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 M-A 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) # 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