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]