This package implements an algorithm that uses a collection of non-matched normal tissue samples as a reference set to detect CNV aberrations in data generated from amplicon based targeted sequencing.
|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 at each iteration with a procedure that subsamples the amplicons associated with each of the targeted genes. To estimate the background noise of sequencing genes with a low number of amplicons a second subsampling step is performed. Both steps are combined to make a decision on the CNV status. Thus classifying the copy number aberrations on the gene level.|
For a complete list of functions, use library(help = "CNVPanelizer").
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.