measurements.
SQN(y, N.mix = 5, ctrl.id, model.weight = 0.9)
y |
A matrix of unnormalized data. |
N.mix |
Number of normal distributions in the mixture approximation. |
ctrl.id |
index of controls. Must be a vector smaller than |
model.weight |
weight given to the parametric normal mixture model |
A matrix of normalized data
Zhijin Wu
Wu Z and Aryee M. Subset Quantile Normalization using Negative Control Features (2010) Journal of Computational Biology, 17(10)
require(mclust) require(nor1mix) data(sqnData0) Ynorm=SQN(sqnData0,ctrl.id=1:1000) #after normalization par(mfrow=c(1,2)) boxplot(sqnData0,main="before normalization") boxplot(sqnData0[1:1000,],add=TRUE,col=3,boxwex=.4) boxplot(Ynorm,main="after normalization") boxplot(Ynorm[1:1000,],add=TRUE,col=3,boxwex=.4) legend(.5,11,legend=c("probes for signal","negative control probes"),text.col=c(1,3),bg="white")
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