deds.pval                package:DEDS                R Documentation

_D_i_f_f_e_r_e_n_t_i_a_l _E_x_p_r_e_s_s_i_o_n _v_i_a _D_i_s_t_a_n_c_e _S_u_m_m_a_r_y _o_f _p _V_a_l_u_e_s _f_r_o_m
_M_u_l_t_i_p_l_e _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     'deds.pval' integrates different p values of differential
     expression (DE) to rank and select a set of DE genes.

_U_s_a_g_e:

     deds.pval(X, E = rep(0, ncol(X)), adj = c("fdr", "adjp"), B = 200, nsig = nrow(X))

_A_r_g_u_m_e_n_t_s:

       X: A matrix, with m rows corresponding to variables (hypotheses)
          and n columns corresponding to p values from different
          statistical models.

       E: A numeric vector indicating the location of the most extreme
          p values in the direction of differential expression.

     adj: A character string specifying the type of multiple testing
          adjustment. 
           If 'adj="fdr"', False Discovery Rate is controled and q
          values are returned. 
           If 'adj="adjp"', ajusted p values that controls family wise
          type I error rate is returned.

       B: The number of permutations. For a complete enumeration, 'B'
          should be 0 (zero) or any number not less than the total
          number of permutations.

    nsig: A numeric variable specifying the number of top genes that
          will be returned.

_D_e_t_a_i_l_s:

     'deds.pval' summarizes p values from multiple statistical models
     for the evidence of DE. The DEDS methodology treats each gene as a
     point corresponding to a gene's vector of DE measures. An "extreme
     origin" is defined as the point that indicate DE, typically a
     vector of zero p values. The  distance from all points to the
     extreme is computed and the ranking of a gene for DE is determined
     by the closeness of the gene to the extreme. To determine a cutoff
     for declaration of DE, null referent distributions are generated
     using an approach similar to the gap statistic (see Reference
     below). DEDS can also summarize different statistics, see
     'deds.stat' and 'deds.stat.linkC'.

_V_a_l_u_e:

     An object of class 'DEDS'. See 'DEDS-class'.

_A_u_t_h_o_r(_s):

     Yuanyuan Xiao, yxiao@itsa.ucsf.edu, 
      Jean Yee Hwa Yang, jean@biostat.ucsf.edu.

_R_e_f_e_r_e_n_c_e_s:

     Tibshirani, R., Walther G., and Hastie T. (2000). Estimating the
     number of clusters in a dataset via the gap statistic. Department
     of Statistics,  Stanford University,
     http://www-stat.stanford.edu/~tibs/ftp/gap.ps

     Yang, Y.H., Xiao, Y. and Segal M.R.: Selecting differentially
     expressed genes from microarray experiment by sets of statistics.
     _Bioinformatics_ 2005 21:1084-1093.

_S_e_e _A_l_s_o:

     'deds.stat', 'deds.stat.linkC'.

_E_x_a_m_p_l_e_s:

