Isoqval               package:IsoGene               R Documentation

_O_b_t_a_i_n_i_n_g _t_h_e _l_i_s_t _o_f _s_i_g_n_i_f_i_c_a_n_t _g_e_n_e_s _u_s_i_n_g _t_h_e _S_A_M _p_r_o_c_e_d_u_r_e

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

     The function obtains the list of significant genes using the SAM
     procedure for the five test statistics (the global likelihood
     test, Williams, Marcus, M, and the modified M).

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

     Isoqval(delta, allfdr, qqstat, stat)

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

   delta: the delta value as cut-off to find the number of significant
          genes

  allfdr: the delta table obtained from function Isoallfdr

  qqstat: output from function Isoqqstat containing the test statistics
          of permutations

    stat: choose one of the five test statistics to use

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

     A list of components 

     res: returns the list genes with descending q-values of the SAM
          procedure in three columns: the first column is the row
          number of the genes, the second column is the observed test
          statistic values, and the last column is the q-values

sign.list: returns the list of significant genes found by the defined
          delta value with descending p-values in three columns: the
          first column is the row number of the genes, the second
          column is the observed test statistic values, and the last
          column is the q-values

_N_o_t_e:

     This function obtains the list of significant genes using the SAM
     procedure for the five test statistics. To use the SAM procedure,
     the number of genes in the dataset is preferably larger than 500.

_A_u_t_h_o_r(_s):

     Lin et al.

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

     'isoreg', 'Isoqqstat', 'Isoallfdr', 'IsoTestSAM', 'IsoSAMPlot'

_E_x_a_m_p_l_e_s:

       set.seed(1234)
       x <- c(rep(1,3),rep(2,3),rep(3,3))
       y1 <- matrix(rnorm(9000, 1,1),1000,9) ## 1000 genes with no trends
       y2 <- matrix(c(rnorm(3000, 1,1),rnorm(3000,2,1),rnorm(3000,3,1)),1000,9) ## 1000 genes with increasing trends
       y <- data.frame(rbind(y1, y2)) ##y needs to be a data frame
       qqstat <- Isoqqstat(x, y, fudge="pooled", niter=100)
       allfdr <- Isoallfdr(qqstat, ,stat="E2")
       qval <- Isoqval(delta=0.2, allfdr, qqstat, stat="E2")

