rmaPara {affyPara} | R Documentation |
Parallelized preprocessing function, which converts an AffyBatch into an ExpressionSet using the robust multi-array average (RMA) expression measure.
rmaPara(object, ids = NULL, phenoData = new("AnnotatedDataFrame"), cdfname = NULL, cluster, verbose = getOption("verbose"))
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. |
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") |
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
.
An object of class ExpressionSet.
Markus Schmidberger schmidb@ibe.med.uni-muenchen.de, Ulrich Mansmann mansmann@ibe.med.uni-muenchen.de
## Not run: library(affyPara) if (require(affydata)) { data(Dilution) makeCluster(3) esset <- rmaPara(Dilution) stopCluster() } ## End(Not run)