library(BiocStyle)
library(knitr)
The R package decompTumor2Sig has been developed to decompose individual tumor genomes (i.e., the lists of somatic single nucleotide variants identified in individual tumors) according to a set of given mutational signatures---a problem termed signature refitting---using a quadratic-programming approach.
Mutational signatures can be either of the form initially proposed by Alexandrov et al. (Cell Rep. 3:246--259, 2013 and Nature 500:415--421, 2013)---in the following called "Alexandrov signatures"---or of the simplified form proposed by Shiraishi et al. (PLoS Genet. 11:e1005657, 2015)---in the following called "Shiraishi signatures".
For each of the given mutational signatures, decompTumor2Sig determines their contribution to the load of somatic mutations observed in a tumor.
Please read the following important notes first:
Note: here and in the following, when referring to "mutations", we intend single nucleotide variants (SNVs).
Note: given the number of parameters to be estimated, decompTumor2Sig should best be applied only to tumor samples with a sufficient number of somatic mutations. From our experience, using tumor samples with 100+ somatic mutations was reasonable when using version 2 of the COSMIC Mutational Signatures (30 signatures). Tumor samples with fewer somatic mutations should be avoided unless a tumor-type specific subset of signatures is used.
Note: be aware that publicly available signature sets have often been defined with respect to genome wide mutation frequencies, so they should best be applied to somatic mutation data from whole genome sequencing. Background frequencies may be different for subsets of the genome, i.e., current signature sets might yield incorrect results when applied to, for example, mutation data from targetted sequencing of only a subset of genes!
Note: for all functions provided by decompTumor2Sig, please see the manual or the inline R help page for further details and explanations.
Krüger S, Piro RM (2019) decompTumor2Sig: Identification of mutational signatures active in individual tumors. BMC Bioinformatics 20(Suppl 4):152.
Krüger S, Piro RM (2017) Identification of mutational signatures active in individual tumors. PeerJ Preprints 5:e3257v1 (Proceedings of the NETTAB 2017 Workshop on Methods, Tools \& Platforms for Personalized Medicine in the Big Data Era, October 16-18, 2017 in Palermo, Italy).
BibTeX:
@UNPUBLISHED(krueger-decompTumor2Sig-paper, author = "Kr{\"u}ger, Sandra and Piro, Rosario Michael", title = "decompTumor2Sig: Identification of mutational signatures active in individual tumors", journal = "BMC Bioinformatics", volume = "20", number = "Suppl 4", pages = "152", year = 2019 ); @ARTICLE(krueger-decompTumor2Sig-nettab, author = "Kr{\"u}ger, Sandra and Piro, Rosario Michael", title = "Identification of mutational signatures active in individual tumors", journal = "PeerJ Preprints", volume = "5", pages = "e3257v1", year = 2017 );
decompTumor2Sig requires several CRAN and Bioconductor R packages to be installed. Dependencies are usually handled automatically, when installing the package using the following commands:
install.packages("BiocManager") BiocManager::install("decompTumor2Sig")
[NOTE: Ignore the first line if you already have installed the
r CRANpkg("BiocManager")
.]
In the unlikely case that a manual installation is required, e.g., if you do not install decompTumor2Sig via Bioconductor (which is highly recommended), before installing decompTumor2Sig make sure the following packages are installed:
CRAN packages:
r CRANpkg("Matrix")
, r CRANpkg("quadprog")
,
r CRANpkg("plyr")
, r CRANpkg("ggplot2")
, r CRANpkg("ggseqlogo")
,
r CRANpkg("gridExtra")
Bioconductor packages:
r Biocpkg("GenomicRanges")
, r Biocpkg("GenomicFeatures")
,
r Biocpkg("GenomeInfoDb")
,
r Biocpkg("Biostrings")
, r Biocpkg("BiocGenerics")
,
r Biocpkg("S4Vectors")
, r Biocpkg("BSgenome.Hsapiens.UCSC.hg19")
,
r Biocpkg("TxDb.Hsapiens.UCSC.hg19.knownGene")
,
r Biocpkg("VariantAnnotation")
, r Biocpkg("SummarizedExperiment")
If you intend to work with Shiraishi-type signatures, you may also want to
install the R package r Rpackage("pmsignature")
which is neither part of
CRAN nor of Bioconductor and must be downloaded and installed manually
(available at: https://github.com/friend1ws/pmsignature).
CRAN packages can be installed from R using the following command:
install.packages("<package_name>")
Bioconductor packages can be installed from R using the following command:
BiocManager::install("<package_name>")
Sometimes, it may also be useful to update Bioconductor:
BiocManager::install()
Finally, the manual installation of decompTumor2Sig can, for example, be done from the command line ...
R CMD INSTALL decompTumor2Sig_<version>.tar.gz
... or the newest version can directly be installed from GitHub using the
CRAN package r CRANpkg("devtools")
:
library(devtools) install_github("rmpiro/decompTumor2Sig")
After installation, loading the package is simple:
library(decompTumor2Sig)
decompTumor2Sig works with two kinds of input data:
i) a set of given mutational signatures, and ii) a set of somatic mutations (single nucleotide variants) observed in a tumor genome.
Additionally, decompTumor2Sig requires the genomic sequence (in form of a
r Rpackage("BSgenome")
object) to determine neighboring nucleotides of the
mutated bases. It may also require transcript annotations (in form of a
r Rpackage("TxDb")
object) in case the given mutational signatures take
information on the transcription direction into account.
Mutational signatures can be read in two different formats: Alexandrov-type signatures and Shiraishi-type signatures.
Alexandrov signatures are specified either in the format used at the COSMIC Mutational Signatures website for signatures version 2 (March 2015, see http://cancer.sanger.ac.uk/cosmic/signatures_v2 -> "Download signatures"), or in the format used for signatures version 3 (May 2019; see https://cancer.sanger.ac.uk/cosmic/signatures/SBS/ and https://www.synapse.org/#!Synapse:syn12009743). For version 3, only Single Base Substitution (SBS) signatures can be used.
Example for version 2:
Substitution Type Trinucleotide Somatic Mutation Type Signature 1 ... C>A ACA A[C>A]A 0.011098326166 ... C>A ACC A[C>A]C 0.009149340734 ... C>A ACG A[C>A]G 0.001490070468 ... C>A ACT A[C>A]T 0.006233885236 ... ... T>G TTG T[T>G]G 0.002031076880 ... T>G TTT T[T>G]T 0.004030128160 ...
Example for version 3:
Type,SubType,SBS1,SBS2,SBS3,SBS4,SBS5,SBS6, ... C>A,ACA,8.86E-04,5.80E-07,2.08E-02,4.22E-02,1.20E-02,4.25E-04, ... C>A,ACC,2.28E-03,1.48E-04,1.65E-02,3.33E-02,9.44E-03,5.24E-04, ... C>A,ACG,1.77E-04,5.23E-05,1.75E-03,1.56E-02,1.85E-03,5.20E-05, ... C>A,ACT,1.28E-03,9.78E-05,1.22E-02,2.95E-02,6.61E-03,1.80E-04, ... ... T>G,TTG,5.83E-04,9.54E-05,8.05E-03,2.32E-03,6.94E-03,3.24E-04, ... T>G,TTT,2.23E-16,2.23E-16,1.05E-02,5.68E-04,1.35E-02,1.01E-03, ...
(Apart from the change of tab- to comma-separator, the main difference is the lack of the redundant 3rd annotation column in version 3.)
The standard Alexandrov-type signatures report mutation frequencies for nucleotide triplets where the mutated base is at the center. Also, the basic Alexandrov signatures do not take transcription direction into account when computing mutation frequencies.
To read Alexandrov-type signatures, use the command readAlexandrovSignatures(). By default, the command reads the version 2 (!) signatures directly from COSMIC and stores them in a list object:
signatures <- readAlexandrovSignatures() length(signatures) signatures[1]
Alternatively, the signatures can be read from a file of the format shown above:
signatures <- readAlexandrovSignatures(file="<signature_file>")
Shiraishi signatures are specified as matrices (in flat files without headers and row names; one file per signature).
Format (see Shiraishi et al. PLoS Genetics 11(12):e1005657, 2015):
The first row: Frequencies of the six possible base changes C>A, C>G, C>T, T>A, T>C, and T>G. Please note that due to the complementarity of base pairing these six base changes already include A>? and G>?.
The following 2k rows (for k up- and downstream flanking bases): Frequencies of the bases A, C, G, and T, followed by two 0 values.
The optional last row (only if transcription direction is considered): Frequencies of occurrences on the transcription strand, and on the opposite strand, followed by four 0 values.
Example:
0.05606328 0.07038910 0.39331059 0.13103780 0.20797476 0.14122446 0.27758477 0.21075424 0.23543226 0.27622874 0 0 0.33081021 0.25347427 0.23662536 0.17909016 0 0 0.21472304 2.6656e-09 0.55231053 0.23296643 0 0 0.22640542 0.20024237 0.32113377 0.25221844 0 0 0.50140066 0.49859934 0 0 0 0
Among its examples, the decompTumor2Sig package provides a small set of
four Shiraishi-type signatures in flat files. These signatures were obtained
from 21 breast cancer genomes (Nik-Zainal et al. Cell 149:979--993, 2012) using
the R package r Rpackage("pmsignature")
(Shiraishi et al. PLoS Genet.
11:e1005657, 2015).
To read these flat files as signatures, you can use the following example:
# take the example signature flat files provided with decompTumor2Sig sigfiles <- system.file("extdata", paste0("Nik-Zainal_PMID_22608084-pmsignature-sig",1:4,".tsv"), package="decompTumor2Sig") # read the signature flat files signatures <- readShiraishiSignatures(files=sigfiles) signatures[1]
r Rpackage("pmsignature")
{#input-pmsig}The third possibility is to convert Shiraishi-type signatures directly from
the package that computes them (r Rpackage("pmsignature")
; Shiraishi et al.
PLoS Genet. 11:e1005657, 2015).
Example workflow:
# load example signatures for breast cancer genomes from Nik-Zainal et al # (PMID: 22608084) in the format produced by pmsignature (PMID: 26630308) pmsigdata <- system.file("extdata", "Nik-Zainal_PMID_22608084-pmsignature-Param.Rdata", package="decompTumor2Sig") load(pmsigdata) # extract the signatures from the pmsignature 'Param' object signatures <- getSignaturesFromEstParam(Param) signatures[1]
Please note that r Rpackage("pmsignature")
is neither part of CRAN nor of
Bioconductor and must be downloaded and installed manually (available at:
https://github.com/friend1ws/pmsignature). To load mutational signatures
without r Rpackage("pmsignature")
being installed, see the previous sections.
An Alexandrov-type signature can be converted into a Shiraishi-type signature (but not vice versa due to the loss of information). Consider the following example:
sign_a <- readAlexandrovSignatures() sign_s <- convertAlexandrov2Shiraishi(sign_a) sign_s[1]
Note: since the standard Alexandrov-type signatures regard nucleotide triplets and do not take transcription direction into account, the obtained Shiraishi-type signatures will also be limited to triplets without information about transcription direction.
Important: Please be aware that signatures are initially not determined in isolation but as a set of signatures derived from a commonly large set of tumor genomes (Alexandrov et al. Cell Rep. 3:246--259, 2013; Alexandrov et al. Nature 500:415--421, 2013; Shiraishi et al. PLoS Genet. 11:e1005657, 2015). Therefore, the biological meaning of converting signatures is not well defined, and the approach should be taken with caution! As an example for the possible outcome, please see our paper (Krüger and Piro, 2018).
For certain applications it may be necessary to construct signatures or convert them from other kind of data. To do so, each signature must be either a numeric vector of probabilities which sum up to 1 (for Alexandrov-type signatures) or a matrix or data.frame with six numeric columns, every row of which sums up to 1 (for Shiraishi-type signatures).
A set of signatures is then simply a list of such signatures.
To verify whether a signature set has a format that can be used with decompTumor2Sig the package provides the following set of functions. Since the mutation frequencies in genomes are specified in exactly the same way, these functions can be used for both signatures and genomes:
Examples:
isAlexandrovSet(sign_a) isSignatureSet(sign_a) isShiraishiSet(sign_s) isSignatureSet(sign_s) sameSignatureFormat(sign_a, sign_s)
Information on the somatic mutations found in a tumor can be read from one of the following formats and converted to mutation frequencies for decompTumor2Sig.
The somatic mutations of a tumor genome can be read from a VCF file. For detailed information on this format (including an example), see https://samtools.github.io/hts-specs/VCFv4.2.pdf.
Mutations from multiple tumor genomes can also be read from a multi-sample VCF file.
As an example, the decompTumor2Sig package provides the somatic mutations for a subset of six of the 21 breast cancer genomes originally published by Nik-Zainal et al (Cell 149:979--993, 2012). The dataset has been converted from MPF (see below) to VCF.
Example workflow:
# load the reference genome and the transcript annotation database refGenome <- BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19 transcriptAnno <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene # take the six breast cancer genomes from Nik-Zainal et al (PMID: 22608084) gfile <- system.file("extdata", "Nik-Zainal_PMID_22608084-VCF-convertedfromMPF.vcf.gz", package="decompTumor2Sig") # read the cancer genomes in VCF format genomes <- readGenomesFromVCF(gfile, numBases=5, type="Shiraishi", trDir=TRUE, refGenome=refGenome, transcriptAnno=transcriptAnno, verbose=FALSE) length(genomes) genomes[1:2]
When reading somatic mutations of tumor genomes with readGenomesFromVCF(), they are preprocessed to determine mutation frequencies according to specific sequence characteristics which can be controlled by the following arguments:
r Rpackage("pmsignature")
package. If you need to
reproduce the old behavior---which basically arbitrarily assigns one of the two
transcriptions strands---then you can set enforceUniqueTrDir=FALSE (this
option exists mostly for backward compatibility with older versions), but it is
recommended to entirely exclude mutations without ambiguous transcription
strands. Note: this option is ignored when trDir=FALSE, where all mutations
can be used.Alternatively, somatic mutations can be read from an MPF file.
Example MPF file:
patient1 chr1 809687 G C patient1 chr1 819245 G T patient1 chr2 2818266 A G patient1 chr2 3433314 G A patient2 chr1 2927666 A G patient2 chr1 3359791 C T
The five columns contain
Hence, with an MPF file, too, multiple tumors can be described.
As an example, the decompTumor2Sig package provides the somatic mutations for six of the 21 breast cancer genomes originally published by Nik-Zainal et al (Cell 149:979--993, 2012).
Example workflow:
# load the reference genome and the transcript annotation database refGenome <- BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19 transcriptAnno <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene # take the six breast cancer genomes from Nik-Zainal et al (PMID: 22608084) gfile <- system.file("extdata", "Nik-Zainal_PMID_22608084-MPF.txt.gz", package="decompTumor2Sig") # read the cancer genomes in MPF format genomes <- readGenomesFromMPF(gfile, numBases=5, type="Shiraishi", trDir=TRUE, refGenome=refGenome, transcriptAnno=transcriptAnno, verbose=FALSE)
Note: the preprocessing of the somatic mutations into mutation frequencies can be controlled with the same function arguments that have been described above for readGenomesFromVCF().
r Rpackage("pmsignature")
{#mutpm}The third possibility to get the somatic mutations from one or more tumor
genomes is to convert them directly from a tumor data object
(MutationFeatureData object) loaded using the r Rpackage("pmsignature")
package (Shiraishi et al. PLoS Genet. 11:e1005657, 2015).
An example of such an object is provided with decompTumor2Sig:
# get breast cancer genomes from Nik-Zainal et al (PMID: 22608084) # in the format produced by pmsignature (PMID: 26630308) pmsigdata <- system.file("extdata", "Nik-Zainal_PMID_22608084-pmsignature-G.Rdata", package="decompTumor2Sig") load(pmsigdata) # extract the genomes from the pmsignature 'G' object genomes <- getGenomesFromMutFeatData(G, normalize=TRUE) genomes[1]
Please note that r Rpackage("pmsignature")
is neither part of CRAN nor of
Bioconductor and must be downloaded and installed manually (available at:
https://github.com/friend1ws/pmsignature). To read tumor genomes without
r Rpackage("pmsignature")
being installed, see the previous sections about
VCF and MPF and the following section.
Important: the argument normalize, that can be specified for getGenomesFromMutFeatData(), controls whether the function should simply count the number of occurrences or whether it provides (normalized) fractions/percentages of mutations among the total set. Normalization is the default and is what should be used for determining the contribution of individual signatures to the mutational load of a tumor. normalize=FALSE should be used only in case you are interested in how many somatic mutations of the single signature categories can be found in a tumor; it should not be used for further processing with decompTumor2Sig!
Important: There is a slight difference on how r Rpackage("pmsignature")
and decompTumor2Sig preprocess mutations for counting them when taking the
transcription direction into account: For mutations which map to a region with
multiple overlapping genes with opposing transcription directions,
r Rpackage("pmsignature")
assigns the transcript direction of the gene
encountered first in the transcript database (see also Section 3.2.1). This
was also the behavior of decompTumor2Sig until version 1.3.5 (used for
our papers; Krüger and Piro, 2017, 2018). In newer versions,
decompTumor2Sig excludes these mutations
by default from the count because from mutation data alone it cannot be
inferred which of the two genes has the higher transcriptional activity which
might potentially be linked to the occurrence of the mutation. However, when
converting data from r Rpackage("pmsignature")
these mutations have already
been processed and can therefore not be excluded during the conversion.
The Bioconductor package r Biocpkg("VariantAnnotation")
provides the class
VRanges which can be used to store mutation information. decompTumor2Sig
allows to extract single nucleotide variants (SNVs) from such an object and
convert them into the tumor genomes' mutation frequencies using the function
convertGenomesFromVRanges(), as in the following example workflow:
# load the reference genome and the transcript annotation database refGenome <- BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19 transcriptAnno <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene # take six breast cancer genomes from Nik-Zainal et al (PMID: 22608084) gfile <- system.file("extdata", "Nik-Zainal_PMID_22608084-VCF-convertedfromMPF.vcf.gz", package="decompTumor2Sig") # get the corresponding VRanges object (using the VariantAnnotation package) library(VariantAnnotation) vr <- readVcfAsVRanges(gfile, genome="hg19") # convert the VRanges object to the decompTumor2Sig format genomes <- convertGenomesFromVRanges(vr, numBases=5, type="Shiraishi", trDir=TRUE, refGenome=refGenome, transcriptAnno=transcriptAnno, verbose=FALSE)
Note: the preprocessing of the somatic mutations into mutation frequencies can be controlled with the same function arguments that have been described above for readGenomesFromVCF().
Since the mutation data of genomes is specified as mutation probabilities and has exactly the same format as signatures, the format can be verified with the very same functions described in Section 3.1.5 (see above).
(Note: The following examples are for illustrative purpose only and need not be biologically meaningful.)
Once both the tumor genome(s) and the given mutational signatures have been read (see above), the contribution of the given signatures to the somatic mutations in individual tumors can be determined using the following workflow.
In the following, we assume to have the signatures in a list object named signatures and the mutation frequencies of the tumor genome(s) in another list object named genomes.
Important note: it is imperative that the mutation frequencies represented by both signatures and genomes have been computed with the same characteristics. That is, if the signatures refer to sequences of 5 nucleotides/bases (with the mutated base at the center), then also the genomes must have been read for 5 bases. If the signatures have been produced taking the transcription direction into account, then also the genomes must have been read taking the transcription direction into account, and so on.
Given that the signatures and the genomes (if read appropriately) have the same format and contain the same type of information (fractions/percentages of somatic mutations that have specific features, e.g., specific neighboring bases), both can essentially be visualized in the same way.
The function plotMutationDistribution() takes as an input either a single signature or the mutation frequencies of an individual tumor genome (either of Alexandrov- or of Shiraishi-type) and plots the mutation frequency data according to the signature model.
To plot, for example, Alexandrov/COSMIC signature 1 (obtained as described in Section 3.1.1):
signatures <- readAlexandrovSignatures() plotMutationDistribution(signatures[[1]])
To plot one of the Shiraishi signatures provided with this package (see Section 3.1.2):
sigfiles <- system.file("extdata", paste0("Nik-Zainal_PMID_22608084-pmsignature-sig",1:4,".tsv"), package="decompTumor2Sig") signatures <- readShiraishiSignatures(files=sigfiles) plotMutationDistribution(signatures[[1]])
decompTumor2Sig's representation of the mutation frequency data of Shiraishi-type signatures uses sequence logos for the flanking bases and the variant bases (with the heights of the bases being proportional to their probability/frequency). The two possibilities for the mutated central base (C or T) are represented next to each other and their respective frequency is indicated below. This side-by-side representation allows to distinguish the probabilities of variant bases (on top) according to the mutated base. Transcription strand bias (if information on transcription direction is used) is shown in the upper right corner (frequency of mutations on the transcription strand, "+", and the opposite strand, "-").
In the plot above, for example, C and T are nearly equally likely to be mutated by the represented mutational process, but a mutated C most frequently becomes a T, while a mutated T becomes one of the other bases with roughly equal probability. Also, the mutational signature has next to no strand bias.
This representation is similar to the way the r Rpackage("pmsignature")
package represents such signatures, as shown by the following example:
include_graphics("example-sign-Shiraishi.png")
(This plot above was generated with r Rpackage("pmsignature")
and serves only
for comparison, showing the same signature as above.)
To show that genome mutation frequencies can be represented in the same manner, the following example reads the tumor genomes provided with this package using the Alexandrov model (see Section 3.2.1) and plots the mutation frequencies of the first genome:
refGenome <- BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19 gfile <- system.file("extdata", "Nik-Zainal_PMID_22608084-VCF-convertedfromMPF.vcf.gz", package="decompTumor2Sig") genomes <- readGenomesFromVCF(gfile, numBases=3, type="Alexandrov", trDir=FALSE, refGenome=refGenome, verbose=FALSE) plotMutationDistribution(genomes[[1]])
In many cases already a small subset of the given signatures is sufficient to explain the major part of the variance of the mutation frequencies observed in a single tumor genome.
The explained variance can be determined by comparing the true mutation frequencies $g_i$ of the tumor genome---where $i$ is one of the mutation features of the signature model (e.g., triplet mutations for Alexandrov signatures, or base changes or flanking bases for Shiraishi signatures)---to the mutation frequencies $\hat{g}_i$ obtained when re-composing/reconstructing the mutation frequencies of the tumor genome from the mutational signatures and their computed exposures/contributions. (See Section 4.3 for computing the exposures/contributions, and Section 4.4 for reconstructing the mutation frequencies of a tumor.)
For the Alexandrov model, the explained variance can be estimated as:
$$ \mathrm{evar}=1-\frac{\sum_i{(g_i-\hat{g}_i)^2}}{\sum_i({g_i}-\bar{g})^2} $$
where the numerator is the residual sum of squares (RSS) between the predicted and true mutation frequencies of the tumor genome (i.e., the squared error), and the denominator can be interpreted as the deviation from a tumor genome with a uniform mutation frequency of 1/96 for each feature (which is precisely the average mutation frequency $\bar{g}$).
For the Shiraishi model, $\bar{g}$ does not describe a tumor genome with uniform mutation frequencies, and hence the explained variance is estimated as:
$$\mathrm{evar}=1-\frac{\sum_i{(g_i-\hat{g}_i)^2}}{\sum_i({g_i}-{g}^{*}_i)^2}$$
where $g^{*}$ is a uniform tumor model that uses mutation frequencies of 1/6 for the six possible base changes, 1/4 for each of the possible flanking bases, and 1/2 for each of the two possible transcription-strand directions. For more details, please see Krüger and Piro (2019).
The function plotExplainedVariance() allows to visually analyze how many signatures are necessary to explain certain fractions of the variance of a tumor genome's mutational patterns.
For an increasing number K of signatures, the highest variance explained by subsets of K signatures will be plotted. This can help to evaluate what minimum threshold for the explained variance could be used to decompose tumor genomes with the function decomposeTumorGenomes() (see below).
As a simple example, load a set of 15 Shiraishi-type signatures (object
signatures) provided with this package. These signatures were obtained with
the package r Rpackage("pmsignature")
from a set of 435 tumor genomes with at
least 100 somatic mutations from ten different tumor entities (data from
Alexandrov et al. Nature 500:415-421, 2013; for the analysis, see our papers
mentioned in Section 1.1):
# load the 15 Shiraishi signatures obtained from # 435 tumor genomes from Alexandrov et al. sfile <- system.file("extdata", "Alexandrov_PMID_23945592_435_tumors-pmsignature-15sig.Rdata", package="decompTumor2Sig") load(sfile) length(signatures) signatures[1]
This loads an object signatures with 15 Shiraishi signatures for mutated subsequences of length 5 (five nucleotides with the mutated base at the center) and taking transcription direction into account.
Now read the tumor genomes (object genomes) provided with this package, as described in Section 3.2.1:
# load the reference genome and the transcript annotation database refGenome <- BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19 transcriptAnno <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene # take six breast cancer genomes from Nik-Zainal et al (PMID: 22608084) gfile <- system.file("extdata", "Nik-Zainal_PMID_22608084-VCF-convertedfromMPF.vcf.gz", package="decompTumor2Sig") # read the cancer genomes in VCF format genomes <- readGenomesFromVCF(gfile, numBases=5, type="Shiraishi", trDir=TRUE, refGenome=refGenome, transcriptAnno=transcriptAnno, verbose=FALSE)
The explained variance can be plotted only for a single tumor genome. Plotting the explained variance of the first tumor genome when using increasing subsets of the 15 mutational signatures (see above), for example, can be done with the following command:
plotExplainedVariance(genomes[[1]], signatures, minExplainedVariance=0.9, minNumSignatures=2, maxNumSignatures=NULL, greedySearch=TRUE)
The function plotExplainedVariance() takes the following arguments:
This is the heart of the functionality of decompTumor2Sig, its main purpose.
Given a tumor genome and a set of mutational signatures (that represent mutational processes like UV light, smoking, etc; see Alexandrov and Stratton, Curr Opin Genet Dev 24:52-60, 2014), we would like to estimate how strongly the different signatures (processes) contributed to the overall mutational load observed in the tumor. To this end decompTumor2Sig relies on quadratic programming. For details, see our paper (Krüger and Piro, 2018).
The result will be a vector of weights/contributions (or "exposures") which indicate the fractions or percentages of somatic mutations which can likely be attributed to the given signatures.
Lets take, for instance, the signature and tumor data used for the example in Section 4.2 (see there).
To compute the contributions of the 15 given signatures to the first tumor genome, we can run the following command ...
exposure <- decomposeTumorGenomes(genomes[1], signatures)
... and get the following exposures (contributions) for the 15 signatures:
exposure
The exposures/contributions for a single tumor genome can also be plotted:
plotDecomposedContribution(exposure)
In some cases, multiple tumor genomes need to be decomposed (each one, however, individually). In this case, a set of tumor genomes can be passed to decomposeTumorGenomes():
exposures <- decomposeTumorGenomes(genomes, signatures) length(exposures) exposures[1:2]
decompTumor2Sig provides an additional function that can be used to verify whether a list object is of the same format as it is returned by decomposeTumorGenomes():
isExposureSet(exposures)
Instead of decomposing a tumor genome precisely into the given set of signatures (15 in the example above), the function decomposeTumorGenomes() can alternatively be used to search for subsets of signatures for which the decomposition satisfies a minimum threshold of explained variance (here, we show this only for the first tumor):
exposures <- decomposeTumorGenomes(genomes[1], signatures, minExplainedVariance=0.9, minNumSignatures=2, maxNumSignatures=NULL, greedySearch=FALSE, verbose=TRUE)
For each tumor genome, the minimum subset of signatures that explain at least minExplainedVariance percent of the variance of the mutation frequencies will be identified (the exposures of all other signatures will be set to NA):
exposures
plotDecomposedContribution(exposures[[1]])
[If plotDecomposedContribution() is run with removeNA=FALSE, also signatures with an NA value as exposure will be included in the x-axis of the plot. Additionally, the signatures can be passed to the function using the parameter signatures; if so, signature names for the plot will be taken from this object, otherwise they are inferred from the exposure object or set sign_1 to sign_N.]
Important note: although a subset of signatures may explain the somatic mutations observed in the tumor genomes reasonably well, they need not necessarily be the signatures with the highest contribution when taking the entire set of signatures (e.g., due to a greedy search which can get stuck in a local optimum).
Important note: if for a tumor genome no (sub)set of signatures is sufficient to explain at least minExplainedVariance percent of the variance, no result (NULL) is returned for that tumor.
The search behavior of decomposeTumorGenomes() when finding a subset of signatures to explain the somatic mutations of a tumor can be influenced by the following arguments:
Performing, for example, a greedy search for the example above is much faster (shown only for the first tumor):
exposures <- decomposeTumorGenomes(genomes[1], signatures, minExplainedVariance=0.95, minNumSignatures=2, maxNumSignatures=NULL, greedySearch=TRUE, verbose=TRUE) exposures plotDecomposedContribution(exposures[[1]])
Important note: of course, a greedy search which starts from the best combination of minNumSignatures need not yield the same result as a full search because the latter finds the overall best subset while the greedy search can get stuck in a local optimum depending on the starting point of the search. The precise behavior depends on different factors, including for example the similarity between mutational signatures and the minimum required explained variance (lower thresholds can easily be satisfied by very different combinations of signatures). We recommend to test different settings (minimum numbers of signatures, thresholds for explained variance, etc) and learn about the behavior to ensure a meaningful biological interpretation of the results.
Given the mutation frequencies of one or more tumor genomes (genomes), a set of mutational signatures (signatures) and their computed exposures/contributions to the given tumor (exposures), the following command allows to compute---for each individual tumor---the variance of the mutation frequency data that the exposures explain.
The following is an example taking the full set of tumors and signatures from Section 4.2.1:
# load the reference genome and the transcript annotation database refGenome <- BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19 transcriptAnno <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene # take the 15 Shiraishi signatures obtained from # 435 tumor genomes from Alexandrov et al. sfile <- system.file("extdata", "Alexandrov_PMID_23945592_435_tumors-pmsignature-15sig.Rdata", package="decompTumor2Sig") load(sfile) gfile <- system.file("extdata", "Nik-Zainal_PMID_22608084-VCF-convertedfromMPF.vcf.gz", package="decompTumor2Sig") # read the cancer genomes in VCF format genomes <- readGenomesFromVCF(gfile, numBases=5, type="Shiraishi", trDir=TRUE, refGenome=refGenome, transcriptAnno=transcriptAnno, verbose=FALSE)
exposures <- decomposeTumorGenomes(genomes, signatures) computeExplainedVariance(exposures, signatures, genomes)
Note: for computing the variance explained for a single tumor by a set of signatures, the corresponding number of exposure values must be the same as the number of signatures. Undefined exposure values (NA), which can be present if a search for a subset of signatures has been performed as described above, will be set to zero such that the corresponding signature does not contribute. For genomes for which the minExplainedVariance could not be reached, and whose exposure vectors are NULL, the explained variance will be set to NA.
Estimating the explained variance of the decomposition of a tumor genome (see Sections 4.2 and 4.3.2) and assessing its quality requires the mutation frequencies $\hat{g}_i$ of the tumor genome to be reconstructed, or predicted, from the mutational signatures $S_j$ and their exposures/contributions (or weights) $w_j$:
$$ \hat{g}_i = \sum_j{w_j * (S_j)_i} $$
This can be easily achieved using the function composeGenomesFromExposures(). The following is an example taking the tumor genomes from Section 3.2, the signatures from Section 4.2.1, and the exposures as computed in Section 4.3:
genomes_predicted <- composeGenomesFromExposures(exposures, signatures) genomes_predicted[1:2]
Once genomes have been reconstructed, the function evaluateDecompositionQuality() allows to compare the reconstructed genome mutation features to the originally observed features in order to assess the quality of the decomposition. The function is applied to an individual tumor genome, and can either return numerical quality measurements, or provide a quality plot which includes said measurements.
Numerical measurements to compare the reconstructed/predicted and the original tumor mutation patterns are:
Example for obtaining numerical measurements:
evaluateDecompositionQuality(exposures[[1]], signatures, genomes[[1]], plot=FALSE)
Example for obtaining a quality plot which compares the reconstructed and the original data:
evaluateDecompositionQuality(exposures[[1]], signatures, genomes[[1]], plot=TRUE)
In some cases it may be of interest to compare or find a mapping between two sets of signatures, e.g., if they have been inferred from different datasets. For this purpose, decompTumor2Sig provides a set of additional functions.
Let's first read two distinct sets of Shiraishi signatures of the same format (5 bases, with transcript-strand direction):
# get 4 Shiraishi signatures from 21 breast cancers from # Nik-Zainal et al (PMID: 22608084) sigfiles <- system.file("extdata", paste0("Nik-Zainal_PMID_22608084-pmsignature-sig",1:4,".tsv"), package="decompTumor2Sig") sign_s4 <- readShiraishiSignatures(files=sigfiles) # get 15 Shiraishi signatures obtained from # 435 tumor genomes from Alexandrov et al. sfile <- system.file("extdata", "Alexandrov_PMID_23945592_435_tumors-pmsignature-15sig.Rdata", package="decompTumor2Sig") load(sfile) sign_s15 <- signatures
Since these signatures have the same format, we can directly compare them. Using the function determineSignatureDistances(), we can compute the distances of one given signature to all target signatures from a set:
determineSignatureDistances(fromSignature=sign_s4[[1]], toSignatures=sign_s15, method="frobenius")
Apart from the Frobenius distance (method="frobenius"), which is suitable to
compare matrices and hence Shiraishi signatures, other distance metrics can be
used: the "rss" (residual sum of squares = squared error) or any distance
measure available for the function dist of the package r Rpackage("stats")
.
If not the distances of one signature to an entire signature set is needed, but instead a mapping from one signature set to another, mapSignatureSets() can be used.
mapSignatureSets(fromSignatures=sign_s4, toSignatures=sign_s15, method="frobenius", unique=FALSE)
Like for determineSignatureDistances(), with the function mapSignatureSets() a mapping can be built based on different distance metrics. Additionally, the user can specify whether the mapping should be unique (one-to-one mapping), or not.
If unique=FALSE then for each signature of fromSignatures the best match (minimum distance) of toSignatures is selected. The selected signatures need not be unique, i.e., one signature of toSignatures may be the best match for multiple signatures of fromSignatures.
If unique=TRUE, i.e., if a unique (one-to-one) mapping is required, an iterative procedure is performed: in each step, the best matching pair from fromSignatures and toSignatures is mapped and then removed from the list of signatures that remain to be mapped, such that they cannot be selected again. In this case, of course, fromSignatures must not contain more signatures than toSignatures.
Sometimes it may also be useful to compare different types or formats of signatures. For example, since Alexandrov signatures are comparably well studied, it might be interesting to determine which Alexandrov signature is closest to a Shiraishi signature of interest.
Since only signatures of the same format can be directly compared or mapped (see above), decompTumor2Sig provides two functions that transform signatures, such that two signatures, or two sets of signatures, can be converted to the same format.
One of these functions, convertAlexandrov2Shiraishi(), has already been presented in Section 3.1.4 (see there for details). We can convert Alexandrov signatures to Shiraishi-type signatures with 3 bases (without transcription-strand direction):
sign_a <- readAlexandrovSignatures() sign_a2s <- convertAlexandrov2Shiraishi(sign_a)
Note: Of course, there is some information loss here (better: a loss of specificity), as we discuss in our paper (Krüger and Piro, 2018).
Additionally, the function downgradeShiraishiSignatures() can be used to reduce the number of flanking bases and/or discard the information on transcription-strand direction from one or more Shiraishi signatures:
sign_sdown <- downgradeShiraishiSignatures(sign_s15, numBases=3, removeTrDir=TRUE) sign_s15[1] sign_sdown[1]
Having obtained two signature sets of the same format (sequence triplets, but no transcription-strand direction), we can now map one set to the other:
mapSignatureSets(fromSignatures=sign_sdown, toSignatures=sign_a2s, method="frobenius", unique=TRUE)
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