preprocess: Performs normalization and/or log2 transformation
In mAPKL: A Hybrid Feature Selection method for gene expression data

Description Usage Arguments Details Value Author(s) Examples

This function performs normalization and/or log2 transformation on gene expression data.

1	preprocess(exprsObj,log2=TRUE, norm="ALL", destname=NULL)

`exprsObj`	An eSet object where its assay data will be normalized
`log2`	Performs logarithmic transformation of base 2 prior to any normalzation. The default value is TRUE
`norm`	The user may define a specific normalization method rather than "ALL" which is the default case. The available abbreviations are described in the details section
`destname`	Here we define the destination path and the name of the jpeg file with the density plots. The default path is the working directory

The available normalization methods are:
Mean-centering normalization "mc"
z-score normalization "z"
Quantile normalization "q"
Cyclic loess normalization "cl"
Mean-centering normalization and log2 transformation "mcL2"
z-score normalization and log2 transformation "zL2"
Quantile normalization and log2 transformation "qL2"
Cyclic loess normalization and log2 transformation "clL2"

`rawdata`	The initial gene expression values
`mc.normdata`	The values after 'mean-centering' normalization
`z.normdata`	The values after 'z-score' normalization
`q.normdata`	The values after 'quantile' normalization
`cl.normdata`	The values after 'cyclic loess' normalization
`mcL2.normdata`	The values after 'mean-centering' normalization and log2
`zL2.normdata`	The values after 'z-score' normalization and log2
`qL2.normdata`	The values after 'quantile' normalization and log2
`clL2.normdata`	The values after 'cyclic loess' normalization and log2

Argiris Sakellariou

library(mAPKLData)
data(mAPKLData)
varLabels(mAPKLData)
breast <- sampling(Data=mAPKLData, valPercent=40, classLabels="type", seed=135)
normTrainData <- preprocess(exprsObj=breast$trainData)