tspsig                package:tspair                R Documentation

_S_i_g_n_i_f_i_c_a_n_c_e _c_a_l_c_u_l_a_t_i_o_n _f_o_r _t_o_p _s_c_o_r_i_n_g _p_a_i_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function calculates the significance of a top-scoring pair.
     It can be run after tspcalc() to calculate how strong a TSP is.

_U_s_a_g_e:

       tspsig(dat,grp,B=50,seed=NULL)

_A_r_g_u_m_e_n_t_s:

     dat: Can take two values: (a) an m genes by n arrays matrix of
          expression data or (b) an eSet object

     grp: Can take one of two values: (a) A group indicator incharacter
          or numeric form, (b) an integer indicating the column of
          pData(dat) to use as the group indicator

       B: The number of permutations to perform in calculation of the
          p-value, default is 50.

    seed: If this is a numeric argument, the seed will be set for
          reproducible p-values.

_D_e_t_a_i_l_s:

     tspsig() only works for two group classification. The computation
     time grows rapidly in the number of  genes, so for large gene
     expression matrices one should be prepared to wait or do a
     pre-filtering step. A progress bar is shown which gives some
     indication of the time until the calculation is complete. The top
     scoring pairs methodology was originally described in Geman et al.
     (2004).

_V_a_l_u_e:

       p: A p-value for testing the null hypothesis that there is no
          TSP for the data set dat.

nullscores: The null TSP scores from the permutation test.

_A_u_t_h_o_r(_s):

     Jeffrey T. Leek jtleek@jhu.edu

_R_e_f_e_r_e_n_c_e_s:

     D. Geman, C. d'Avignon, D. Naiman and R. Winslow, "Classifying
     gene expression profiles from pairwise mRNA comparisons," 
     Statist. Appl. in Genetics and Molecular Biology, 3, 2004.

_S_e_e _A_l_s_o:

     'tspplot', 'ts.pair', 'tspcalc','predict.tsp', 'summary.tsp'

_E_x_a_m_p_l_e_s:

       ## Not run: 
       ## Load data
       data(tspdata) 

       ## Run tspcalc() on a data matrix and grp vector
       tsp1 <- tspcalc(dat,grp)

       ## Run tspsig() to get a p-value
       p <- tspsig(dat,grp)
       p
      ## End(Not run)

