RPA.preprocess: RPA preprocessing

RPA.preprocessR 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.

Author(s)

Leo Lahti leo.lahti@iki.fi

References

See citation("RPA")

Examples

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microbiome/RPA documentation built on April 9, 2023, 10:59 a.m.