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.
Details
This function uses neural networks to model the bias in cDNA data sets.
Value
A marrayNorm object containing the normalized log ratios. See marrayNorm
class for details
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
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])
#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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(nnNorm)
Loading required package: marray
Loading required package: limma
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/nnNorm/maNormNN.Rd_%03d_medium.png", width=480, height=480)
> ### Name: maNormNN
> ### Title: Intensity and spatial normalization using robust neural networks
> ### fitting
> ### Aliases: maNormNN
> ### Keywords: smooth robust
>
> ### ** 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])
Processing array 1 of 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)
binWidth being too large or NULL, the space coordinate X was not used in normalization
binHeight being too large or NULL, the space coordinate Y was not used in normalization
Processing array 1 of 1
****************>
>
>
>
>
>
> dev.off()
null device
1
>