geneclus                package:gaga                R Documentation

_C_l_u_s_t_e_r _g_e_n_e_s _i_n_t_o _e_x_p_r_e_s_s_i_o_n _p_a_t_t_e_r_n_s.

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

     Performs supervised gene clustering. Clusters genes into the
     expression pattern with highest posterior probability, according
     to a GaGa or MiGaGa fit.

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

     geneclus(gg.fit, method='posprob')

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

  gg.fit: GaGa or MiGaGa fit (object of type 'gagafit', as returned by
          'fitGG'). 

  method: For 'method==1' samples are assigned to pattern with highest
          posterior probability, and for 'method==1' to the pattern
          with highest  likelihood (e.g. assuming equal a priori prob
          for all patterns)

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

     Each gene is assigned to the pattern with highest posterior
     probability.   This is similar to routine 'findgenes', which also
     assigns genes to the pattern with highest posterior probability,
     although 'findgenes' applies an FDR-based correction i.e. tends to
     assign more genes to the null pattern of no differential
     expression.

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

     List with components: 

      d : Vector indicating the pattern that each gene is assigned to.

posprob : Vector with posterior probabilities of the assigned patterns.

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

     David Rossell

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

     Rossell D. GaGa: a simple and  flexible hierarchical model for
     microarray data analysis. <URL:
     http://rosselldavid.googlepages.com>.

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

     'fitGG', 'parest'

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

     #Not run. Example from the help manual
     #library(gaga)
     #set.seed(10)
     #n <- 100; m <- c(6,6)
     #a0 <- 25.5; nu <- 0.109
     #balpha <- 1.183; nualpha <- 1683
     #probpat <- c(.95,.05)
     #xsim <- simGG(n,m,p.de=probpat[2],a0,nu,balpha,nualpha)
     #
     #ggfit <- fitGG(xsim$x[,c(-6,-12)],groups,patterns=patterns,nclust=1)
     #ggfit <- parest(ggfit,x=xsim$x[,c(-6,-12)],groups,burnin=100,alpha=.05)
     #
     #dclus <- geneclus(ggfit)  #not use FDR correction
     #dfdr <- findgenes(ggfit,xsim$x[,c(-6,-12)],groups,fdrmax=.05,parametric=TRUE) #use FDR correction
     #table(dfdr$d,dclus$d) #compare results

