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)

