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ExomeDepth (version 0.8.0)

select.reference.set: Combine multiple samples to optimize the reference set in order to maximise the power to detect CNV.

Description

The power to detect copy number variant (CNVs) from targeted sequence data can be maximised if the most appropriate set of sequences is used as reference. This function is designed to combine multiple reference exomes in order to build the best reference set.

Usage

select.reference.set(test.counts, reference.counts, bin.length = NULL,
n.bins.reduced = 0, data = NULL, formula = 'cbind(test, reference) ~ 1')

Arguments

test.counts
Read count data for the test sample (numeric, typically a vector of integer values).
reference.counts
Matrix of read count data for a set of additional samples that can be used as a comparison point for the test sample.
bin.length
Length (in bp) of each of the regions (often exons, but not necessarily) used to compute the read count data. If not provided all bins are assumed to have equal length.
n.bins.reduced
This function can be slow when applied genome-wide. For the purpose of building the reference sample, it is not necessary to use the full data. The number provided by this argument specifies the number of regions (typically exons) that will be
data
Defaults to NULL: A data frame of covariates that can be included in the model.
formula
Defaults to 'cbind(test, reference) ~ 1'. This formula will be used to fit the read count data. Covariates present in the data frame (for example GC content) can be included in the right hand side of the equation'. If covariates are provided t

Value

  • reference.choicecharacter: list of samples selected as optimum reference set.
  • summary.statsA data frame summarizing the output of this computation, including expected Bayes factor, Rs statistic (see reference for explanation) for multiple choices of reference set.

References

Key paper currently under review.