threestep {affyPLM} | R Documentation |
This function converts an AffyBatch
into an
ExpressionSet
using a three step expression measure.
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)
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. |
This function computes the expression measure using threestep methods. Greater details can be found in a vignette.
Ben Bolstad bmb@bmbolstad.com
Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.
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") }