ts.pair                package:tspair                R Documentation

_C_a_l_c_u_l_a_t_i_o_n _o_f _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 pair of genes that show the maximum
     difference in ranking between two user specified groups. The
     function tspcalc() is the general version of this function that
     accepts eSets.

_U_s_a_g_e:

       ts.pair(dat,grp)

_A_r_g_u_m_e_n_t_s:

     dat: An m genes by n arrays matrix of expression data.

     grp: A group indicator with values 0 and 1 for the two groups.

_D_e_t_a_i_l_s:

     ts.pair() only works for two group classification. All the caveats
     for tspcalc() apply here, but this function only works for matrix
     and vector arguments, for a more general function see tspcalc().
     The top scoring pairs methodology was originally described in
     Geman et al. (2004). and the unique TSP idea was described in Tan
     et al. (2005).

_V_a_l_u_e:

     A tsp object with elements: 

   index: A two-column matrix where each row is a pair of indices from
          the data matrix achieving the top score

tspscore: The rank based score described in Geman et al. (2004),
          essentially the empirical average of sensitivity and
          specificity for the pair.

   score: The tie-breaking score described in Tan et al. (2005).

     grp: The binary group indicator.

  tspdat: Row i and row (i + Number of total TSPs) of this data matrix
          represent the expression data for the ith TSP pair.

  labels: The group labels for the two groups as defined by the grp
          variable.

_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.

     A.C. Tan, D.Q. Naiman, L. Xu, R.L. Winslow, D. Geman, "Simple
     decision rules for classifying human cancers from gene expression
     profiles," Bioinformatics, 21: 3896-3904, 2005.

_S_e_e _A_l_s_o:

     'tspplot', 'tspcalc', 'tspsig','predict.tsp', 'summary.tsp'

_E_x_a_m_p_l_e_s:

       ## Not run: 
       ## Load data
       data(tspdata) 
       tsp1 <- ts.pair(dat,grp)
       tsp1$index
      
     ## End(Not run)

