A class to hold the read count data that is used by ExomeDepth to call CNVs.
Objects from the Class
Objects can be created by calls of the form
data = NULL, test, reference, formula = 'cbind(test, reference) ~
1', subset.for.speed = NULL).
data is optional and is only used if the
argument refers to covariates (in which case these covariates must be
included in the data frame).
reference refer to the read count data for the
test and reference samples.
Creating a ExomeDepth object will automatically fit the beta-binomial
model (using routines from the
aod package) and compute the
likelihood for the three copy number states (normal, deletion and
numeric, read count data for the test sample.
numeric, read count data for the reference sample (usually a combination of samples).
character, a character string describing the linear model linking test and reference. Typically this would be
cbind(test, reference) ~ 1.
The expected read count data for the test sample assuming normal copy number.
The over-dispersion parameter of the binomial model. See the
aodpackage for more details.
A matrix of likelihood values, one column per copy number (deletion, normal, duplication).
data.framespecifying the chromosome, start and end for the bins used in the read count computation.
data.framedescribing the output of the CNV calling procedure.
signature(x = "ExomeDepth", transition.probability = "numeric", chromosome = "factor", start = "numeric", end = "numeric", name = "character") ): Uses the pre-computed likelihood values and fits a hidden Markov Chain to the data to generated merged CNV calls.
signature(object = "ExomeDepth", name = "character", chromosome = "factor", start = "numeric", end = "numeric"): This method is unlikely to be directly used but it can include the exon names, chromosome, start, end into the ExomeDepth object.
signature(x = "ExomeDepth", chromosome = "factor", start = "numeric", end = "numeric", type = "character"): type must be either deletion of duplication. This function takes an ExomeDepth object and returns the Bayes factor in favor of a CNV at the specified location.
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