normalizeData {CALIB} | R Documentation

## Normalization: estimation of absolute expression levels

### Description

estimates absolute expression levels for each combination of a gene and a tested biological condition.

### Usage

    normalizeData(RG, parameter, array = array, condition = condition, dye = dye,
                  cloneid = cloneid, idcol = idcol)

### Arguments

`RG` | an `RGList_CALIB` object
`parameter` | a `ParameterList` object
`array` | integer vector specifying the index of the arrays. Has length equal to two times of the number of arrays.
`condition` | integer vector specifying the index of the conditions. Has length equal to two times of the number of arrays.
`dye` | integer vector specifying the index of the dyes. Has length equal to two times of the number of arrays.
`cloneid` | string vector specifying the clone ids of the clones to be normalized. If missing, normalize all the clones.
`idcol` | string specifying the column name of clone ids in the genes field of RG.

### Details

This function estimates absolute expression levels for each combination of a gene and a tested biological condition from the measured intensity. It accepts measured intensities from `RG`.

The argument `parameter` is an object of `ParameterList`. The function accepts model parameters from this argument.

By using this function, for each combination of a gene and a tested biological condition, a single absolute expression level fo target is estimated. Therefore, specifying the design of experiment is necessary. Namely, the design of array, condition and dye is needed. The three arguments `array`,`condition` and `dye` are three numeric vector to indicate the design of array, condition and dye respectively. How to specify these three arguments refer to the example below.

The function is able to not only estimate all the genes on the slides but also estimate any gene on the slides seperately. The argument `cloneid` accepts the clone ids of which the genes are interested by the user. If `cloneid` argument is missing, the function will estimate all the genes on the slides.In order to match clone id in the RG, column name which indicates clone ids in RG\$genes should be specified by argument `idcol`.

### Value

a numeric matix containing the absolute expression levels. Columns indicate different conditions and rows indicate different genes.

### Warning

The function doesn't allow missing clone id. So please check before run the function.

### Note

The main calculation part in this function is done by c++ code.

### Author(s)

Hui Zhao

### References

Engelen, K., Naudts, B., DeMoor, B., Marchal, K. (2006) A calibration method for estimating absolute expression levels from microarray data. Bioinformatics 22: 1251-1258.

### Examples

    # load data: RG and parameter
    data(RG)
    data(parameter)

    # define design matrix: two arrays, two condition and color-flip design
    array <- c(1,1,2,2)
    condition <- c(1,2,2,1)
    dye <- c(1,2,1,2)

    # specify clone-id column
    idcol <- "CLONE_ID"

    #data <- normalizeData(RG,parameter,array=array,condition=condition,dye=dye,idcol=idcol)

    ## only normalize a group of genes
    cloneid_interested <- c("250001", "250002", "250003", "250004", "250005")
    data <- normalizeData(RG,parameter,array=array,condition=condition,dye=dye,cloneid=cloneid_interested,idcol=idcol)

[Package CALIB version 1.48.0 Index]