Skip to content

A method for fitting a known mutational signature reference to mutational catalogues from cancer samples Resources

License

Unknown, MIT licenses found

Licenses found

Unknown
LICENSE
MIT
LICENSE.md
Notifications You must be signed in to change notification settings

UM-Myeloma-Genomics/mmsig

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

mmsig

The goal of mmsig is to provide a flexible and easily interpretable mutational signature analysis tool. mmsig was developed for hematological malignancies, but can be extended to any cancer with a well-known mutational signature landscape.

mmsig is based on an expectation maximization algorithm for mutational signature fitting and applies cosine similarities for dynamic error suppression as well as bootstrapping-based confidence intervals and assessment of transcriptional strand bias.

Citation: Rustad, E.H., Nadeu, F., Angelopoulos, N. et al. mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies. Commun Biol 4, 424 (2021). https://doi.org/10.1038/s42003-021-01938-0

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("evenrus/mmsig")

Example

This is a basic example which shows mmsig usage

library(mmsig)
#> Loading required package: BSgenome.Hsapiens.UCSC.hg19
#> Loading required package: BSgenome
#> Loading required package: BiocGenerics
#> Loading required package: parallel
#> 
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:parallel':
#> 
#>     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#>     clusterExport, clusterMap, parApply, parCapply, parLapply,
#>     parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     anyDuplicated, append, as.data.frame, basename, cbind, colnames,
#>     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
#>     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
#>     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#>     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
#>     union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> Loading required package: stats4
#> 
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:base':
#> 
#>     expand.grid
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: GenomicRanges
#> Loading required package: Biostrings
#> Loading required package: XVector
#> 
#> Attaching package: 'Biostrings'
#> The following object is masked from 'package:base':
#> 
#>     strsplit
#> Loading required package: rtracklayer

data(mm_5_col)
data(signature_ref)

setting up the mutational signature reference

# remove canonical AID (SBS84) for genome-wide analysis
# remove the platinum signature (SBS35) because the patients are not platinum exposed

sig_ref <- signature_ref[c("sub", "tri", "SBS1", "SBS2", "SBS5", "SBS8", 
                           "SBS9", "SBS13", "SBS18", "SBS-MM1")] 

head(sig_ref)
#>   sub tri     SBS1     SBS2    SBS5    SBS8     SBS9    SBS13   SBS18
#> 1 C>A ACA 0.000886 5.80e-07 0.01200 0.04410 0.000558 0.001820 0.05150
#> 2 C>A ACC 0.002280 1.48e-04 0.00944 0.04780 0.004090 0.000721 0.01580
#> 3 C>A ACG 0.000177 5.23e-05 0.00185 0.00462 0.000426 0.000264 0.00243
#> 4 C>A ACT 0.001280 9.78e-05 0.00661 0.04700 0.003050 0.000348 0.02140
#> 5 C>A CCA 0.000312 2.08e-04 0.00743 0.04010 0.004800 0.001400 0.07400
#> 6 C>A CCC 0.001790 9.53e-05 0.00614 0.03880 0.001920 0.000968 0.01960
#>      SBS-MM1
#> 1 0.00000382
#> 2 0.00000696
#> 3 0.00005140
#> 4 0.00003070
#> 5 0.02199082
#> 6 0.00000258

Setting up the mutation data

# subset three samples to reduce run time

mm_5_col_subset <- mm_5_col[mm_5_col$sample %in% c("MEL_PD26412a", "MEL_PD26411c", "PD26414a"),]
head(mm_5_col_subset)
#>             sample  chr     pos ref alt
#> 96207 MEL_PD26411c chr1 1606928   G   C
#> 96208 MEL_PD26411c chr1 2900399   C   T
#> 96209 MEL_PD26411c chr1 3003910   G   A
#> 96210 MEL_PD26411c chr1 3085231   A   G
#> 96211 MEL_PD26411c chr1 3435711   A   G
#> 96212 MEL_PD26411c chr1 4074739   A   T

Perform mutational signature analysis

# Bootstrapping large datasets with many iterations can significantly increase runtime. 

set.seed(1)

sig_out <- mm_fit_signatures(muts.input=mm_5_col_subset, 
                             sig.input=sig_ref,
                             input.format = "vcf",
                             sample.sigt.profs = NULL, 
                             strandbias = TRUE,
                             bootstrap = TRUE,
                             iterations = 20, # 1000 iterations recommended for stable results
                             refcheck=TRUE,
                             cos_sim_threshold = 0.01,
                             force_include = c("SBS1", "SBS5"),
                             dbg=FALSE) 
#>   |                                                                              |                                                                      |   0%  |                                                                              |=======================                                               |  33%  |                                                                              |===============================================                       |  67%  |                                                                              |======================================================================| 100%

Plot signature estimates

plot_signatures(sig_out$estimate, 
                samples = T, 
                sig_order = c("SBS1", "SBS2", "SBS13", "SBS5", "SBS8", "SBS9", 
                              "SBS18", "SBS-MM1", "SBS35"))

Plot bootstraping confidence intervals

bootSigsPlot(sig_out$bootstrap)

Transcriptional strand bias for SBS-MM1

head(sig_out$strand_bias_mm1)
#>          group transcribed untranscribed     ratio     p_poisson MM1_flag
#> 1 MEL_PD26411c         192           119 1.6134454 0.00004127438        *
#> 2 MEL_PD26412a         193           148 1.3040541 0.01705720483        *
#> 3     PD26414a          29            30 0.9666667 1.00000000000

About

A method for fitting a known mutational signature reference to mutational catalogues from cancer samples Resources

Resources

License

Unknown, MIT licenses found

Licenses found

Unknown
LICENSE
MIT
LICENSE.md

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages