maNormNN: Intensity and spatial normalization using robust neural...
In nnNorm: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/maNormNN.R

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

1	maNormNN(mbatch,w=NULL,binWidth=3,binHeight=3,model.nonlins=3,iterations=100,nFolds=10,maplots=FALSE,verbose=FALSE)

`mbatch`	A `marrayRaw` or `marrayNorm` batch of arrays.
`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 `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.
`nFolds`	Number of cross-validation folds. It represents the number of equal parts in which the data from a print tip is divided into: the model is trained on nFolds-1 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 `"TRUE"` will produce a M-A plot for each slide before and after normalization.
`verbose`	If set to `"TRUE"` will show the output of the nnet function which is training the neural network models.

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

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

#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)