CALIB (version 1.38.0)

normalizeData: 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.

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.

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.

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)