vst                   package:lumi                   R Documentation

_V_a_r_i_a_n_c_e _S_t_a_b_i_l_i_z_i_n_g _T_r_a_n_s_f_o_r_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Stabilizing the expression variance based on the bead level
     expression variance and mean relations

_U_s_a_g_e:

     vst(u, std, nSupport = min(length(u), 500), backgroundStd=NULL, fitMethod = c('linear', 'quadratic'), lowCutoff = 1/3, ifPlot = FALSE)

_A_r_g_u_m_e_n_t_s:

       u: mean expression of the beads with same sequence 

     std: expression standard deviation of the beads with same sequence 

nSupport: the number of down-sampling to speed processing 

backgroundStd: pre-estimated background standard deviation level

fitMethod: methods of fitting the relations between expression variance
          and mean relations 

lowCutoff: cutoff ratio to determine the low expression range. Do not
          change this until you now what you are doing. 

  ifPlot: plot intermediate results or not 

_D_e_t_a_i_l_s:

     The variance-stabilizing transformation (VST) takes the advantage
     of larger number of technical replicates available on the Illumina
     microarray. It models the mean-variance relationship of the
     within-array technical replicates at the bead level of Illumina
     microarray. An arcsinh transform is then applied to stabilize the
     variance. See reference for more details.

     For the methods of fitting the relations between expression
     variance and mean relations, the 'linear' method is more robust
     and provides detailed parameters for inverseVST.

_V_a_l_u_e:

     Return the transformed (variance stabilized) expression values.

_A_u_t_h_o_r(_s):

     Pan Du, Simon Lin

_R_e_f_e_r_e_n_c_e_s:

     Lin, S.M., Du, P., Kibbe, W.A.,  "Model-based Variance-stabilizing
     Transformation for Illumina Mi-croarray Data", submitted

_S_e_e _A_l_s_o:

     'lumiT', 'inverseVST'

_E_x_a_m_p_l_e_s:

     ## load example data
     data(example.lumi)

     ## get the gene expression mean for one chip
     u <- exprs(example.lumi)[,1]
     ## get the gene standard deviation for one chip
     std <- se.exprs(example.lumi)[,1]

     ## do variance stabilizing transform
     transformedU <- vst(u, std)

     ## do variance stabilizing transform with plotting intermediate result 
     transformedU <- vst(u, std, ifPlot=TRUE)

