preproPara             package:affyPara             R Documentation

_P_a_r_a_l_l_e_l_i_z_e_d _p_r_e_p_r_o_c_e_s_s_i_n_g

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

     Parallelized preprocessing function, which goes from raw probe
     intensities to expression values in three steps: Background
     correction, normalization and summarization

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

     preproPara(object,
         bgcorrect = TRUE, bgcorrect.method = NULL, bgcorrect.param = list(),
         normalize = TRUE, normalize.method = NULL, normalize.param = list(),
         pmcorrect.method = NULL, pmcorrect.param = list(),
         summary.method = NULL, summary.param = list(),
         ids = NULL,phenoData = new("AnnotatedDataFrame"), cdfname = NULL,
         cluster, verbose = getOption("verbose"))

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

  object: An object of class AffyBatch  OR a 'character' vector with
          the names of CEL files  OR a (partitioned) list of
          'character' vectors with CEL file names.

bgcorrect: A boolean to express whether background correction is wanted
          or not. 

bgcorrect.method: The name of the background adjustment method to use. 

bgcorrect.param: A list of parameters for 'bgcorrect.method' (if
          needed/wanted) 

normalize: A boolean to express whether normalization is wanted or not. 

normalize.method: The name of the normalization method to use. 

normalize.param: A list of parameters to be passed to the normalization
          method (if wanted). 

pmcorrect.method: The name of the PM adjustment method. 

pmcorrect.param: A list of parameters for 'pmcorrect.method' (if
          needed/wanted). 

summary.method: The method used for the computation of expression
          values 

summary.param: A list of parameters to be passed to the
          'summary.method' (if wanted). 

     ids: List of 'ids' for summarization 

phenoData: An AnnotatedDataFrame object. 

 cdfname: Used to specify the name of an alternative cdf package. If
          set to 'NULL',  the usual cdf package based on Affymetrix'
          mappings will be used. 

 cluster: A cluster object obtained from the function makeCluster in
          the SNOW package.  For default '.affyParaInternalEnv$cl' will
          be used. 

 verbose: A logical value. If 'TRUE' it writes out some messages.
          default: getOption("verbose") 

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

     Parallelized preprocessing function, which goes from raw probe
     intensities to expression values in three steps:  Background
     correction, normalization and summarization

     For the serial function and more details see the function
     'expresso'.

     For using this function a computer cluster using the SNOW package
     has to be started.  Starting the cluster with the command
     'makeCluster' generates an cluster object in the affyPara
     environment (.affyParaInternalEnv) and  no cluster object in the
     global environment. The cluster object in the affyPara environment
     will be used as default cluster object,  therefore no more cluster
     object handling is required.   The 'makeXXXcluster' functions from
     the package SNOW can be used to create an cluster object in the
     global environment and  to use it for the preprocessing functions.

     Available methods:

     '_b_g_c_o_r_r_e_c_t._m_e_t_h_o_d': see 'bgcorrect.methods()'

     '_n_o_r_m_a_l_i_z_e._m_e_t_h_o_d': 'quantil', 'constant', 'invariantset','loess'

     '_s_u_m_m_a_r_y._m_e_t_h_o_d': see 'generateExprSet.methods()'

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

     An object of class ExpressionSet.

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

     Markus Schmidberger schmidb@ibe.med.uni-muenchen.de, Ulrich
     Mansmann mansmann@ibe.med.uni-muenchen.de

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

     ## Not run: 
     library(affyPara)
     if (require(affydata)) {
       data(Dilution)

       makeCluster(3)

       esset <- preproPara(Dilution,
         bgcorrect = TRUE, bgcorrect.method = "rma2",
         normalize = TRUE, normalize.method = "quantil",
         pmcorrect.method = "pmonly",
         summary.method = "avgdiff",
         verbose = TRUE)
         
       stopCluster()
     }
     ## End(Not run)

