tdclust                package:GeneNT                R Documentation

_T_r_a_d_i_t_i_o_n_a_l _c_l_u_s_t_e_r_i_n_g

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

     This function does traditional clustering based on core R function
     hclust().

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

     tdclust(p)

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

       p: p is the exponential tuning factor. The default value is 1,
          and it can be set to other positive integers when necessary.

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

     This function is written in comparison to the network constrained
     clustering implemented as ncclust().

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

     This function returns a distance matrix that can be used by any
     distance based clustering software.

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

     Dongxiao Zhu (<URL: http://www-personal.umich.edu/~zhud>)

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

     Zhu, D., Hero, A.O., Qin, Z.S. and Swaroop, A. High throughput
     screening of co-expressed gene pairs with controlled False
     Discovery Rate (FDR) and Minimum Acceptable Strength (MAS). _J.
     Comput. Biol, in press_.

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

     'ncclust'

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

     # load GeneNT and GeneTS library
     library(GeneTS)
     library(GeneNT)

     #EITHER use the example dataset
     data(dat) 
     #OR use the following if you want to import external data 
     #dat <- read.table("gal.txt", h = T, row.names = 1) 
     #Note, data matrix name has to be "dat"
     #Regular hierarchical clustering, for example, p = 3.
     #tdclust(3)

