samrocNboot               package:SAGx               R Documentation

_C_a_l_c_u_l_a_t_e _R_O_C _c_u_r_v_e _b_a_s_e_d _S_A_M _s_t_a_t_i_s_t_i_c

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

     A c-code version of samrocN. Calculation of the regularised
     t-statistic which minimises  the false positive and false negative
     rates.

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

     samrocNboot(data=M,formula=~as.factor(g), contrast=c(0,1), N = c(50, 100, 200, 300), B=100, perc = 0.6, 
     smooth=FALSE, w = 1, measure = "euclid", probeset = NULL)

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

    data: The data matrix

 formula: a linear model formula

contrast: the contrast to be estimnated 

       N: the size of top lists under consideration

       B: the number of bootstrap iterations

    perc: the largest eligible percentile of SE to be used as fudge
          factor

  smooth: if TRUE, the std will be estimated as a smooth function of
          expression level

       w: the relative weight of false positives

 measure: the goodness criterion

probeset: probeset ids;if NULL then "probeset 1", "probeset 2", ... are
          used.

_D_e_t_a_i_l_s:

     The test statistic is based on the one in Tusher et al (2001):


                         d = diff / (s_0 + s)


     where diff is a the estimate of a constrast, s_0 is the
     regularizing constant  and s the standard error.  At the heart of
     the method lies an estimate of the false negative and false
     positive rates. The test is calibrated so that these are
     minimised. For calculation of p-values a bootstrap procedure is
     invoked. Further details are given in Broberg (2003).

     The p-values are calculated through permuting the rows of the
     design matrix. NB This is not adequate for all linear models.

     samrocNboot uses C-code to speed up the bootstrap loop.

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

     An object of class samroc.result.

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

     Per Broberg and Freja Vamborg

_R_e_f_e_r_e_n_c_e_s:

     Tusher, V.G., Tibshirani, R., and Chu, G. (2001) Significance
     analysis of microarrays applied to the ionizing radiation
     response. _PNAS_ Vol. 98, no.9, pp. 5116-5121 

     Broberg, P. (2002) Ranking genes with respect to differential
     expression , <URL:
     http://genomebiology.com/2002/3/9/preprint/0007> 

     Broberg. P: Statistical methods for ranking differentially
     expressed genes. Genome Biology 2003, 4:R41 <URL: 
     http://genomebiology.com/2003/4/6/R41>

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

     library(multtest)
     #Loading required package: genefilter 
     #Loading required package: survival 
     #Loading required package: splines 
     #Loading required package: reposTools 
     data(golub)
      # This makes the expression data from Golub et al available
      # in the matrix golub, and the sample labels in the vector golub.cl
     set.seed(849867)
     samroc.res <- samrocNboot(data = golub, formula = ~as.factor(golub.cl))
     # The proportion of unchanged genes is estimated at
     samroc.res@p0
     # The fudge factor equals
      samroc.res$s0
     # A histogram of p-values
      hist(samroc.res@pvalues)
      # many genes appear changed

