r2xcluster                package:ctc                R Documentation

_W_r_i_t_e _t_o _X_c_l_u_s_t_e_r _f_i_l_e _f_o_r_m_a_t

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

     Converting data to Xcluster format

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

     r2xcluster(data,labels=FALSE,description=FALSE,file="xcluster.txt")

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

    file: the path of the file

    data: a matrix (or data frame) which provides the data to put into
          the file

  labels: a logical value indicating whether we use the frist column as
          labels (NAME column for xcluster file)

description: a logical value indicating whether we use the second
          column as description (DESCRIPTION column for cluster file)

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

     Software _Xcluster_, made by _G. Sherlock_ needs formatted input
     data like:


     NAME    DESCRIPTION     GWEIGHT V2      V3      V4
     EWEIGHT                         1       1       1
     gbk01   Gene1           1       0.9     0.4     1.4
     gbk02   Gene2           1       0.6     0.2     0.2
     gbk03   Gene3           1       1.6     1.1     0.9
     gbk04   Gene4           1       0.4     1       1

     Line begining with 'EWEIGHT' gives weights for each column
     (variable). Column 'GWEIGHT' gives weights for each line
     (individuals).

_N_o_t_e:

     _Xcluster_ is a C program made by _Gavin Sherlock_ that performs
     hierarchical clustering, K-means and SOM. 

     _Xcluster_ is copyrighted.  To get or have information about
     _Xcluster_: <URL:
     http://genome-www.stanford.edu/~sherlock/cluster.html>

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

     Antoine Lucas, <URL: http://antoinelucas.free.fr/ctc>

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

     Antoine Lucas and Sylvain Jasson, _Using amap and ctc Packages for
     Huge Clustering_, R News, 2006, vol 6, issue 5 pages 58-60.

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

     'xcluster', 'xcluster2r', 'hclust', 'hcluster'

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

     ##    Create data
     .Random.seed <- c(1,  416884367 ,1051235439)
     m <- matrix(rep(1,3*24),ncol=3)  
     m[9:16,3] <- 3 ; m[17:24,] <- 3    #create 3 groups
     m <- m+rnorm(24*3,0,0.5)           #add noise
     m <- floor(10*m)/10                #just one digits

     r2xcluster(m)

     ## And once you have Xcluster program:

     ## Not run: 
       system('Xcluster -f xcluster.txt -e 0 -p 0 -s 0 -l 0')
       h <- xcluster2r('xcluster.gtr')
       plot(h,hang=-1)
       
     ## End(Not run)

