affy (version 1.50.0)

threestep: Three Step expression measures

Description

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

Usage

threestep(object, subset=NULL, normalize=TRUE, background=TRUE,
          background.method="RMA.2", normalize.method="quantile",
          summary.method="median.polish", background.param=list(),
          normalize.param=list(), summary.param=list(), verbosity.level=0)

Arguments

object

an AffyBatch.

subset

a vector with the names of probesets to be used. If NULL, then all probesets are used.

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.

verbosity.level

An integer specifying how much to print out. Higher values indicate more verbose. A value of 0 will print nothing.

Value

• An ExpressionSet

Details

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

References

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

Examples

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

  # should be equivalent to rma()
  eset <- threestep(Dilution)

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

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

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

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

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

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

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

  eset <- threestep(Dilution, background.method="LESN2")
}

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