In caravagn/TIN: TINC - Tumour-in-Normal contamination

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(TINC)

TINC is a simple package that provides only a few functions.

Input format

The input mutation data should report the following information

chr7:96202670:96202670:T:G      33  0   109 47
chr11:127887757:127887757:T:C   26  0   108 52
chr7:147652534:147652534:G:A    37  0   114 10
chr3:18609334:18609334:T:G      31  0   136 15
chr4:86759954:86759954:T:A      29  0   121 14
chr6:95597565:95597565:C:T      39  0   130 70

Columns:

1) a mutation identifier, which can be anything provided it does not contain duplicates. In this example all mutations are SNVs, and the id contains the chromosome, positions, reference and alternative alleles of the SNV.

2) the number of total reads with both reference/ alternative alleles, in the normal sample. 3) the number of total reads with the alternative allele, in the normal sample.

4) the number of total reads with both reference/ alternative alleles, in the tumour sample. 5) the number of total reads with the alternative allele, in the tumour sample.

Input (two options):

• File : a file that will be loaded by TINC, which should have no column names or headers.
• Dataframe : a data.frame or tibble that has the following columns: id, n_ref_count, n_alt_count, t_ref_count, t_alt_count and filters. .

Data generator

TIN provides a function to generate a random dataset for the analysis, which you can use to play around with TIN.

sampled_data = random_TIN(TIT = .8, TIN = 0.05)

print(sampled_data)

You can then proceed as follows - using FAST so the analysis is faster

TIN_fit = autofit(sampled_data$data, FAST = TRUE)

Summary of the results (with an S3 object)

print(TIN_fit)

Plot the results (with an S3 object)

plot(TIN_fit)

caravagn/TIN documentation built on Dec. 10, 2019, 11:21 a.m.