lumiT                  package:lumi                  R Documentation

_T_r_a_n_s_f_e_r _t_h_e _I_l_l_u_m_i_n_a _d_a_t_a _t_o _s_t_a_b_i_l_i_z_e _t_h_e _v_a_r_i_a_n_c_e

_D_e_s_c_r_i_p_t_i_o_n:

     Transfer the Illumina data to stabilize the variance.

_U_s_a_g_e:

     lumiT(x.lumi, method = c("vst", 'log2', 'cubicRoot'), ifPlot = FALSE, stdCorrection = TRUE, simpleOutput = TRUE, verbose = TRUE, ...)

_A_r_g_u_m_e_n_t_s:

  x.lumi: LumiBatch object 

  method: four methods are supported: "vst", "log2", "cubicRoot" 

  ifPlot: determine whether to plot the intermediate results 

stdCorrection: determine transfer the standard error of the mean as the
          standard deviation, used for 'vst' method. 

simpleOutput: determine whether to simplify the output LumiBatch
          object, which will set the se.exprs, detection and beadNum
          slots as NULL. 

 verbose: a boolean to decide whether to print out some messages 

     ...: other parameters used by 'vst' 

_D_e_t_a_i_l_s:

     lumiT is an interface of difference variance stabilizing
     transformation. See 'vst' for details of VST (Variance Stabilizing
     Transform) of Illumina data.

     The adding of the parameter "stdCorrection" is for the value
     correction of the STDEV (or STDERR) columns when 'vst' method is
     selected. The STDEV (or STDERR) columns of the BeadStudio output
     file is the standard error of the mean of the bead intensities
     corresponding to the same probe. (Thanks Gordon Smyth kindly
     provided this information.). As the variance stabilization (see
     'vst' function) requires the information of the standard deviation
     instead of the standard error of the mean, the value correction is
     required. The corrected value will be x * sqrt(N), where x is the
     old value (standard error of the mean), N is the number of beads
     corresponding to the probe.

_V_a_l_u_e:

     Return a LumiBatch object with transformed expression values. It
     also includes the VST transform function and its parameters as
     attributes: "transformFun", "parameter". See 'inverseVST' for
     details.

_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., (2008) 'Model-based 
     Variance-stabilizing Transformation for Illumina Microarray Data',
     Nucleic Acids Res. 36, e11

_S_e_e _A_l_s_o:

     'vst'

_E_x_a_m_p_l_e_s:

     ## load example data
     data(example.lumi)

     ## Do default VST variance stabilizing transform
     lumi.T <- lumiT(example.lumi, ifPlot=TRUE)

