maNormNN               package:nnNorm               R Documentation

_I_n_t_e_n_s_i_t_y _a_n_d _s_p_a_t_i_a_l _n_o_r_m_a_l_i_z_a_t_i_o_n _u_s_i_n_g _r_o_b_u_s_t _n_e_u_r_a_l _n_e_t_w_o_r_k_s _f_i_t_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

  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.  

_D_e_t_a_i_l_s:

     This function uses neural networks to model the bias in cDNA data
     sets.

_V_a_l_u_e:

     A 'marrayNorm' object containing the normalized log ratios. See
     'marrayNorm'  class for details

_A_u_t_h_o_r(_s):

     Tarca, A.L.

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

     'compNorm','nnet'

_E_x_a_m_p_l_e_s:

     # 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)  

