doCRMAv1: Estimation and assessment of raw copy numbers at the single locus level (CRMA v1)

Description

Estimation and assessment of raw copy numbers at the single locus level (CRMA v1) based on [1]. The algorithm is processed in bounded memory, meaning virtually any number of arrays can be analyzed on also very limited computer systems.

Usage

# S3 method for AffymetrixCelSet
doCRMAv1(csR, shift=+300, combineAlleles=TRUE, lengthRange=NULL, arrays=NULL, drop=TRUE,
  verbose=FALSE, ...)
  # S3 method for default
doCRMAv1(dataSet, ..., verbose=FALSE)
  # S3 method for default
doASCRMAv1(...)

Value

Returns a named list, iff drop == FALSE, otherwise only ChipEffectSet object.

Arguments

csR, dataSet: An AffymetrixCelSet (or the name of an AffymetrixCelSet).
shift: An tuning parameter specifying how much to shift the probe signals before probe summarization.
combineAlleles: A logical specifying whether allele probe pairs should be summed before modeling or not.
lengthRange: An optional numeric vector of length two passed to FragmentLengthNormalization.
arrays: A integer vector specifying the subset of arrays to process. If NULL, all arrays are considered.
drop: If TRUE, the summaries are returned, otherwise a named list of all intermediate and final results.
verbose: See Verbose.
...: Additional arguments used to set up AffymetrixCelSet (when argument dataSet is specified).

Allele-specific or only total-SNP signals

If you wish to obtain allele-specific estimates for SNPs, which are needed to call genotypes or infer parent-specific copy numbers, then use argument combineAlleles=FALSE. Total copy number signals are still available. If you know for certain that you will not use allele-specific estimates, you will get slightly less noisy signals (very small difference) if you use combineAlleles=TRUE.

doASCRMAv1(...) is a wrapper for doCRMAv1(..., combineAlleles=FALSE).

Author

Henrik Bengtsson

References

[1] H. Bengtsson, R. Irizarry, B. Carvalho & T.P. Speed. Estimation and assessment of raw copy numbers at the single locus level, Bioinformatics, 2008.