affy (version 1.50.0)

normalize.quantiles.robust: 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.AffyBatch.quantiles.robust(abatch, type = c("separate","pmonly","mmonly","together"), weights = NULL, remove.extreme = c("variance","mean","both","none"), n.remove = 1, use.median = FALSE, use.log2 = FALSE)

Arguments

abatch

an AffyBatch object.

type

a string specifying how the normalization should be applied. See details for more.

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, the 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.

Value

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. The type argument should be one of "separate","pmonly","mmonly","together" which indicates whether to normalize only one probe type (PM,MM) or both together or separately.

See Also

normalize, normalize.quantiles