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

1 2 | ```
normalize.quantiles.use.target(x,target,copy=TRUE,subset=NULL)
normalize.quantiles.determine.target(x,target.length=NULL,subset=NULL)
``` |

`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 |

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

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.

From `normalize.quantiles.use.target`

a normalized `matrix`

.

Ben Bolstad, bmb@bmbolstad.com

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 Unpublished manuscript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`

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