fitProbeLevelModel: Tool to fit Probe Level Models.

In oligo: Preprocessing tools for oligonucleotide arrays

Description

Fits robust Probe Level linear Models to all the (meta)probesets in an FeatureSet. This is carried out on a (meta)probeset by (meta)probeset basis.

Usage

fitProbeLevelModel(object, background=TRUE, normalize=TRUE, target="core", method="plm", verbose=TRUE, S4=TRUE, ...)

Arguments

object	FeatureSet object.
background	Do background correction?
normalize	Do normalization?
target	character vector describing the summarization target. Valid values are: 'probeset', 'core' (Gene/Exon), 'full' (Exon), 'extended' (Exon).
method	summarization method to be used.
verbose	verbosity flag.
S4	return final value as an S4 object (oligoPLM) if TRUE. If FALSE, final value is returned as a list.
...	subset to be passed down to getProbeInfo for subsetting. See subset for details.

Value

fitProbeLevelModel returns an oligoPLM object, if S4=TRUE; otherwise, it will return a list.

Note

This is the initial port of fitPLM to oligo. Some features found on the original work by Ben Bolstad (in the affyPLM package) may not be yet available. If you found one of this missing characteristics, please contact Benilton Carvalho.

Author(s)

This is a simplified port from Ben Bolstad's work implemented in the affyPLM package. Problems with the implementation in oligo should be reported to Benilton Carvalho.

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

rma, summarizationMethods, subset

Examples

if (require(oligoData)){
  data(nimbleExpressionFS)
  fit <- fitProbeLevelModel(nimbleExpressionFS)
  image(fit)
  NUSE(fit)
  RLE(fit)
}

oligo documentation built on Nov. 8, 2020, 6:52 p.m.