mfuzz                 package:Mfuzz                 R Documentation

_F_u_n_c_t_i_o_n _f_o_r _s_o_f_t _c_l_u_s_t_e_r_i_n_g _b_a_s_e_d _o_n _f_u_z_z_y _c-_m_e_a_n_s.

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

     This function is a wrapper function for 'cmeans' of the 'e1071'
     package.  It performs soft clustering of genes based on their
     exprssion values using the fuzzy c-means algorithm.

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

     mfuzz(eset,centers,m,...)

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

    eset: object of the class "exprSet"

 centers: number of clusters

       m: fuzzification parameter

     ...: additional parameters for 'cmeans'

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

     This function is the core function for soft clustering. It groups
     genes based on the Euclidean distance and the c-means objective
     function which is a weighted square error function. Each gene is
     assigned a membership value between 0 and 1 for each cluster.
     Hence, genes can be assigned to different clusters in a gradual
     manner.  This contrasts hard clustering where each gene can
     belongs to a single cluster.

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

     An object of class 'flcust' (see 'cmeans') which is a list with
     components: 

 centers: the final cluster centers.

    size: the number of data points in each cluster of the closest hard
          clustering.

 cluster: a vector of integers containing the indices of the clusters
          where the data points are assigned to for the closest hard
          clustering, as obtained by assigning points to the (first)
          class with maximal membership.

    iter: the number of iterations performed.

membership: a matrix with the membership values of the data points to
          the clusters.

withinerror: the value of the objective function.

    call: the call used to create the object.

_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

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

     'cmeans'

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

     if (interactive()){
     data(yeast)
     # Data pre-processing
     yeastF <- filter.NA(yeast)
     yeastF <- fill.NA(yeastF) # for illustration only; rather use knn method
     yeastF <- standardise(yeastF)

     # Soft clustering and visualisation
     cl <- mfuzz(yeastF,c=20,m=1.25)
     mfuzz.plot(yeastF,cl=cl,mfrow=c(2,2))

     # Plotting center of cluster 1 
     X11(); plot(cl[[1]][1,],type="l",ylab="Expression") 

     # Getting the membership values for the first 10 genes in cluster 1
     cl[[4]][1:10,1] 
     }

