This is the released version of CNVPanelizer; for the devel version, see CNVPanelizer.
Reliable CNV detection in targeted sequencing applicationsBioconductor version: Release (3.21)
A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level.
Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths]
Maintainer: Thomas Wolf <thomas_wolf71 at gmx.de>
Citation (from within R, entercitation("CNVPanelizer")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CNVPanelizer")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("CNVPanelizer")Follow Installation instructions to use this package in your R session.
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