Motivation: Cancer is an evolutionary process driven by continuous acquisition of genetic variations in individual cells. The diversity and the complexity of somatic mutational processes is a conspicuous feature orchestrated by DNA damage agents and repair processes, including exogenous or endogenous mutagen exposures, defects in DNA mismatch repair and enzymatic modification of DNA. The identification of the underlying mutational processes are central to the understanding of cancer origin and evolution.
The signeR package focuses on the estimation and further analysis of mutational signatures. The functionalities of this package can be divided into three categories. First, it provides tools to process VCF files and generate matrices of SNV mutation counts and mutational opportunities, both defined according to a 3bp context (mutation site and its neighboring 3' and 5' bases). Second, these count matrices are considered as input for the estimation of the underlying mutational signatures and the number of active mutational processes. Third, the package provides tools to correlate the activities of those signatures with other relevant information such as clinical data, in order to draw conclusions about the analyzed genome samples, which can be useful for clinical applications. These include the Differential Exposure Score and the a posteriori sample classification.
Although signeR is intended for the estimation of mutational signatures, it actually provides a full Bayesian treatment to the non-negative matrix factorisation (NMF) model. Further details about the method can be found in Rosales & Drummond et al., 2016 (see section 9.1 below).
This vignette briefly explains the use of signeR through examples.
Before installing, please make sure you have the latest version of R and Bioconductor installed.
To install signeR, start R and enter:
install.packages("BiocManager") BiocManager::install("signeR")
For more information, see this page.
Once installed the library can be loaded as
library(signeR)
signeR takes as input a count matrix of samples x features. Each feature is usually an SNV mutation within a 3bp context (96 features, 6 types of SNV mutations and 4 possibilities for the bases at each side of the SNV change). Optionally, an opportunity matrix can also be provided containing the count frequency of the features in the whole analyzed region for each sample. Although not required, this argument is highly recommended because it allows signeR to normalize the feature frequency over the analyzed region.
Input matrices can be read both from a VCF, MAF or a tab-delimited file, as described next.
The VCF file format is the most common format for storing genetic variations, the signeR package includes a utility function for generating a count matrix from the VCF:
library(VariantAnnotation) # BSgenome, equivalent to the one used on the variant call library(BSgenome.Hsapiens.UCSC.hg19) vcfobj <- readVcf("/path/to/a/file.vcf", "hg19") mut <- genCountMatrixFromVcf(BSgenome.Hsapiens.UCSC.hg19, vcfobj)
This function will generate a matrix of mutation counts for each sample in the provided VCF.
If you have one VCF per sample you can combine the results into a single matrix like this:
mut = matrix(ncol=96,nrow=0) for(i in vcf_files) { vo = readVcf(i, "hg19") # sample name (should pick up from the vcf automatically if available) # colnames(vo) = i m0 = genCountMatrixFromVcf(mygenome, vo) mut = rbind(mut, m0) } dim(mut) # matrix with all samples
The opportunity matrix can also be generated from the reference genome (hg19 in the following case):
library(rtracklayer) target_regions <- import(con="/path/to/a/target.bed", format="bed") opp <- genOpportunityFromGenome(BSgenome.Hsapiens.UCSC.hg19, target_regions, nsamples=nrow(mut))
Where target.bed is a bed file
containing the genomic regions analyzed by the variant caller.
If a BSgenome is not available for your genome, you can use a fasta file:
library(Rsamtools) # make sure /path/to/genome.fasta.fai exists ! # you can use "samtools faidx" command to create it mygenome <- FaFile("/path/to/genome.fasta") mut <- genCountMatrixFromVcf(mygenome, vcfobj) opp <- genOpportunityFromGenome(mygenome, target_regions)
mut <- genCountMatrixFromMAF(mygenome, "my_file.maf")
By convention, the input file should be tab-delimited with sample names as row names and features as column names. Features should be referred to in the format "base change:triplet", e.g. "C>A:TCG", as can be seen in the example below. Similarly, the opportunity matrix can be provided in a tab-delimited file with the same structure as the mutation counts file. An example of the required matrix format can be seen here.
This tutorial uses as input the 21 breast cancer dataset described in
Nik-Zainal et al 2012. For the sake of convenience, this dataset is
included with the package and can be accessed by using the
system.file function:
mut <- read.table(system.file("extdata","21_breast_cancers.mutations.txt", package="signeR"), header=TRUE, check.names=FALSE) opp <- read.table(system.file("extdata","21_breast_cancers.opportunity.txt", package="signeR"))
signeR analysis can incorporate any previous knowledge about the signatures present in the dataset. If signatures are known in advance, they can be provided as a matrix, which may be used by signeR in two different ways: a starting value that will be updated according to mutation patterns found on present data or a fixed set of parameters, kept unchanged during the estimation of exposures.
The signatures matrix shall contain each signature in one column. An example of the required matrix format can be seen here.
Along this tutorial a matrix of signatures found in breast cancer, as described in
Cosmic database. For the sake of convenience this matrix is included with the
package and can be accessed by the
system.file function:
Pmatrix <- as.matrix(read.table(system.file("extdata","Cosmic_signatures_BRC.txt", package="signeR"), sep="\t", check.names=FALSE))
signeR takes a count matrix as its only required parameter, but the user can provide an opportunity matrix as well. The algorithm allows the assessment of the number of signatures by three options, as follows.
signatures <- signeR(M=mut, Opport=opp)
signatures <- signeR(M=mut, Opport=opp, nlim=c(3,7))
signatures <- signeR(M=mut, Opport=opp, nsig=3, main_eval=100, EM_eval=50, EMit_lim=20)
signatures.Pstart <- signeR(M=mut, Opport=opp, P=Pmatrix, fixedP=FALSE, main_eval=100, EM_eval=50, EMit_lim=20)
The parameters testing_burn and testing_eval
control the number of iterations used to estimate the number of signatures
(default value is 1000 for both parameters). There are other
arguments that may be passed on to signeR. Please have a look at signeR's
manual, issued by typing help(signeR).
Whenever signeR is left to decide which number of signatures is optimal, it will search for the rank Nsig that maximizes the median Bayesian Information Criterion (BIC). After the processing is done, this information can be plotted by the following command:
BICboxplot(signatures)
Boxplot of BIC values, showing that the optimal number of signatures for this dataset is 5.
signeR also offers the possibility to estimate exposures to known signatures as, for example, the ones described on Cosmic database. In this case, signatures should be provided in a matrix, as described in item 3.4 above, and should be kept constant during analysis:
Pmatrix <- as.matrix(read.table(system.file("extdata","Cosmic_signatures_BRC.txt", package="signeR"), sep="\t", check.names=FALSE))
The following command will make signeR estimate the exposures to the Cosmic signatures found on breast cancer:
exposures.known.sigs <- signeR(M=mut, Opport=opp, P=Pmatrix, fixedP=TRUE, main_eval=100, EM_eval=50, EMit_lim=20)
Exposures can then be recovered from the signeR output by the following command (as in any signeR analysis):
exposures <- Median_exp(exposures.known.sigs$SignExposures)
signeR offers several plots to visualize estimated signatures and their exposures, as well as the convergence of the MCMC used to estimate them.
The following instruction plots the MCMC sample paths for each entry of the signature matrix P and their exposures, i.e. the E matrix. Only post-burnin paths are available for plotting. Those plots are useful for checking if entries have leveled off, reflecting the sampler convergence.
Paths(signatures$SignExposures)