Timing_driver_genes

Timing analysis of most recurrent driver genes as a contribution to PCAWG-11

Input files

The script needs 4 files to be run:

• TableS2_driver_point_mutations: File not privided but can be obtained from PCAWG Drivers' table
• Purity and ploidy: file containing purity and ploidy of each sample
• Patients tissue file info: R object file containing minor patient information.

Analysis workflow

load("TableS2_driver_point_mutations_annotation.RData")  #or equivalent read.table to TableS2_driver_point_mutations generated by PCAWG
CANCERGENES <- levels(finalDrivers$ID)

finalGenotypes <- Genotypes(path_snv_timing_VCFs, path_indel_timing_VCFs) ## both files can be obtained running [MutationTimeR](https://github.com/gerstung-lab/MutationTimeR)

purity_ploidy <- read.table(purity_ploidy_file, header = TRUE, stringsAsFactors = FALSE)
load("2017_02_Patients_tissue_colors.Rdata", verbose = TRUE) #sample2donor, specimenData and tissueColors files

odds_top50 <- timing_drivers(finalGenotypes, purity_ploidy, tissueColors, sample2donor, specimenData, 50)
odds_top50$plot

odds_tp53 <- timing_tp53(finalGenotypes, purity_ploidy, tissueColors, sample2donor, specimenData, odds_top50[["count_genes"]])
odds_tp53$plot

save(list(odds_top50 = odds_top50, odds_tp53 = odds_tp53), file = "final_results.RData")

Output description

• odds_top50: List containing 3 objetcs:
  • data: results from the analysis, including odds ratio, error range, etc.
  • plot: 3 sections plot describing the results (see publication's figure 3)
  • count_genes: objetct needed to run the tp53 analysis
• odds_tp53: List cointaining 2 objetcs:
  • data: similar to odds_top50 object cointaing the odds numerbs.
  • plot: TP53 plot accross the different tumour types