# Invariant Set normalization

### Description

Normalize arrays in an `AffyBatch`

using an
invariant set.

### Usage

1 2 3 4 5 6 | ```
normalize.AffyBatch.invariantset(abatch, prd.td = c(0.003, 0.007),
verbose = FALSE,
baseline.type = c("mean","median","pseudo-mean","pseudo-median"),
type = c("separate","pmonly","mmonly","together"))
normalize.invariantset(data, ref, prd.td=c(0.003,0.007))
``` |

### Arguments

`abatch` |
an |

`data` |
a vector of intensities on a chip (to normalize to the reference). |

`ref` |
a vector of reference intensities. |

`prd.td` |
cutoff parameter (details in the bibliographic reference). |

`baseline.type` |
specifies how to determine the baseline array. |

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

`verbose` |
logical indicating printing throughout the normalization. |

### Details

The set of invariant intensities between `data`

and `ref`

is
found through an iterative process (based on the respective ranks the
intensities). This set of intensities is used to generate a normalization
curve by smoothing.

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.

### Value

Respectively a `AffyBatch`

of normalized
objects, or a vector of normalized intensities, with an attribute
"invariant.set" holding the indexes of the 'invariant' intensities.

### Author(s)

L. Gautier <laurent@cbs.dtu.dk> (Thanks to Cheng Li for the discussions about the algorithm.)

### References

Cheng Li and Wing Hung Wong, Model-based analysis of oligonucleotides arrays: model validation, design issues and standard error application. Genome Biology 2001, 2(8):research0032.1-0032.11

### See Also

`normalize`

to normalize `AffyBatch`

objects.