sigstats enables common mathematical operations / transformations to be applied to sigverse style signatures / catalogues
Installation
You can install the development version of sigstats like so:
if (!require("pak", quietly = TRUE))
install.packages("pak")
pak::pak("selkamand/sigstats")Quick Start
library(sigstats)
library(sigstash)
library(sigvis)
# Load a signature collection
signatures <- sig_load("COSMIC_v3.3.1_SBS_GRCh38")
# Create a model that represents a mix of SBS2 (40%) and SBS13 (60%)
model <- c(SBS2 = 0.4, SBS13 = 0.6)
# Add selected signatures to the combined model
combined_signatures <- sig_combine(signatures, model)
# Visualise result
sig_visualise(
combined_signatures,
title = "Model",
subtitle = "Created by combining SBS2 (40%) and SBS13 (60%)"
)
#> ✔ All channels matched perfectly to set [sbs_96]. Using this set for sort order
#> ✔ All types matched perfectly to set [sbs_type]. Using this set for sort order
#> ✔ Types matched perfectly to palette [snv_type]
Signature Operations
sigstats helps you add and subtract catalogues/signatures
# Load a signature collection
signatures <- sig_load("COSMIC_v3.3.1_SBS_GRCh38")
# Reconstruct catalogues for two pure samples (each with 100 mutations)
catalogue1 <- sig_reconstruct(signatures[['SBS3']], n = 100)
catalogue2 <- sig_reconstruct(signatures[['SBS4']], n = 100)
catalogue3 <- sig_reconstruct(signatures[['SBS5']], n = 100)
# Subtract catalogue2 from catalogue1
difference <- catalogue1 %-% catalogue2
# Inspect result
head(difference)
#> # A tibble: 6 × 4
#> type channel fraction count
#> <chr> <chr> <dbl> <dbl>
#> 1 C>A A[C>A]A 0.0224 -2.15
#> 2 C>A A[C>A]C 0.0173 -1.66
#> 3 C>A A[C>A]G 0.0149 -1.43
#> 4 C>A A[C>A]T 0.0181 -1.74
#> 5 C>G A[C>G]A 0.0134 1.29
#> 6 C>G A[C>G]C 0.00912 0.878
# Sum three catalogues
catalogue1 %+% catalogue2 %+% catalogue3
#> # A tibble: 96 × 4
#> type channel fraction count
#> <chr> <chr> <dbl> <dbl>
#> 1 C>A A[C>A]A 0.0251 7.54
#> 2 C>A A[C>A]C 0.0196 5.87
#> 3 C>A A[C>A]G 0.00661 1.98
#> 4 C>A A[C>A]T 0.0162 4.86
#> 5 C>G A[C>G]A 0.0123 3.69
#> 6 C>G A[C>G]C 0.00668 2.00
#> 7 C>G A[C>G]G 0.00112 0.336
#> 8 C>G A[C>G]T 0.0104 3.12
#> 9 C>T A[C>T]A 0.0186 5.58
#> 10 C>T A[C>T]C 0.0114 3.41
#> # ℹ 86 more rows
# Sum a catalogue collection
catalogues <- list(cat1 = catalogue1, cat2 = catalogue2, cat3 = catalogue3)
sig_sum(catalogues)
#> # A tibble: 96 × 4
#> type channel fraction count
#> <chr> <chr> <dbl> <dbl>
#> 1 C>A A[C>A]A 0.0251 7.54
#> 2 C>A A[C>A]C 0.0196 5.87
#> 3 C>A A[C>A]G 0.00661 1.98
#> 4 C>A A[C>A]T 0.0162 4.86
#> 5 C>G A[C>G]A 0.0123 3.69
#> 6 C>G A[C>G]C 0.00668 2.00
#> 7 C>G A[C>G]G 0.00112 0.336
#> 8 C>G A[C>G]T 0.0104 3.12
#> 9 C>T A[C>T]A 0.0186 5.58
#> 10 C>T A[C>T]C 0.0114 3.41
#> # ℹ 86 more rowsReconstruct a mutation catalogue from a signature model
We often need to reconstruct a catalogue (or tally) from our signature model.
# Load a signature collection
signatures <- sig_load("COSMIC_v3.3.1_SBS_GRCh38")
# Create a model that represents a mix of SBS2 (40%) and SBS13 (60%)
model <- c(SBS2 = 0.4, SBS13 = 0.6)
# Create a new signature by combining SBS2 and SBS13 in ratios dictated by the above model
signature <- sig_combine(signatures, model)
# Reconstruct a perfect catalogue describing what the mutational profile of a sample
# with 200 mutations and the given signature model would look like
reconstuction <- sig_reconstruct(signature, n=200)
# Visualise result
sig_visualise(
reconstuction,
class = "catalogue",
title = "Reconstructed Catalogue",
subtitle = "Expected profile of a sample with 200 mutations: 40% from SBS2, 60% from SBS13"
)
#> ✔ All channels matched perfectly to set [sbs_96]. Using this set for sort order
#> ✔ All types matched perfectly to set [sbs_type]. Using this set for sort order
#> ✔ Types matched perfectly to palette [snv_type]