IsoTestSAM              package:IsoGene              R Documentation

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_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:

     IsoTestSAM(x, y, fudge, niter, FDR, stat)

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

       x: numeric vector containing the dose levels 

       y: data frame of the gene expression with Probe ID as row names 

   fudge: option used for calculating the fudge factor in the SAM test 
          statistic, either '"pooled"' (fudge factor will be
          automatically computed in the function), or '"none"' if no
          fudge factor is used

   niter: number of permutations to use

     FDR: choose the desired FDR to control

    stat: choose one of the five test statistics to use

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

     sign.genes1: a list of genes declared significant using the SAM
     procedure in a matrix of 5 columns. The first colomn is the probe
     id, the second column is the corresponding row number of the probe
     in the dataset, and the third column is the ordered test statistic
     values, and the fourth column is the q-values of the SAM
     procedure. The last two columns are raw p-values based on
     permutations and BH adjusted p-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', 'Isofudge', 'IsoGenemSAM', 'Isoqqstat',
     'Isoallfdr','Isoqval', '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(4500, 1,1),500,9) ## 500 genes with no trends
       y2 <- matrix(c(rnorm(1500, 1,1),rnorm(1500,2,1),rnorm(1500,3,1)),500,9) ## 500 genes with increasing trends
       y <- data.frame(rbind(y1, y2)) ##y needs to be a data frame
       SAM.obj <- IsoTestSAM(x, y, fudge="pooled", niter=100, FDR=0.05, stat="E2") 

