normalizeAffyBatchLoessIterPara   package:affyPara   R Documentation

_P_a_r_a_l_l_e_l_i_z_e_d _p_a_r_t_i_a_l _l_o_e_s_s _n_o_r_m_a_l_i_z_a_t_i_o_n _w_i_t_h _p_e_r_m_u_t_a_t_i_o_n

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

     Parallelized partial cyclic loess normalization of arrays with
     permutation.

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

     normalizeAffyBatchLoessIterPara(cluster,
                     object,
                     percentPerm = 0.75,
                     phenoData = new("AnnotatedDataFrame"), cdfname = NULL,
                     type=c("separate","pmonly","mmonly","together"), 
                     subset = NULL,
                     epsilon = 10^-2, maxit = 1, log.it = TRUE, 
                     span = 2/3, family.loess ="symmetric",
                     verbose=FALSE) 

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

 cluster: A cluster object obtained from the function makeCluster in
          the 'SNOW' package. 

  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.

percentPerm: Percent of permutations to do.

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. 

    type: A string specifying how the normalization should be applied.

  subset: a subset of the data to fit a loess to.

 epsilon: a tolerance value (supposed to be a small value - used as a
          stopping criterium).

   maxit: maximum number of iterations.

  log.it: logical. If 'TRUE' it takes the log2 of mat

    span: parameter to be passed the function loess

family.loess: parameter to be passed the function loess. "gaussian" or
          "symmetric" are acceptable values for this parameter.

 verbose: A logical value. If 'TRUE' it writes out some messages. 

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

     Parallelized partial cyclic loess normalization of arrays with
     permutation. This is a new kind of normalization based on cyclic
     loess normalization. 

     In the partial cyclic loess normalization the loess normalization
     will be done only at the slaves with the arrays at the slaves.
     Therefore we only have to do loess normalization for some pairs
     and have a big saving of time. But this is no enough for good
     normalization. We have to do some interations of array permutation
     between the slaves and again loess normalization at the slaves. If
     we did about 75 percent of the complete cyclic loess normalization
     we can achieve same results and save computation time.

     For the similar serial function and more details to loess
     normalization see the function 'normalize.AffyBatch.loess'.

     For using this function a computer cluster using the 'snow'
     package has to be started. In the loess normalization the arrays
     will compared by pairs. Therefore at every node minimum two arrays
     have to be!

_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)

       c1 <- makeCluster(3)

       AffyBatch <- normalizeAffyBatchLoessIterPara(c1, percentPerm=0.75, Dilution, verbose=TRUE)

       stopCluster(c1)
     }
     ## End(Not run)

