samrocN                 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:

     samrocN(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")

_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

_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.

     samrocN calls the function Xprep which has been improved in terms
     of speed.

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

     A list with components 

       d: the statistic for each probe set

    diff: The effect estimate, e.g. the mean difference between two
          groups

      se: the standard error

      d0: the bootstrapped values on the test statistic

      p0: the proportion unchanged genes

      s0: the regularising constant

 pvalues: the p-values

       N: The optimal toplist size

  errors: the estimated sum of false positive and false negative rates
          when selcted the gene and all higher ranking ones

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

     Per Broberg

_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>

