# Intensity and spatial normalization using robust neural networks fitting

### Description

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

### Usage

1 |

### Arguments

`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 |

`binHeight` |
Height of the bins in the |

`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 |

`verbose` |
If set to |

### 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

1 2 3 4 5 6 7 | ```
# 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)
``` |