stat.norm.exp              package:sma              R Documentation

_N_o_r_m_a_l_i_z_a_t_i_o_n _o_f _l_o_g _I_n_t_e_n_s_i_t_y _R_a_t_i_o_s _a_c_r_o_s_s _s_l_i_d_e_s / _e_x_p_e_r_i_m_e_n_t_s.

_D_e_s_c_r_i_p_t_i_o_n:

     Performs scale normalization across slides (experiments)

_U_s_a_g_e:

     stat.norm.exp(X)

_A_r_g_u_m_e_n_t_s:

       X: X is a matrix of log intensity ratios M=log_2 (R/G) The rows
          of X correspond to genes and columns correspond to different 
          hybridizations, that is different slides (experiments).

_V_a_l_u_e:

     A matrix of normalized log intensity ratios across different
     slides.  For the matrix in each of the components, rows correspond
     to genes and columns correspond to different hybridizations, that
     is different  slides.  This methods scale the matrix such that
     each column has the  same median absolute deviation.

_A_u_t_h_o_r(_s):

     Yee Hwa Yang, yeehwa@stat.berkeley.edu 

_R_e_f_e_r_e_n_c_e_s:

     Y. H. Yang, S. Dudoit, P. Luu and T. P. Speed.  Normalization for
     cDNA Microarray Data. (Statistics, UC Berkeley,  Tech Report #
     589).

_S_e_e _A_l_s_o:

     'ma.func', 'norm.l.func', 'norm.pin.func', 'norm.scale.func',
     'plot.mva', 'lowess'.

_E_x_a_m_p_l_e_s:

     data(MouseArray)
     ## mouse.setup <- init.grid() 
     ## mouse.data <- init.data() ## see \emph{init.data} 
     mouse.lratio <- stat.ma(mouse.data, mouse.setup)
     mouse.norm.lratio <- stat.norm.exp(mouse.lratio$M)

