normalize.quantiles.robust {affy}R Documentation

Robust Quantile Normalization

Description

Using a normalization based upon quantiles, this function normalizes a matrix of probe level intensities. Allows weighting of chips

Usage

      normalize.quantiles.robust(x,weights=NULL,
                remove.extreme=c("variance","mean","both","none"),
                n.remove=1,approx.meth = FALSE,use.median=FALSE,use.log2=FALSE)

Arguments

x A matrix of intensities, columns are chips, rows are probes
weights A vector of weights, one for each chip
remove.extreme If weights is null, then this will be used for determining which chips to remove from the calculation of the normalization distribution, See details for more info
n.remove number of chips to remove
approx.meth Use the approximation method. Not currently implememnted
use.median if TRUE use the median to compute normalization chip, otherwise use a weighted median
use.log2 work on log2 scale. This means we will be using the geometric mean rather than ordinary mean

Details

This method is based upon the concept of a quantile-quantile plot extended to n dimensions. Note that the matrix is of intensities not log intensities. The function performs better with raw intensities.

Choosing variance will remove chips with variances much higher or lower than the other chips, mean removes chips with the mean most different from all the other means, both removes first extreme variance and then an extreme mean. The option none does not remove any chips, but will assign equal weights to all chips.

Value

a matrix of normalized intensites

Note

This function is still experimental.

Author(s)

Ben Bolstad, bolstad@stat.berkeley.edu

See Also

normalize, normalize.quantiles


[Package affy version 1.8.1 Index]