getBM                 package:GeneNT                 R Documentation

_G_e_n_e_r_a_t_e _P_a_j_e_k _c_o_m_p_a_t_i_b_l_e _m_a_t_r_i_x _f_r_o_m _s_c_r_e_e_n_e_d _g_e_n_e _p_a_i_r_s

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

     This function takes inputs of screened gene pairs based on BOTH
     Pearson correlation and Kendall correlation statistics, and
     generates Pajek compatible Boolean matrix that can be visulized
     using network visulization software such as Pajek.

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

     getBM(pG2, kG2)

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

     pG2: Gene pairs that passed two-stage screening based on Pearson
          correlation coefficient.

     kG2: Gene pairs that passed two-stage screening based on Kendall
          correlation coefficient.

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

     A Pajek compatible Boolean matrix "BMPajek.mat" and a R Boolean
     matrix "BM.tsv" will be exported to the working directory.

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

     "BM.tsv" (R format Boolean matrix) and "BMPajek.mat" (Pajek format
     Boolean matrix) will be returned.

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

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

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

     'row.names'

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

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

     #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"
     #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  
     #g2 <- kendallfdrci(0.2, 0.5) #use (FDR, MAS) criteria (0.2, 0.5) as example to screen gene pairs 
     #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, for example. p=3.
     #spclust(3, pG2, kG2) 

