Plot the comparison of the mutation load against TCGA cohorts

This function computes and plot the mutation load of the input MAF against all of 33 TCGA cohorts derived from MC3 project.

generateTCGAComparePlot(
  maf,
  capture_size = NULL,
  tcga_capture_size = 35.8,
  cohortName = NULL,
  tcga_cohorts = NULL,
  primarySite = FALSE,
  col = c("gray70", "black"),
  medianCol = "red",
  decreasing = FALSE,
  logscale = TRUE,
  rm_hyper = FALSE,
  rm_zero = TRUE
)

Source

TCGA MC3 file was obtained from https://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc. See TCGAmutations R package for more details. Further downstream script to estimate TMB for each sample can be found in ‘inst/scripts/estimate_tcga_tmb.R’

Arguments

maf

A MAF object

capture_size

capture size for input MAF in MBs. Default NULL. If provided plot will be scaled to mutations per MB

tcga_capture_size

capture size for TCGA cohort in MB. Default 35.8.

cohortName

name for the input MAF cohort. Default "Input"

tcga_cohorts

restrict tcga data to these cohorts.

primarySite

If TRUE uses primary site of cancer as labels instead of TCGA project IDs. Default FALSE.

col

color vector for length 2 TCGA cohorts and input MAF cohort. Default gray70 and black.

medianCol

color for median line. Default red.

decreasing

Default FALSE. Cohorts are arranged in increasing mutation burden.

logscale

Default TRUE

rm_hyper

Remove hyper mutated samples (outliers)? Default FALSE

rm_zero

Remove samples with zero mutations? Default TRUE

Value

A list consisting of median mutations per cohort, mutation burden per cohort, significant differences between cohorts, and ggplot object to show mutation burden plot

Details

Tumor mutation burden for TCGA cohorts is obtained from TCGA MC3 study. For consistency TMB is estimated by restricting variants within Agilent Sureselect capture kit of size 35.8 MB.

References

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines Kyle Ellrott, Matthew H. Bailey, Gordon Saksena, et. al. Cell Syst. 2018 Mar 28; 6(3): 271–281.e7. https://doi.org/10.1016/j.cels.2018.03.002

Author

Ashish Jain, Mayank Tandon

Examples

library(maftools)
library(MAFDash)
maf <- system.file("extdata", "test.mutect2.maf.gz", package = "MAFDash")
maf <- read.maf(maf = maf)
#> -Reading
#> -Validating
#> -Silent variants: 561 
#> -Summarizing
#> --Possible FLAGS among top ten genes:
#>   MACF1
#>   MUC16
#> -Processing clinical data
#> --Missing clinical data
#> -Finished in 0.239s elapsed (0.212s cpu) 
l<-generateTCGAComparePlot(maf = maf, cohortName = "test")
l$tcga_compare_plot