genediff               package:LMGene               R Documentation

_R_a_w _p-_v_a_l_u_e _c_a_l_c_u_l_a_t_i_o_n _f_u_n_c_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Computes two vectors of p-values per gene or probe using
     gene-by-gene anova with individual gene MSE using  both the
     gene-specific MSE and the posterior mean MSE for each term in the
     anova.  
      Assumes a fixed effects model and the correct denominator for all
     comparisons is the MSE

_U_s_a_g_e:

     genediff(eS)

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

      eS: Array data. must be a exprSet type and the log-transformation
          and the normalization of exprSet@exprs are recommended 

_D_e_t_a_i_l_s:

     The input argument, eS, must be exprSet type from Biobase package.
      If you have a matrix data and information about the considered
     factors, then you can use 'neweS' to conver the data into exprSet.
     Please see 'neweS' in more detail.

_V_a_l_u_e:

 pvlist : a list containing two sets of p-values obtained by gene
          specific MSE and the posterior MSE methods

_A_u_t_h_o_r(_s):

     David Rocke and Geun-Cheol Lee

_R_e_f_e_r_e_n_c_e_s:

     David M. Rocke (2004), Design and analysis of experiments with
     high throughput biological assay data, Seminars in Cell &
     Developmental Biology, 15, 703-713. 

     <URL: http://www.idav.ucdavis.edu/~dmrocke/>

_S_e_e _A_l_s_o:

     'LMGene', 'rowaov'

_E_x_a_m_p_l_e_s:

     #library
     library(Biobase)
     library(LMGene)

     #data
     data(Smpd)
     data(vlist)
     LoggedSmpd0<-neweS(lnorm(log(Smpd)),vlist)

     pvlist<-genediff(LoggedSmpd0)
     pvlist$Posterior[1:5,]

