deds.stat                package:DEDS                R Documentation

_D_i_f_f_e_r_e_n_t_a_i_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 _M_u_l_t_i_p_l_e _S_t_a_t_i_s_t_i_c_s

_D_e_s_c_r_i_p_t_i_o_n:

     'deds.stat' integrates different statistics of differential
     expression (DE) to rank and select a set of DE genes.

_U_s_a_g_e:

     deds.stat(X, L, B = 1000, testfun = list(t = comp.t(L), fc = comp.FC(L),
     sam = comp.SAM(L)), tail = c("abs", "lower", "higher"), distance =
     c("weuclid", "euclid"), adj = c("fdr", "adjp"), 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 observations. In
          the case of gene expression data, rows correspond to genes
          and columns to mRNA samples. The data can be read using
          'read.table'.

       L: A vector of integers corresponding to observation (column)
          class labels. For k classes, the labels must be integers
          between 0 and k-1.

       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.

 testfun: A list of functions specifying the statistics to be used to
          test the null hypothesis of no association between the
          variables and the class labels. The default uses t, fold
          change and SAM. The input can also be generated using the
          function 'deds.chooseTest'.

    tail: A character string specifying the type of rejection region.
           If 'side="abs"', two-tailed tests, the null hypothesis is
          rejected for large absolute values of the test statistic.
           If 'side="higher"', one-tailed tests, the null hypothesis is
          rejected for large values of the test statistic.
           If 'side="lower"', one-tailed tests,  the null hypothesis is
          rejected for small values of the test statistic. 

distance: A character string specifying the type of distance measure
          used for the calculation of the distance to the extreme point
          (E). 
           If 'distance="weuclid"', weighted euclidean distance, the
          weight for statistic t is 1/MAD(t); 
           If 'distance="euclid"', euclidean distance.


     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.

    nsig: If 'adj = "fdr"', 'nsig' specifies the number of top
          differentially expressed genes whose q values will be
          calculated; we recommend  setting 'nsig < m', as the
          computation of $q$ values will be extensive. $q$ values for
          the rest of genes will be approximated to 1. If 'adj =
          "adjp"', the  calculation of the adjusted p values will be
          for the whole dataset.

_D_e_t_a_i_l_s:

     'deds.stat' summarizes multiple statistical measures 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 maxima of all statistics and the
     distance from all points to the extreme is computed and 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 by permuting the data matrix.

     Statistical measures currently in the DEDS package include t
     statistics ('comp.t'), fold changes('comp.FC'), F statistics
     ('comp.F'), SAM (('comp.SAM'), moderated t ('comp.modt'),
     moderated F statistics ('comp.modF'), and B statistics ('comp.B').
     The user can also supply their own function for a statistic other
     than the above, provided the function is written in a similar
     format as the above ones.

     The function 'deds.stat' could be slow if the size of the data
     matrix and the number of permutations are big. We hence recommend
     the user to use 'deds.stat.linkC' as the default function.
     'deds.stat.linkC' interfaces to a C function, which handles a
     10,000 by 10 matrix and 1000 permutations in minutes.

     DEDS can also summarize p values from different statistical
     models, see 'deds.pval'.

_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:

     Yang, Y. H., Xiao, Y. and Segal MR: Selecting differentially
     expressed genes from microarray experiment by sets of statistics.
     _Bioinformatics_, 2004, accepted.
     http://www.biostat.ucsf.edu/jean/Papers/DEDS.pdf.

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     X <- matrix(rnorm(1000,0,0.5), nc=10)
     L <- rep(0:1,c(5,5))

     # genes 1-10 are differentially expressed
     X[1:10,6:10]<-X[1:10,6:10]+1

     # DEDS summarizing t, sam and fc
     deds.X <- deds.stat(X, L, B=200)

     # DEDS summarizing t, tmod and fc
     ## Not run: 
     deds.X <- deds.stat(X, L, testfun=list(t=comp.t(L),
     tmod=comp.modt(L), sam=comp.SAM(L)))
     ## End(Not run)

     # one can also use:
     ## Not run: 
     deds.X <- deds.stat(X, L, testfun=deds.chooseTest(L,
     tests=c("t","modt","fc")))
     ## End(Not run)

