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,binWidth=3,binHeight=3,model.nonlins=3,iterations=200,robust=TRUE,maplots=FALSE,verbose=FALSE) 

_A_r_g_u_m_e_n_t_s:

  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. 

  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.  

 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)  

