threestep {affyPLM}R Documentation

Three Step expression measures


This function converts an AffyBatch into an exprSet using a three step expression measure.


threestep(object,subset=NULL, verbose=TRUE, normalize=TRUE,background=TRUE,background.method="RMA.2",normalize.method="quantile",summary.method="median.polish",background.param = list(),normalize.param=list(),summary.param=list())


object an AffyBatch
subset a vector with the names of probesets to be used. If NULL then all probesets are used.
verbose logical value. If TRUE it writes out some messages. (Curently ignored)
normalize logical value. If TRUE normalize data using quantile normalization
background logical value. If TRUE background correct using RMA background correction
background.method name of background method to use.
normalize.method name of normalization method to use.
summary.method name of summary method to use.
background.param list of parameters for background correction methods
normalize.param list of parameters for normalization methods
summary.param list of parameters for summary methods


This function computes the expression measure using threestep methods. Greater details can be found in a vignette.


An exprSet


Ben Bolstad


Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.

See Also

expresso, rma



# should be equivalent to rma()
eset <- threestep(affybatch.example)

# Using Tukey Biweight summarization
eset <- threestep(affybatch.example,summary.method="tukey.biweight")

# Using Average Log2 summarization
eset <- threestep(affybatch.example,summary.method="average.log")

# Using IdealMismatch background and Tukey Biweight and no normalization.
eset <- threestep(affybatch.example,normalize=FALSE,background.method="IdealMM",summary.method="tukey.biweight")

# Using average.log summarization and no background or normalization.
eset <- threestep(affybatch.example,background=FALSE,normalize=FALSE,background.method="IdealMM",summary.method="tukey.biweight")

# Use threestep methodology with the rlm model fit
eset <- threestep(affybatch.example,summary.method="rlm")

# Use threestep methodology with the log of the average
eset <- threestep(affybatch.example,summary.method="log.average")

# Use threestep methodology with log 2nd largest method
eset <- threestep(affybatch.example,summary.method="log.2nd.largest")

eset <- threestep(affybatch.example,background.method="LESN2")

[Package affyPLM version 1.6.0 Index]