IsoSAMPlot              package:IsoGene              R Documentation

_P_l_o_t_s _p_r_o_d_u_c_e_d _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 produces four plots using the SAM procedure for one
     of the five test statistics (the likelihood ratio test, Williams,
     Marcus, the M and modified M tests): FDR vs. delta, number of
     significant genes vs. delta, number of false positives vs. delta,
     and the observed vs. expected SAM test statistics obtained from
     permutations.

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

     IsoSAMPlot(qqstat, allfdr, FDR, stat)

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

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

  allfdr: the delta table obtained from function Isoallfdr

     FDR: choose the desired FDR to control

    stat: choose one of the five test statistics to use

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

     returns four plots produced using the SAM procedure.

_N_o_t_e:

     This function produces four plots 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','Isoqval', 'IsoTestSAM'

_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")
       IsoSAMPlot(qqstat, allfdr, FDR = 0.1, stat = "E2")

