vsnPara               package:affyPara               R Documentation

_P_a_r_a_l_l_e_l _f_i_r _o_f _t_h_e _v_s_n _m_o_d_e_l

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

     These parallel functions fit the vsn model to intensity data in an
     AffyBatch.  They hav the same functionality than the vsn methods
     in the 'vsn' package but are implemented in parallel (and only
     supports an AffyBatch as input data).

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

     vsn2Para(object,
             phenoData = new("AnnotatedDataFrame"), cdfname = NULL,
             reference, subsample,
             ..., 
             cluster, verbose = getOption("verbose")) 

     justvsnPara(object,
                     ...,
                     cluster, verbose = getOption("verbose")) 
             
     vsnrmaPara(object,
             pmcorrect.method="pmonly", pmcorrect.param=list(),
             summary.method="medianpolish", 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.

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. 

subsample: Integer of length 1. If specified, the model parameters are
          estimated from a subsample of the data of size 'subsample'
          only, yet the fitted transformation is then applied to all
          data. For large datasets, this can substantially reduce the
          CPU time and memory consumption at a negligible loss of
          precision.

reference: Optional, a 'vsn' object from a previous fit. If this
          argument is specified, the data are normalized "towards" an
          existing set of reference arrays whose parameters are stored
          in the object 'reference'. If this argument is not specified,
          then the data are normalized "among themselves".

     ...: Further arguments that get passed and are similar to 'vsn2'.

 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") 

pmcorrect.method: The name of the PM adjustement 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 

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

     For the serial function and more details see the function 'vsn2'.

     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.

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

     An AffyBatch of normalized objects.

_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)

       AB1 <- justvsnPara(Dilution, verbose=verbose )

       stopCluster()
     }
     ## End(Not run)

