normalize.quantiles.in.blocks: Quantile Normalization carried out separately within blocks... In preprocessCore: A collection of pre-processing functions

Description

Using a normalization based upon quantiles this function normalizes the columns of a matrix such that different subsets of rows get normalized together.

Usage

 `1` ``` normalize.quantiles.in.blocks(x,blocks,copy=TRUE) ```

Arguments

 `x` A matrix of intensities where each column corresponds to a chip and each row is a probe. `copy` Make a copy of matrix before normalizing. Usually safer to work with a copy `blocks` A vector giving block membership for each each row

Details

This method is based upon the concept of a quantile-quantile plot extended to n dimensions. No special allowances are made for outliers. If you make use of quantile normalization either through `rma` or `expresso` please cite Bolstad et al, Bioinformatics (2003).

Value

From `normalize.quantiles.use.target` a normalized `matrix`.

References

Bolstad, B (2001) Probe Level Quantile Normalization of High Density Oligonucleotide Array Data. Unpublished manuscript http://bmbolstad.com/stuff/qnorm.pdf

Bolstad, B. M., Irizarry R. A., Astrand, M, and Speed, T. P. (2003) A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2) ,pp 185-193. http://bmbolstad.com/misc/normalize/normalize.html

`normalize.quantiles`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ``` ### setup the data blocks <- c(rep(1,5),rep(2,5),rep(3,5)) par(mfrow=c(3,2)) x <- matrix(c(rexp(5,0.05),rnorm(5),rnorm(5,10))) boxplot(x ~ blocks) y <- matrix(c(-rexp(5,0.05),rnorm(5,10),rnorm(5))) boxplot(y ~ blocks) pre.norm <- cbind(x,y) ### the in.blocks version post.norm <- normalize.quantiles.in.blocks(pre.norm,blocks) boxplot(post.norm[,1] ~ blocks) boxplot(post.norm[,2] ~ blocks) ### the usual version post.norm <- normalize.quantiles(pre.norm) boxplot(post.norm[,1] ~ blocks) boxplot(post.norm[,2] ~ blocks) ```