RPA.preprocess: RPA preprocessing
In microbiome/RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

RPA.preprocess

R Documentation

RPA preprocessing

Description

Preprocess AffyBatch object for RPA.

Usage

RPA.preprocess(
  abatch,
  bg.method = "rma",
  normalization.method = "quantiles.robust",
  cdf = NULL,
  cel.files = NULL,
  cel.path = NULL,
  quantile.basis = NULL
)

Arguments

`abatch`	An AffyBatch object.
`bg.method`	Specify background correction method. See bgcorrect.methods(abatch) for options.
`normalization.method`	Specify normalization method. See normalize.methods(abatch) for options. For memory-efficient online version, use "quantiles.online".
`cdf`	The CDF environment used in the analysis.
`cel.files`	List of CEL files to preprocess.
`cel.path`	Path to CEL file directory.
`quantile.basis`	Optional. Basis for quantile normalization. NOTE: required in original, not log2 scale!

Details

Background correction, quantile normalization and log2-transformation for probe-level raw data in abatch. Then probe-level differential expression is computed between the specified 'reference' array (cind) and the other arrays. Probe-specific variance estimates are robust against the choice of reference array.

Value

fcmat: Probes x arrays preprocessed differential expression matrix. cind: Specifies which array in abatch was selected as a reference in calculating probe-level differential expression. cdf: The CDF environment used in the analysis. set.inds: Indices for probes in each probeset, corresponding to the rows of fcmat.