normalizeWithinArrays.SNP: Within Array normalization
In beadarraySNP: Normalization and reporting of Illumina SNP bead arrays

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/SnpSetIlluminaNormalize.R

Perform within array normalization on Illumina Golden Gate bead arrays.

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   normalizeWithinArrays.SNP(object, callscore=0.5, normprob=0.5, quantilepersample=FALSE,
                             relative=FALSE, fixed=FALSE, useAll=FALSE, subsample="OPA",
                             Q.scores="callProbability")

`object`	class SnpSetIllumina.
`callscore`	numeric with range 0:1, threshold for probe inclusion.
`normprob`	numeric with range 0:1, target quantile for normalization. The default is to divide the sample intensities by the median of the selected subset.
`quantilepersample`	logical. If `TRUE` then the threshold is determined for each sample, else it is experiment wide. This is only relevant when `fixed` is `FALSE`.
`relative`	logical. If `TRUE` then the ratio of GCS and GTS is used, else only the GCS is used as the quality score.
`fixed`	logical. If `TRUE` then `callscore` is the fixed threshold for the quality score, else the probes above the quantile `callscore` are used.
`useAll`	logical. If `TRUE` then all probes in the dataset are eligible as the invariant set, else only the heterozygous SNPs.
`subsample`	factor or column name in `featureData` slot, the levels of the factor are treated separately.
`Q.scores`	name of assayData() element, or numeric matrix of appropriate size. Quality scores to select high quality SNPs

The function uses high quality heterozygous SNPs as an invariant set with the assumption that these have the highest probability of coming from unaffected regions of the genome. Most of the arguments are used to determine the quality of the call.

This function returns a SnpSetIllumina object.

Jan Oosting

SnpSetIllumina,normalizeLoci.SNP, backgroundCorrect.SNP,normalizeBetweenAlleles.SNP