preprocessCore: normalize.quantiles.in.blocks – R documentation

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normalize.quantiles.in.blocks

Quantile Normalization carried out separately within blocks of rows

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

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.

Author(s)

Ben Bolstad, bmb@bmbolstad.com

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 Unpublished manuscript http://bmbolstad.com/stuff/qnorm.pdf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

Examples

### 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)

preprocessCore

A collection of pre-processing functions

v1.52.1

LGPL (>= 2)

Authors

Ben Bolstad <bmb@bmbolstad.com>

Initial release