kendallfdrci             package:GeneNT             R Documentation

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

     This function implement the two-stage screening procedure based on
     Kendall correlation coefficient. Specifying a pair of FDR and MAS
     criteria, the algorithm provides an initial co-expression
     discovery that controls only FDR, which is then followed by a
     second stage co-expression discovery which controls both FDR and
     MAS.

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

     kendallfdrci(Q, cormin)

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

       Q: The significant level

  cormin: The specified minimum acceptable strength of association
          measured using Kendall correlation coefficient

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

     The data matrix file must be in the right format. The first row
     must be one shorter than the rest rows. The first column must be
     gene names.

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

     The function returns a list of gene pairs that satisfies the FDR
     and MAS criteria simultaneously measured by Kendall correlation
     coefficient.  

    kG1 : The gene pairs that passes Stage I (FDR only) screening

    kG2 : The gene pairs that passed both Stage I (FDR) and II (MAS)
          screenings

_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).
     _Submitted_.

     Hollander M. and Wolfe D.A. (1999). Nonparametric Statistical
     Methods, New York: Wiley.

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

     'corfdrci'

_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$pG1
     #pG2 is the dataset containing gene pairs that passed two-stage screening
     #pG2 <- g1$pG2  
     #use (FDR, MAS) criteria (0.2, 0.5) as example to screen gene pairs 
     #g2 <- kendallfdrci(0.2, 0.5) 
     #kG1 <- g2$kG1
     #kG2 is the dataset containing gene pairs that passed two-stage screening
     #kG2 <- g2$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) 

