LMGene                package:LMGene                R Documentation

_L_M_G_e_n_e _m_a_i_n _f_u_n_c_t_i_o_n

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

     LMGene calls function 'genediff' to calculate the raw p-values of
     all genes and then calls function 'pvadjust' to calculate the
     adjusted p-values of all genes. Finally, calls function 'rowlist'
     to list the names of genes that are selected as significant under
     the specified significance level.

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

     LMGene(eS, level = 0.05)

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

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

   level: Significance level 

_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. 
      'level' indicates False Discovery Rate. e.g.) level 0.05 means 5

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

  lmres : A list which contains significant gene names for each
          considered factor

_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:

     'genediff', 'pvadjust', 'rowlist'

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

     #library
     library(Biobase)
     library(LMGene)

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

     siggeneslist<-LMGene(LoggedSmpd0, 0.01)
     siggeneslist$x3

