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]