threestep: Three Step expression measures

In affyPLM: Methods for fitting probe-level models

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.

Details

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

Value

An ExpressionSet

Author(s)

Ben Bolstad [e-mail]

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")
}

affyPLM documentation built on Nov. 8, 2020, 6:53 p.m.