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, model=NULL)

_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 

   model: Model used for comparison; see details and 'LMGene'.

_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. 'model' is an optional
     character string, constructed like the right-hand side of a
     formula for lm. It specifies which of the variables in the exprSet
     will be used in the model and whether interaction terms will be
     included. If model=NULL, it uses all variables from the exprSet
     without interactions. Be careful of using interaction terms with
     factors: this often leads to overfitting, which will yield an
     error.

_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(sample.mat)
     data(vlist)
     LoggedSmpd0<-neweS(lnorm(log(sample.mat)),vlist)

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

