SQN {SQN} | R Documentation |
subset quantile normalization
Description
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.
Usage
SQN(y, N.mix = 5, ctrl.id, model.weight = 0.9)
Arguments
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 |
Value
A matrix of normalized data
Author(s)
Zhijin Wu
References
Wu Z and Aryee M. Subset Quantile Normalization using Negative Control Features (2010) Journal of Computational Biology, 17(10)
Examples
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")
[Package SQN version 1.0.6 Index]