| Type: | Package |
| Title: | Subset Quantile Normalization |
| Version: | 1.0.6 |
| Date: | 2022-06-10 |
| Author: | Zhijin(Jean) Wu, Martin Aryee |
| Maintainer: | Martin Aryee <martin.aryee@gmail.com> |
| Depends: | R (≥ 2.6.0), mclust(≥ 3.2), nor1mix(≥ 1.0-7) |
| Description: | Normalization based a subset of negative control probes as described in 'Subset quantile normalization using negative control features'. Wu Z, Aryee MJ, J Comput Biol. 2010 Oct;17(10):1385-95 [PMID 20976876]. |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.0)] |
| NeedsCompilation: | no |
| Packaged: | 2022-06-10 12:11:00 UTC; martin |
| Repository: | CRAN |
| Date/Publication: | 2022-06-10 16:00:02 UTC |
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")
example data
Description
Simulated data with two samples, each with 1000 negative controls and 5000 signal bearing probes
Usage
data(sqnData0)Format
A matrix with two columns