normalize.quantiles.target: Quantile Normalization using a specified target distribution vector

Description

Using a normalization based upon quantiles, these function normalizes the columns of a matrix based upon a specified normalization distribution

Usage

normalize.quantiles.use.target(x,target,copy=TRUE,subset=NULL) normalize.quantiles.determine.target(x,target.length=NULL,subset=NULL)

Arguments

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

target

A vector containing datapoints from the distribution to be normalized to

target.length

number of datapoints to return in target distribution vector. If NULL then this will be taken to be equal to the number of rows in the matrix.

subset

A logical variable indexing whether corresponding row should be used in reference distribution determination

Value

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

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).

These functions will handle missing data (ie NA values), based on the assumption that the data is missing at random.

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 Unpublished manuscript http://bmbolstad.com/stuff/qnorm.pdf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