cselection               package:Mfuzz               R Documentation

_R_e_p_e_a_t_e_d _s_o_f_t _c_l_u_s_t_e_r_i_n_g _f_o_r _d_e_t_e_c_t_i_o_n _o_f _e_m_p_t_y _c_l_u_s_t_e_r_s

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

     This function  performs repeated soft clustering for a range of
     cluster numbers c and reports the number of empty clusters
     detected.

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

     cselection(eset,m,crange=seq(4,32,4),repeats=5,visu=TRUE,...)

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

    eset: object of class _ExpressionSet_.

       m: value of fuzzy c-means parameter 'm'.

  crange: range of number of clusters 'c'.

 repeats: number of repeated clusterings.

    visu: If 'visu=TRUE' plot of number of empty clusters is produced.

     ...: additional arguments for underlying 'mfuzz'.

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

     A soft cluster is considered as empty, if none of the genes has a
     corresponding membership value larger than 0.5

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

     A matrix with  the number of empty clusters detected is generated.

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

     Matthias E. Futschik (<URL:
     http://itb.biologie.hu-berlin.de/~futschik>)

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

     M.E. Futschik and B. Charlisle, Noise robust clustering of gene
     expression time-course data, Journal of Bioinformatics and
     Computational Biology, 3 (4), 965-988, 2005

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

     if (interactive()){
     data(yeast)
     # Data pre-processing
     yeastF <- filter.NA(yeast)
     yeastF <- fill.NA(yeastF)
     yeastF <- standardise(yeastF)

     #### parameter selection
     # Empty clusters should not appear
     cl <- mfuzz(yeastF,c=20,m=1.25)
     mfuzz.plot(yeastF,cl=cl,mfrow=c(4,5))

     # Note: The following calculation might take some time

      tmp  <- cselection(yeastF,m=1.25,crange=seq(5,40,5),repeats=5,visu=TRUE)
      # derivation of number of non-empty clusters (crosses) from diagnonal
      # line  indicate appearance of empty clusters 

     # Empty clusters might appear 
     cl <- mfuzz(yeastF,c=40,m=1.25)
     mfuzz.plot(yeastF,cl=cl,mfrow=c(4,5)) 
      }

