\name{normalize.quantiles.target} \alias{normalize.quantiles.use.target} \alias{normalize.quantiles.determine.target} \title{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{ \item{x}{A matrix of intensities where each column corresponds to a chip and each row is a probe.} \item{copy}{Make a copy of matrix before normalizing. Usually safer to work with a copy} \item{target}{A vector containing datapoints from the distribution to be normalized to} \item{target.length}{number of datapoints to return in target distribution vector. If \code{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 \code{\link[affy]{rma}} or \code{\link[affy]{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. } \value{ From \code{normalize.quantiles.use.target} a normalized \code{matrix}. } \references{ Bolstad, B (2001) \emph{Probe Level Quantile Normalization of High Density Oligonucleotide Array Data}. Unpublished manuscript \url{http://bmbolstad.com/stuff/qnorm.pdf} Bolstad, B. M., Irizarry R. A., Astrand, M, and Speed, T. P. (2003) \emph{A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance.} Bioinformatics 19(2) ,pp 185-193. \url{http://bmbolstad.com/misc/normalize/normalize.html} } \author{Ben Bolstad, \email{bmb@bmbolstad.com}} \seealso{\code{\link[affy]{normalize}}} \keyword{manip}