This function normalizes a batch of cDNA arrays by removing the intensity and spatial dependent bias.
1 
mbatch 
A 
w 
Weights to be assigned to each spot. If provided, it should be a vector with the same length as maNspots(mbatch). 
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 
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 
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. 
nFolds 
Number of crossvalidation folds. It represents the number of equal parts in which the data from a print tip is divided into: the model is trained on nFolds1 parts and the bias is estimated for one part at the time. Higher values improve the results but increase the computation time. Ideal values are between 5 and 10. 
maplots 
If set to 
verbose 
If set to 
This function uses neural networks to model the bias in cDNA data sets.
A marrayNorm
object containing the normalized log ratios. See marrayNorm
class for details
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.
compNorm
,nnet
1 2 3 4 5 6 7  # Normalization of swirl data
data(swirl)
# printtip, intensity and spatial normalization of the first slide in swirl data set
swirlNN<maNormNN(swirl[,1])
#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)

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