maNormNN {nnNorm}R Documentation

Intensity and spatial normalization using robust neural networks fitting

Description

This function normalizes a batch of cDNA arrays by removing the intensity and spatial dependent bias.

Usage

maNormNN(mbatch,binWidth=3,binHeight=3,model.nonlins=3,iterations=200,save.models=TRUE,robust=TRUE,maplots=FALSE) 

Arguments

mbatch A marrayRaw or marrayNorm batch of arrays.
binWidth Width of the bins in the X direction (spot column) in which the print tip will be divided in order to account for spatial variation. Max value is maNsc(mbatch), Min value is 1. However if it is set to a number larger than maNsc(mbatch)/2 (so less than two bins in X direction) the variable X will not be used as predictor to estimate the bias.
binHeight Height of the bins in the Y direction (spot row)in which the print tip will be divided in order to account for spatial variation. Max value is maNsr(mbatch), Min value is 1. However if it is set to a number larger than maNsr(mbatch)/2 (so less than two bins in Y direction) the variable Y will not be used as predictor to estimate the bias.
model.nonlins Number of nodes in the hidden layer of the neural network model.
iterations The number of iterations at which (if not converged) the training of the neural net will be stopped.
save.models If set to "TRUE" will enable storage of the models (parameters and ranges of aplicability) in the component models of the list that this function returns.
robust If set to "TRUE", each spot will be assigned a weight in the model identification, providing resistance to outliers.
maplots If set to "TRUE" will produce a M-A plot for each slide before and after normalization.

Details

This function uses neural networks to model the bias in cDNA data sets.

Value

A list with components:

batchn A marrayNorm object containing the normalized log ratios. See marrayNorm class for details
models A list containing the parameters and ranges of aplicability for the models. This component is to be used only as a argument to the function predictBias.

Author(s)

Tarca, A.L.

References

A. L. Tarca, J. E. K. Cooke, and J. Mackay. Robust neural networks approach for spatial and intensity dependent normalization of cDNA data. Bioinformatics. 2004,submitted.

See Also

predictBias,compNorm,nnet

Examples

# Normalization of swirl data
data(swirl)
# print-tip, intensity and spatial normalization of the first slide in swirl data set
swirlNN<-maNormNN(swirl[,1])$batchn    

#do not consider spatial variations, and display M-A plots before and after normalization
swirlNN<-maNormNN(swirl[,1],binWidth=maNsc(swirl),binHeight=maNsr(swirl),maplots=TRUE)$batchn    


[Package nnNorm version 1.0.1 Index]