affy (version 1.50.0)

rma: Robust Multi-Array Average expression measure

Description

This function converts an AffyBatch object into an ExpressionSet object using the robust multi-array average (RMA) expression measure.

Usage

rma(object, subset=NULL, verbose=TRUE, destructive=TRUE, normalize=TRUE, background=TRUE, bgversion=2, ...)

Arguments

object

an AffyBatch object.

subset

a character vector with the the names of the probesets to be used in expression calculation.

verbose

logical value. If TRUE, it writes out some messages indicating progress. If FALSE nothing should be printed.

destructive

logical value. If TRUE, works on the PM matrix in place as much as possible, good for large datasets.

normalize

logical value. If TRUE, normalize data using quantile normalization.

background

logical value. If TRUE, background correct using RMA background correction.

bgversion

integer value indicating which RMA background to use 1: use background similar to pure R rma background given in affy version 1.0 - 1.0.2 2: use background similar to pure R rma background given in affy version 1.1 and above

...

further arguments to be passed (not currently implemented - stub for future use).

Value

An ExpressionSet

Details

This function computes the RMA (Robust Multichip Average) expression measure described in Irizarry et al Biostatistics (2003).

Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.

Please note that the default background adjustment method was changed during the lead up to the Bioconductor 1.2 release. This means that this function and expresso should give results that directly agree.

References

Rafael. A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of Affymetrix GeneChip probe level data Nucleic Acids Research 31(4):e15

Bolstad, B.M., Irizarry R. A., Astrand M., and Speed, T.P. (2003), A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193

Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2003) Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics .Vol. 4, Number 2: 249-264

See Also

expresso

Examples

if (require(affydata)) {
  data(Dilution)
  eset <- rma(Dilution)
}