inverseVST               package:lumi               R Documentation

_I_n_v_e_r_s_e _V_S_T _t_r_a_n_s_f_o_r_m

_D_e_s_c_r_i_p_t_i_o_n:

     Inverse transform of VST (variance stabilizing transform), see
     'vst'.

_U_s_a_g_e:

     inverseVST(x, fun = c('asinh', 'log'), parameter)

_A_r_g_u_m_e_n_t_s:

       x: a VST transformed LumiBatch object or a numeric matrix or
          vector

     fun: function used in VST transform 

parameter: parameter of VST function 

_D_e_t_a_i_l_s:

     Recover the raw data from VST transformed data returned by 'vst'.
     This function can be directly applied to the VST transformed or
     VST + RSN normalized LumiBatch object to reverse transform the
     data to the original scale.

_V_a_l_u_e:

     Return the raw data before VST transform

_A_u_t_h_o_r(_s):

     Pan Du

_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:

     'vst'

_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 inverse transform and recover the raw data
     parameter <- attr(transformedU, 'parameter')
     transformFun <- attr(transformedU, 'transformFun')
     recoveredU <- inverseVST(transformedU, fun=transformFun, parameter=parameter)

     ## compare with the raw data
     print(u[1:5])
     print(recoveredU[1:5])

     ## do inverse transform of the VST + RSN processed data
     lumi.N <- lumiExpresso(example.lumi[,1:2])
     ## Inverse transform. 
     ## Note: as the normalization is involved, the processed data will be different from the raw data.
     lumi.N.raw <- inverseVST(lumi.N)

