affy (version 1.50.0)

rmaPara: Parallelized PMA preprocessing

Description

Parallelized preprocessing function, which converts an AffyBatch into an ExpressionSet using the robust multi-array average (RMA) expression measure.

Usage

rmaPara(object, cluster,
	ids = NULL,
	phenoData = new("AnnotatedDataFrame"), cdfname = NULL,
	verbose = getOption("verbose"), summary.method="medianpolish")

Arguments

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.

cluster

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

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.

verbose

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

summary.method

The method used for the computation of expression values

Value

• An object of class ExpressionSet.

Details

Parallelized preprocessing function, which goes from raw probe intensities to expression values using the robust multi-array average (RMA) expression measure: Background correction: rma; Normalization: quantile; Summarization: medianpolish For the serial function and more details see the function rma. 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. This is a wrapper function for preproPara.

Examples

library(affyPara)
if (require(affydata)) {
  data(Dilution)

  makeCluster(3)

  esset <- rmaPara(Dilution)

  stopCluster()
}