sagmbSimulateData            package:vsn            R Documentation

_S_i_m_u_l_a_t_e _d_a_t_a _a_n_d _a_s_s_e_s_s _v_s_n'_s _p_a_r_a_m_e_t_e_r _e_s_t_i_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Functions to validate and assess the performance of vsn through
     simulation of data.

_U_s_a_g_e:

     sagmbSimulateData(n=8064, d=2, de=0, up=0.5)
     sagmbAssess(h1, sim)

_A_r_g_u_m_e_n_t_s:

       n: Numeric. Number of probes (rows).

       d: Numeric. Number of arrays (columns).

      de: Numeric. Fraction of differentially expressed genes.

      up: Numeric. Fraction of up-regulated genes among the
          differentially expressed genes.

      h1: Matrix. Calibrated and transformed data, according, e.g., to
          vsn

     sim: List. The output of a previous call to 'sagmbSimulateData',
          see Value

_D_e_t_a_i_l_s:

     Please see the vignette.

_V_a_l_u_e:

     For 'sagmbSimulateData', a list with three components: 'hy', an 'n
     x d' matrix with the true (=simulated) calibrated, transformed
     data; 'y', an 'n x d' matrix with the simulated uncalibrated raw
     data - this is intended to be fed into 'vsn'; 'is.de', a logical
     vector of length 'n', specifying which probes are simulated to be
     differentially expressed.

     For 'sagmbSimulateData', a number: the root mean squared
     difference between true and estimated transformed data.

_A_u_t_h_o_r(_s):

     Wolfgang Huber <URL: http://www.dkfz.de/mga/whuber>

_R_e_f_e_r_e_n_c_e_s:

     Wolfgang Huber, Anja von Heydebreck, Holger Sueltmann, Annemarie
     Poustka, and Martin Vingron (2003) "Parameter estimation for the
     calibration and variance stabilization of microarray data",
     Statistical Applications in Genetics and Molecular Biology: Vol.
     2: No. 1, Article 3. http://www.bepress.com/sagmb/vol2/iss1/art3

_S_e_e _A_l_s_o:

     'vsn'

_E_x_a_m_p_l_e_s:

       sim <- sagmbSimulateData()
       ny  <- vsn(sim$y)
       res <- sagmbAssess(exprs(ny), sim)
       res

