lumiExpresso              package:lumi              R Documentation

_F_r_o_m _r_a_w _I_l_l_u_m_i_n_a _p_r_o_b_e _i_n_t_e_n_s_i_t_i_e_s _t_o _e_x_p_r_e_s_s_i_o_n _v_a_l_u_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Goes from raw Illumina probe intensities to expression values

_U_s_a_g_e:

     lumiExpresso(lumiBatch, bg.correct = TRUE, bgcorrect.param = list(method='bgAdjust'), variance.stabilize = TRUE, 
             varianceStabilize.param = list(), normalize = TRUE, normalize.param = list(), QC.evaluation = TRUE, 
             QC.param = list(), verbose = TRUE)

_A_r_g_u_m_e_n_t_s:

lumiBatch: a LumiBatch object, which can be the return of 'lumiR' 

bg.correct: a boolean to decide whether to do background correction or
          not 

bgcorrect.param: a list of parameters of 'lumiB' 

variance.stabilize: a boolean to decide whether to do variance
          stabilization or not 

varianceStabilize.param: a list of parameters of 'lumiT' 

normalize: a boolean to decide whether to do normalization or not 

normalize.param: a list of parameters of 'lumiN' 

QC.evaluation: a boolean to decide whether to do quality control
          estimation before and after preprocessing 

QC.param: a list of parameters of 'lumiQ' 

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

_D_e_t_a_i_l_s:

     The function is to encapsulate the major functions of Illumina
     preprocessing. It is organized in a similar way as the 'expresso'
     function in affy package.

_V_a_l_u_e:

     return a processed LumiBatch object. The operation history can be
     track in the history slot of the object.

_A_u_t_h_o_r(_s):

     Pan Du

_S_e_e _A_l_s_o:

     'lumiB', 'lumiT', 'lumiN'

_E_x_a_m_p_l_e_s:

     ## load example data
     data(example.lumi)

     ## Do all the default preprocessing in one step
     lumi.N <- lumiExpresso(example.lumi)

     ## Do customized preprocessing. No variance stabilizing or log transform, use Quantile normalization.
     lumi.N <- lumiExpresso(example.lumi, variance.stabilize=FALSE, normalize.param = list(method='quantile'))

