normalize.quantiles.target {affyPLM} | R Documentation

## 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)
      normalize.quantiles.determine.target(x,target.length=NULL)

### 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
`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.

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 Unpublished manuscript http://bmbolstad.com/stuff/qnorm.pdf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

### See Also

`normalize`

[Package affyPLM version 1.10.0 Index]