maNormNN {nnNorm}  R Documentation 
This function normalizes a batch of cDNA arrays by removing the intensity and spatial dependent bias.
maNormNN(mbatch,binWidth=3,binHeight=3,model.nonlins=3,iterations=200,save.models=TRUE,robust=TRUE,maplots=FALSE)
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 MA plot for each slide before and after normalization.

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

Tarca, A.L.
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.
predictBias
,compNorm
,nnet
# Normalization of swirl data data(swirl) # printtip, intensity and spatial normalization of the first slide in swirl data set swirlNN<maNormNN(swirl[,1])$batchn #do not consider spatial variations, and display MA plots before and after normalization swirlNN<maNormNN(swirl[,1],binWidth=maNsc(swirl),binHeight=maNsr(swirl),maplots=TRUE)$batchn