stat.ChurSap               package:sma               R Documentation

_A_p_p_l_y _S_a_p_i_r _a_n_d _C_h_u_r_c_h_i_l_l_s _s_i_n_g_l_e _s_l_i_d_e _m_e_t_h_o_d

_D_e_s_c_r_i_p_t_i_o_n:

     Applies Sapirs and Churchills single slide method.

_U_s_a_g_e:

     stat.ChurSap(RG,layout,pp=0.95,norm="p", pout=TRUE, image.id=1,...)

_A_r_g_u_m_e_n_t_s:

      RG: a list with 4 elements, each represents a matrix with p rows
          for p genes and n columns for n slides. 
           The first element "R" contains the raw red intensities from
          slide i=1,...,n .
           Similarly, the second element "G" contains the raw green
          intensities. 
           The third element "Rb"  contains the background red
          intensities and 
           the fourth element "Gb" contains the  background green
          intensities.
           This list structure can be generated by the interactive
          function 'init.data'. 

  layout: a list specifying the dimensions of the spot matrix and the
          grid matrix.  This can be generated by calling 'init.grid'.

      pp: Posterior probability of being differentially expressed.
          Defaults to 0.95

image.id: Specifies image to which Chen's method will be applied.

    norm: Character string, one of "n", "m", "l", "p" or "s".  This
          argument specifies the type of normalization method to be
          performed: "n" no normalization between the 2 channels; "m"
          'median' normalization, which sets the median of log
          intensity ratios to zero; "l" global 'lowess' normalization;
          "p" print-tip group lowess normalization and "s" scaled
          print-tip group lowess normalization. The default method is
          set to print-tip normalization.

    pout: if TRUE, an M vs. A plot will be produced with limits due to
          Churchill and Sapir at the specified posterior probability
          level. If FALSE, return a list with pertinant information

     ...: additional graphical parameters

_V_a_l_u_e:

     List containing the following components:

  limits: the positive value of the limit at the posterior probability
          value of pp

   theta: parameters estimated by EM algorithm, specifically a mixing
          proportion and a variance

      pp: Posterior probabilities of being differentially expressed
          given observed data for each gene.

_A_u_t_h_o_r(_s):

     Ben Bolstad, bolstad@stat.berkeley.edu

_R_e_f_e_r_e_n_c_e_s:

     Sapir and Churchill(2000), Estimating the posterior probability of
     differential gene expression from microarray data . <URL:
     http://www.jax.org/research/churchill/>

_S_e_e _A_l_s_o:

     'stat.Chen','stat.Newton'

_E_x_a_m_p_l_e_s:

     data(MouseArray)
     ##mouse.setup <- init.grid() 
     ##mouse.data <- init.data() ## see \emph{init.data}
     stat.ChurSap(mouse.data,mouse.setup,pp=0.95,image.id=3)

