In jgoldmann/MutSigs: Jakob's functions for analysing mutation signatures

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

MutSigs

Fit known mutation signatures to collections of mutations.

Example

We will fit COSMIC signatures to test data.

library(MutSigs)
library(tidyverse)

First, generate test data:

possibleSurroundings <-
  expand.grid(
    fistLetter=c("A", "C", "G", "T"),
    secondLetter=".",
    thirdLetter=c("A", "C", "G", "T")) %>%
  unite("surrounding", fistLetter, secondLetter, thirdLetter, sep="") %>%
  pull(surrounding)

possibleSubstitutions <-
  c("C>A", "C>G", "C>T", "T>A", "T>C", "T>G")

n <- 1000
set.seed(1234)

testData <- data.frame(
  surrounding = sample(possibleSurroundings, n, replace = TRUE),
  substitution = sample(possibleSubstitutions, n, replace = TRUE),
  group = sample(c("A", "B"),                n, replace = TRUE)
)

Second, perform the fitting:

result <- assessSignatures(testData,
                           group="group")

Finally, visualize the result:

result %>%
  separate(signature, c("sign", "signatureNr"), sep=10) %>%
  mutate(signatureNr = if_else(signatureNr=="", 0, as.numeric(signatureNr))) %>%
  ggplot(aes(x=signatureNr, 
             y=relC, 
             fill=group,
             ymin=relC-0.5*relSd,
             ymax=relC+0.5*relSd)) + 
  geom_bar(stat="identity", position="dodge") + 
  geom_errorbar(position="dodge") +
  geom_errorbar(position ="dodge",
                aes(ymin=relci95lower,
                    ymax=relci95upper),
                lty=2) +
  labs(y="relative contribution", x="cancer mutation signature nr.")