This function works on a CNAqc object with annotated driver mutations and a column (in mutation data) reporting gene symbols in human-readable format (e.g., TP53, KRAS, etc).
This function retains only driver genes, maps them to clonal copy number segments, and then computes mutation phasing by VAFs. This phasing is the same operation carried out to compute CCFs in CNAqc; despite being an approximation to the canonical SNP-based phasing, this operation can easily determine from VAFs how many genome copies carry out a somatic mutation.
With the complete information altogether (major/minor allele copies and mutation multiplicity), it is straightforward to identify, for instance, driver genes that are mutated with a matched LOH status (e.g., complete inactivation of suppressor genes, or amplification of oncogenic mutations).
wt_mutant_alleles(x, gene_column = "VEP.SYMBOL")A CNAqc object.
The gene column where the human-redable gene name should be. By default it is `"VEP.SYMBOL"` assuming that this function gets run on a dataset where VEP annotations have been augmented.
function augment_with_vep to add VEP annotations to
a CNAqc object.
# Example with a CNAqc input object, and MAF annotations
if(FALSE)
{
# Create your CNAqc object (omissis here) from an original "file.vcf"
x = init(mutations = ..., cna = ..., purity = ...)
# Offline, create your MAF annotations as file "file_vcf.maf" from "file.vcf"
# vcf2maf file.vcf .... file_vcf.maf
# Import into R/CNAqc
x = augment_with_maf(x, maf = "file_vcf.maf")
# check they are in (there should be many columns with "MAF." prefix)
x %>% Mutations %>% colnames
# With MAF-imported that is the target column
wt_mutant_alleles(x, gene_column = 'MAF.Hugo_Symbol')
}