normalize.quantiles.robust: Robust Quantile Normalization

In preprocessCore: A collection of pre-processing functions

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,copy=TRUE,weights=NULL,
                remove.extreme=c("variance","mean","both","none"),
                n.remove=1,use.median=FALSE,use.log2=FALSE)

Arguments

x	A matrix of intensities, columns are chips, rows are probes
copy	Make a copy of matrix before normalizing. Usually safer to work with a copy
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
use.median	if TRUE use the median to compute normalization chip, otherwise uses a weighted mean
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.

Note that this function does not handle missing values (ie NA). Unexpected results might occur in this situation.

Value

a matrix of normalized intensites

Note

This function is still experimental.

Author(s)

Ben Bolstad, [e-mail]

See Also

normalize.quantiles

preprocessCore documentation built on Jan. 10, 2021, 2:01 a.m.