ncclust                package:GeneNT                R Documentation

_N_e_t_w_o_r_k _c_o_n_s_t_r_a_i_n_e_d _c_l_u_s_t_e_r_i_n_g

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

     This function does network constrained clustering based on
     Floyd-Warshall algorithm, R function allshortestpaths()in R
     packpage e1071.

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

     ncclust(p, pG2, kG2)

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

       p: p is the exponential tuning factor with default value 1. it
          can also be set to other intergers whenever necessary.

     pG2: pG2 is the gene pairs that are screened using the two-stage
          algorithm based on Pearson correlation statistic.

     kG2: kG2 is the gene pairs that are screened using the two-stage
          algorithm based on Kendall correlation statistic.

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

     This function is written in comparison to the traditional
     clustering implemented as tdclust().

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

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

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

     Dongxiao Zhu (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_. Zhu, D., Hero, A.O., Hong, C., Khanna, R.,
     and Swaroop A. Network constrained clustering for gene microarray
     data. {\it Submitted}

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

     'tdclust'

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

     # load GeneNT and GeneTS library
     library(GeneTS)
     library(GeneNT)
     library(e1071)

     #EITHER use the internal 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"
     #use (FDR, MAS) criteria (0.2, 0.5) as example to screen gene pairs
     #g1 <- corfdrci(0.2, 0.5)  
     #pG1 <- g1$pG1
     #pG2 contains gene pairs that passed two-stage screening
     #pG2 <- g1$pG2  
     #use (FDR, MAS) criteria (0.2, 0.5) as example to screen gene pairs 
     #g2 <- kendallfdrci(0.2, 0.5) 
     #kG1 <- g2$kG1
     #kG2 contains gene pairs that passed two-stage screening
     #kG2 <- g2$kG2 
     #generate Pajek compatible matrix to visualize network
     #getBM(pG2, kG2) 
     #clustering from network using network constraint clustering
     #ncclust(3, pG2, kG2) 

