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\name{batchStatisticAccessors}
\alias{Ns}
\alias{corr}
\alias{tau2}
\alias{mads}
\alias{medians}
\title{
Accessors for batch-specific summary statistics.
}
\description{
The summary statistics stored here are used by the tools for
copy number estimation.
}
\usage{
corr(object, ...)
tau2(object, ...)
mads(object,...)
medians(object,...)
Ns(object,...)
}
\arguments{
\item{object}{ An object of class \code{CNSet}.}
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| 46b837ea |
R: number of markers
A: number of alleles (2)
G: number of biallelic genotypes (3)
C: number of batches
\code{Ns} returns an array of genotype frequencies stratified by
batch. Dimension R x G x C.
\code{corr} returns an array of within-genotype correlations
(log2-scale) stratified by batch. Dimension R x G x C.
\code{medians} returns an array of the within-genotype medians
(intensity-scale) stratified by batch and allele. Dimension R x A x G
x C.
\code{mads} returns an array of the within-genotype median absolute
deviations (intensity-scale) stratified by batch and allele. Dimension
is the same as for \code{medians}.
\code{tau2} returns an array of the squared within-genotype median
absolute deviation on the log-scale. Only the mads for AA and BB
genotypes are stored. Dimension is R x A x G x C, where G is AA or
BB. Note that the mad for allele A/B for subjects with genotype BB/AA
is a robust estimate of the background variance, whereas the the mad
for allele A/B for subjects with genotype AA/BB is a robust estimate
of the variance for copy number greater than 0 (we assume that on the
log-scale the variance is rougly constant for CA, CB > 0).
}
\seealso{
\code{\link{batchStatistics}}
}
\examples{
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| cceffc9c |
data(cnSetExample)
Ns(cnSetExample)[1:5, , ]
corr(cnSetExample)[1:5, , ]
meds <- medians(cnSetExample)
mads(cnSetExample)[1:5, , ,]
tau2(cnSetExample)[1:5, , ,]
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