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


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.


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



Read count data for the test sample (numeric, typically a vector of integer values).


Matrix of read count data for a set of additional samples that can be used as a comparison point for the test sample.


Length (in bp) of each of the regions (often exons, but not necessarily) that were used to compute the read count data (i.e. what is provided in the argument test.counts of this function). If not provided all bins are assumed to have equal length.


This optimization 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 sub-sampled (using a grid) to optimise the referenceset. I find that 10,000 is largely sufficient for exome data.


Defaults to NULL: A data frame of covariates that can be included in the model.


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 they must be provided as arguments (in the data frame “data”).


Numeric integer (typically 1, 2, or 3) that specifies the number of windows where the over-dispersion parameter phi can vary. It defaults to 1, i.e. a single over-dispersion parameter, independently of read depth.



character: list of samples selected as optimum reference set.


A data frame summarizing the output of this computation, including expected Bayes factor, Rs statistic (see reference for explanation) for multiple choices of reference set.


Vincent Plagnol


Key paper currently under review.

Want to suggest features or report bugs for Use the GitHub issue tracker.

comments powered by Disqus