detectSpatialBias           package:nnNorm           R Documentation

_D_e_t_e_c_t_i_n_g _s_p_a_t_i_a_l _b_i_a_s _w_i_t_h_i_n _t_h_e _p_r_i_n_t-_t_i_p_s _o_f _a _t_w_o _c_h_a_n_n_e_l _a_r_r_a_y

_D_e_s_c_r_i_p_t_i_o_n:

     This function allows to identify in two channel batch of arrays,
     which are the print-tips where  spatial bias is present.

_U_s_a_g_e:

     detectSpatialBias(mbatch, corThreshold=0.6) 

_A_r_g_u_m_e_n_t_s:

  mbatch: A 'marrayRaw' or 'marrayNorm' batch of two channel arrays. 

corThreshold: The correlation treshold to be used.  

_D_e_t_a_i_l_s:

     This function computes two matrices: 'biasRow' and 'biasCol'. The
     elements of these  matrices represent the fraction of rows
     (columns) for which the correlation coefficient between
     log-ratios, M, and  column index (row index) is higher than a user
     specified treshold (default corThreshold=0.6). The idea here is to
     see in which print-tip a important fraction of the rows (columns)
     are highly correlated with the  column (row) index. Since some
     rows (columns) will show positive correlation while the other
     negative correlation, we  are only interested in a sigle direction
     of the correlation, i.e. either positive or negative.

_V_a_l_u_e:

     This function returns a list with two matrices. 'biasRow' and
     'biasCol'. The rows of these matrices correspond to the print tips
     counted metaRow wise, and the columns correspond to arrays. Values
     in these matrices superior to 33  point to print-tips that have
     more tha a third of the rows (columns) with important spatial
     bias.

_A_u_t_h_o_r(_s):

     Tarca, A.L.

_R_e_f_e_r_e_n_c_e_s:

     A robust neural networks approach for spatial and intensity
     dependent normalization of cDNA microarray data, Adi. L. Tarca ,
     Janice. E. K. Cooke, and John Mackay, Bioinformatics, 21, 2005,
     2674 - 2683.

_S_e_e _A_l_s_o:

     'maNormNN'

_E_x_a_m_p_l_e_s:

     # detecting spatial bias in swirl data
     data(swirl)
     # print-tip, intensity and spatial normalization of the first slide in swirl data set
     myres<-detectSpatialBias(swirl)   

