Inferring Somatic Signatures from Single Nucleotide Variant Calls
Table of Contents
- 1. Motivation: The Concept Behind Mutational Signatures
- 2. Methodology: From Mutations to Somatic Signatures
- 3. Workflow: Analysis with the SomaticSignatures Package
- 4. Use case: Estimating Somatic Signatures from TCGA WES Studies
- 4.1. Data: Preproccessing of the TCGA WES Studies
- 4.2. Motifs: Extracting the Sequence Context of Somatic Variants
- 4.3. Decomposition: Inferring Somatic Signatures
- 4.4. Assessment: Number of Signatures
- 4.5. Visualization: Exploration of Signatures and Samples
- 4.6. Clustering: Grouping by Motifs or Samples
- 4.7. Extension: Correction for Batch Effects and Confounding Variables
- 4.8. Extension: Normalization of Sequence Motif Frequencies
- 4.9. Extension: Motifs from Non-Reference Genomes
- 5. Alternatives: Inferring Somatic Signatures with Different Approaches
- 6. Frequently Asked Questions
- 7. References
- 8. Session Information
1 Motivation: The Concept Behind Mutational Signatures
Recent publications introduced the concept of identifying mutational signatures from cancer sequencing studies and linked them to potential mutation generation processes [11,2,3]. Conceptually, this relates somatically occurring single nucleotide variants (SNVs) to the surrounding sequence which will be referred to as mutational or somatic motifs in the following. Based on the frequency of the motifs occurring in multiple samples, these can be decomposed mathematically into so called mutational signatures. In case of the investigation of tumors, the term somatic signatures will be used here to distinguish them from germline mutations and their generating processes.
The SomaticSignatures package provides an efficient and user-friendly
implementation for the extraction of somatic motifs based on a list of
somatically mutated genomic sites and the estimation of somatic signatures with
different matrix decomposition algorithms. Methodologically, this is based on
the work of Nik-Zainal and colleagues [11]. If you
use SomaticSignatures in your research, please cite it as:
Gehring, Julian S., Bernd Fischer, Michael Lawrence, and Wolfgang Huber.
SomaticSignatures: Inferring Mutational Signatures from Single Nucleotide Variants.
Bioinformatics, 2015, btv408. http://dx.doi.org/10.1093/bioinformatics/btv408
2 Methodology: From Mutations to Somatic Signatures
The basic idea of somatic signatures is composed of two parts:
Firstly, each somatic mutation is described in relation of the sequence context
in which it occurs. As an example, consider a SNV, resulting in the alteration
from A in the normal to G in the tumor sample, that is embedded in the
sequence context TAC. Thus, the somatic motif can be written as TAC>TGC or
T.C A>G. The frequency of these motifs across multiple samples is then
represented as a matrix \(M_{ij}\), where \(i\) counts over the motifs and \(j\) over
the samples.
In a second step, the matrix \(M\) is numerically decomposed into two matrices \(W\) and \(H\)
\(M_{ij} \approx \sum_{k=1}^{r} W_{ik} H_{kj}\)
for a fixed number \(r\) of signatures. While \(W\) describes the composition of each signature in term of somatic motifs, \(H\) shows the contribution of the signature to the alterations present in each sample.
3 Workflow: Analysis with the SomaticSignatures Package
The SomaticSignatures package offers a framework for inferring signatures of
SNVs in a user-friendly and efficient manner for large-scale data sets. A tight
integration with standard data representations of the Bioconductor project
[8] was a major design goal. Further, it extends
the selection of multivariate statistical methods for the matrix decomposition
and allows a simple visualization of the results.
For a typical workflow, a set of variant calls and the reference sequence are
needed. Ideally, the SNVs are represented as a VRanges object with the
genomic location as well as reference and alternative allele defined. The
reference sequence can be, for example, a FaFile object, representing an
indexed FASTA file, a BSgenome object, or a GmapGenome object.
Alternatively, we provide functions to extract the relevant information from
other sources of inputs. At the moment, this covers the MuTect
[4] variant caller.
Generally, the individual steps of the analysis can be summarized as:
- The somatic motifs for each variant are retrieved from the reference sequence
with the
mutationContextfunction and converted to a matrix representation with themotifMatrixfunction. - Somatic signatures are estimated with a method of choice (the package
provides with
nmfDecompositionandpcaDecompositiontwo approaches for the NMF and PCA). - The somatic signatures and their representation in the samples are assessed with a set of accessor and plotting functions.
To decompose \(M\), the SomaticSignatures package implements two methods:
- Non-negative matrix factorization (NMF)
- The NMF decomposes \(M\) with the constraint of positive components in \(W\) and \(H\) [7]. The method was used [11] for the identification of mutational signatures, and can be computationally expensive for large data sets.
- Principal component analysis (PCA)
- The PCA employs the eigenvalue decomposition and is therefore suitable for large data sets [13]. While this is related to the NMF, no constraint on the sign of the elements of \(W\) and \(H\) exists.
Other methods can be supplied through the decomposition argument of the
identifySignatures function.
4 Use case: Estimating Somatic Signatures from TCGA WES Studies
In the following, the concept of somatic signatures and the steps for inferring
these from an actual biological data set are shown. For the example, somatic
variant calls from whole exome sequencing (WES) studies from The Cancer Genome
Atlas (TCGA) project will be used, which are part of the
SomaticCancerAlterations package.
library(SomaticSignatures)
library(SomaticCancerAlterations) library(BSgenome.Hsapiens.1000genomes.hs37d5)
4.1 Data: Preproccessing of the TCGA WES Studies
The SomaticCancerAlterations package provides the somatic SNV calls for eight
WES studies, each investigating a different cancer type. The metadata
summarizes the biological and experimental settings of each study.
sca_metadata = scaMetadata() sca_metadata
Cancer_Type Center NCBI_Build Sequence_Source
gbm_tcga GBM broad.mi.... 37 WXS
hnsc_tcga HNSC broad.mi.... 37 Capture
kirc_tcga KIRC broad.mi.... 37 Capture
luad_tcga LUAD broad.mi.... 37 WXS
lusc_tcga LUSC broad.mi.... 37 WXS
ov_tcga OV broad.mi.... 37 WXS
skcm_tcga SKCM broad.mi.... 37 Capture
thca_tcga THCA broad.mi.... 37 WXS
Sequencing_Phase Sequencer Number_Samples
gbm_tcga Phase_I Illumina.... 291
hnsc_tcga Phase_I Illumina.... 319
kirc_tcga Phase_I Illumina.... 297
luad_tcga Phase_I Illumina.... 538
lusc_tcga Phase_I Illumina.... 178
ov_tcga Phase_I Illumina.... 142
skcm_tcga Phase_I Illumina.... 266
thca_tcga Phase_I Illumina.... 406
Number_Patients Cancer_Name
gbm_tcga 291 Glioblastoma multiforme
hnsc_tcga 319 Head and Neck squamous cell carcinoma
kirc_tcga 293 Kidney Chromophobe
luad_tcga 519 Lung adenocarcinoma
lusc_tcga 178 Lung squamous cell carcinoma
ov_tcga 142 Ovarian serous cystadenocarcinoma
skcm_tcga 264 Skin Cutaneous Melanoma
thca_tcga 403 Thyroid carcinoma
The starting point of the analysis is a VRanges object which describes the
somatic variants in terms of their genomic locations as well as reference and
alternative alleles. For more details about this class and how to construct it,
please see the documentation of the VariantAnnotation package
[12]. In this example, all mutational calls
of a study will be pooled together, in order to find signatures related to a
specific cancer type.
sca_data = unlist(scaLoadDatasets()) sca_data$study = factor(gsub("(.*)_(.*)", "\\1", toupper(names(sca_data)))) sca_data = unname(subset(sca_data, Variant_Type %in% "SNP")) sca_data = keepSeqlevels(sca_data, hsAutosomes(), pruning.mode = "coarse") sca_vr = VRanges( seqnames = seqnames(sca_data), ranges = ranges(sca_data), ref = sca_data$Reference_Allele, alt = sca_data$Tumor_Seq_Allele2, sampleNames = sca_data$Patient_ID, seqinfo = seqinfo(sca_data), study = sca_data$study) sca_vr
VRanges object with 594607 ranges and 1 metadata column:
seqnames ranges strand ref alt
<Rle> <IRanges> <Rle> <character> <characterOrRle>
[1] 1 887446 * G A
[2] 1 909247 * C T
[3] 1 978952 * C T
[4] 1 981607 * G A
[5] 1 985841 * C T
... ... ... ... ... ...
[594603] 22 50961303 * G T
[594604] 22 50967746 * C A
[594605] 22 50967746 * C A
[594606] 22 51044090 * C T
[594607] 22 51044095 * G A
totalDepth refDepth altDepth sampleNames
<integerOrRle> <integerOrRle> <integerOrRle> <factorOrRle>
[1] <NA> <NA> <NA> TCGA-06-5858
[2] <NA> <NA> <NA> TCGA-32-1977
[3] <NA> <NA> <NA> TCGA-06-0237
[4] <NA> <NA> <NA> TCGA-06-0875
[5] <NA> <NA> <NA> TCGA-06-6693
... ... ... ... ...
[594603] <NA> <NA> <NA> TCGA-BJ-A0Z0
[594604] <NA> <NA> <NA> TCGA-BJ-A2NA
[594605] <NA> <NA> <NA> TCGA-BJ-A2NA
[594606] <NA> <NA> <NA> TCGA-EM-A3FK
[594607] <NA> <NA> <NA> TCGA-EL-A3T0
softFilterMatrix | study
<matrix> | <factor>
[1] | GBM
[2] | GBM
[3] | GBM
[4] | GBM
[5] | GBM
... ... . ...
[594603] | THCA
[594604] | THCA
[594605] | THCA
[594606] | THCA
[594607] | THCA
-------
seqinfo: 22 sequences from an unspecified genome
hardFilters: NULL
To get a first impression of the data, we count the number of somatic variants per study.
sort(table(sca_vr$study), decreasing = TRUE)
LUAD SKCM HNSC LUSC KIRC GBM THCA OV
208724 200589 67125 61485 24158 19938 6716 5872
4.2 Motifs: Extracting the Sequence Context of Somatic Variants
In a first step, the sequence motif for each variant is extracted based on the
genomic sequence. Here, the BSgenomes object of the human hg19 reference is
used for all samples. However, personalized genomes or other sources for
sequences, for example an indexed FASTA file, can be used naturally.
Additionally, we transform all motifs to have a pyrimidine base (C or T) as
a reference base [2]. The resulting VRanges object
then contains the new columns context and alteration which specify the
sequence content and the base substitution.
sca_motifs = mutationContext(sca_vr, BSgenome.Hsapiens.1000genomes.hs37d5) head(sca_motifs)
VRanges object with 6 ranges and 3 metadata columns:
seqnames ranges strand ref alt
<Rle> <IRanges> <Rle> <character> <characterOrRle>
[1] 1 887446 * G A
[2] 1 909247 * C T
[3] 1 978952 * C T
[4] 1 981607 * G A
[5] 1 985841 * C T
[6] 1 1120451 * C T
totalDepth refDepth altDepth sampleNames
<integerOrRle> <integerOrRle> <integerOrRle> <factorOrRle>
[1] <NA> <NA> <NA> TCGA-06-5858
[2] <NA> <NA> <NA> TCGA-32-1977
[3] <NA> <NA> <NA> TCGA-06-0237
[4] <NA> <NA> <NA> TCGA-06-0875
[5] <NA> <NA> <NA> TCGA-06-6693
[6] <NA> <NA> <NA> TCGA-26-1439
softFilterMatrix | study alteration context
<matrix> | <factor> <DNAStringSet> <DNAStringSet>
[1] | GBM CT G.G
[2] | GBM CT A.G
[3] | GBM CT G.G
[4] | GBM CT G.C
[5] | GBM CT A.G
[6] | GBM CT C.G
-------
seqinfo: 22 sequences from an unspecified genome
hardFilters: NULL
To continue with the estimation of the somatic signatures, the matrix \(M\) of the
form {motifs × studies} is constructed. The normalize argument specifies
that frequencies rather than the actual counts are returned.
sca_mm = motifMatrix(sca_motifs, group = "study", normalize = TRUE) head(round(sca_mm, 4))
GBM HNSC KIRC LUAD LUSC OV SKCM THCA CA A.A 0.0083 0.0098 0.0126 0.0200 0.0165 0.0126 0.0014 0.0077 CA A.C 0.0093 0.0082 0.0121 0.0217 0.0156 0.0192 0.0009 0.0068 CA A.G 0.0026 0.0061 0.0046 0.0144 0.0121 0.0060 0.0004 0.0048 CA A.T 0.0057 0.0051 0.0070 0.0134 0.0100 0.0092 0.0007 0.0067 CA C.A 0.0075 0.0143 0.0215 0.0414 0.0390 0.0128 0.0060 0.0112 CA C.C 0.0075 0.0111 0.0138 0.0415 0.0275 0.0143 0.0018 0.0063
The observed occurrence of the motifs, also termed somatic spectrum, can be visualized across studies, which gives a first impression of the data. The distribution of the motifs clearly varies between the studies.
plotMutationSpectrum(sca_motifs, "study")
Mutation spectrum over studies
4.3 Decomposition: Inferring Somatic Signatures
The somatic signatures can be estimated with each of the statistical methods
implemented in the package. Here, we will use the NMF and PCA, and compare
the results. Prior to the estimation, the number \(r\) of signatures to obtain
has to be fixed; in this example, the data will be decomposed into 5 signatures.
n_sigs = 5 sigs_nmf = identifySignatures(sca_mm, n_sigs, nmfDecomposition) sigs_pca = identifySignatures(sca_mm, n_sigs, pcaDecomposition)
sigs_nmf
MutationalSignatures:
Samples (8): GBM, HNSC, ..., SKCM, THCA
Signatures (5): S1, S2, S3, S4, S5
Motifs (96): CA A.A, CA A.C, ..., TG T.G, TG T.T
sigs_pca
MutationalSignatures:
Samples (8): GBM, HNSC, ..., SKCM, THCA
Signatures (5): S1, S2, S3, S4, S5
Motifs (96): CA A.A, CA A.C, ..., TG T.G, TG T.T
The individual matrices can be further inspected through the accessors
signatures, samples, observed and fitted.
4.4 Assessment: Number of Signatures
Up to now, we have performed the decomposition based on a known number \(r\) of signatures. In many settings, prior biological knowledge or complementing experiments may allow to determine \(r\) independently. If this is not the case, we can try to infer suitable values for \(r\) from the data.
Using the assessNumberSignatures function, we can compute the residuals sum of
squares (RSS) and the explained variance between the observed \(M\) and fitted
\(WH\) mutational spectrum for different numbers of signatures. These measures
are generally applicable to all kinds of decomposition methods, and can aid in
choosing a likely number of signatures. The usage and arguments are analogous
to the identifySignatures function.
n_sigs = 2:8 gof_nmf = assessNumberSignatures(sca_mm, n_sigs, nReplicates = 5) gof_pca = assessNumberSignatures(sca_mm, n_sigs, pcaDecomposition)
The obtained statistics can further be visualized with the
plotNumberSignatures. For each tested number of signatures, black crosses
indicate the results of individual runs, while the red dot represents the
average over all respective runs. Please note that having multiple runs is only
relevant for randomly seeded decomposition methods, as the NMF in our example.
plotNumberSignatures(gof_nmf)
Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2
3.3.0.
ℹ Please use the `fun` argument instead.
ℹ The deprecated feature was likely used in the SomaticSignatures
package.
Please report the issue at <https://support.bioconductor.org>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning
was generated.
Summary statistics for selecting the number of signatures in the NMF decomposition.
plotNumberSignatures(gof_pca)
Summary statistics for selecting the number of signatures in the PCA decomposition.
\(r\) can then be chosen such that increasing the number of signatures does not yield a significantly better approximation of the data, i.e. that the RSS and the explained variance do not change sufficiently for more complex models. The first inflection point of the RSS curve has also been proposed as a measure for the number of features in this context [9]. Judging from both statistics for our dataset, a total of 5 signatures seems to explain the characteristics of the observed mutational spectrum well. In practice, a combination of a statistical assessment paired with biological knowledge about the nature of the data will allow for the most reliable interpretation of the results.
4.5 Visualization: Exploration of Signatures and Samples
To explore the results for the TCGA data set, we will use the plotting
functions. All figures are generated with the ggplot2 package, and thus,
their properties and appearances can directly be modified, even at a later
stage.
library(ggplot2)
Focusing on the results of the NMF first, the five somatic signatures (named S1 to S5) can be visualized either as a heatmap or as a barchart.
plotSignatureMap(sigs_nmf) + ggtitle("Somatic Signatures: NMF - Heatmap")
Composition of somatic signatures estimated with the NMF, represented as a heatmap.
plotSignatures(sigs_nmf) + ggtitle("Somatic Signatures: NMF - Barchart")
Composition of somatic signatures estimated with the NMF, represented as a barchart.
plotObservedSpectrum(sigs_nmf)
plotFittedSpectrum(sigs_nmf)
Each signature represents different properties of the somatic spectrum observed
in the data. While signature S1 is mainly characterized by selective C>T
alterations, others as S4 and S5 show a broad distribution across the motifs.
In addition, the contribution of the signatures in each study can be represented with the same sets of plots. Signature S1 and S3 are strongly represented in the GBM and SKCM study, respectively. Other signatures show a weaker association with a single cancer type.
plotSampleMap(sigs_nmf)
Occurrence of signatures estimated with the NMF, represented as a heatmap.
plotSamples(sigs_nmf)
Occurrence of signatures estimated with the NMF, represented as a barchart.
In the same way as before, the results of the PCA can be visualized. In contrast to the NMF, the signatures also contain negative values, indicating the depletion of a somatic motif.
Comparing the results of the two methods, we can see similar characteristics between the sets of signatures, for example S1 of the NMF and S2 of the PCA.
plotSignatureMap(sigs_pca) + ggtitle("Somatic Signatures: PCA - Heatmap")
Composition of somatic signatures estimated with the PCA, represented as a heatmap.
plotSignatures(sigs_pca) + ggtitle("Somatic Signatures: PCA - Barchart")
Composition of somatic signatures estimated with the PCA, represented as a barchart.
plotFittedSpectrum(sigs_pca)
Since the observed mutational spectrum is defined by the data alone, it is identical for both all decomposition methods.
plotObservedSpectrum(sigs_pca)
4.5.1 Customization: Changing Plot Properties
As elaborated before, since all plots are generated with the ggplot2 framework
[15], we can change all their properties. To continue the
example from before, we will visualize the relative contribution of the
mutational signatures in the studies, and change the plot to fit our needs
better.
library(ggplot2)
p = plotSamples(sigs_nmf) ## (re)move the legend p = p + theme(legend.position = "none") ## (re)label the axis p = p + xlab("Studies") ## add a title p = p + ggtitle("Somatic Signatures in TGCA WES Data") ## change the color scale p = p + scale_fill_brewer(palette = "Blues") ## decrease the size of x-axis labels p = p + theme(axis.text.x = element_text(size = 9))
p
Occurrence of signatures estimated with the NMF, customized plot. See the original plot above for comparisons.
If you want to visualize a large number of samples or signatures, the default
color palette may not provide a sufficient number of distinct colors. You can
add a well-suited palette to your plot, as we have shown before with the
scale_fill functions. For example, scale_fill_discrete will get you the
default ggplot2 color scheme; while this supports many more colors, the
individual levels may be hard to distinguish.
4.6 Clustering: Grouping by Motifs or Samples
An alternative approach to interpreting the mutational spectrum by decomposition
is clustering. With the clusterSpectrum function, the clustering is computed,
by grouping either by the sample or motif dimension of the spectrum. By
default, the Euclidean distance is used; other distance measures, as for example
cosine similarity, are implemented is the proxy package and can be passed as
an optional argument.
clu_motif = clusterSpectrum(sca_mm, "motif")
library(ggdendro) p = ggdendrogram(clu_motif, rotate = TRUE) p
Hierachical clustering of the mutational spectrum, according to motif.
4.7 Extension: Correction for Batch Effects and Confounding Variables
When investigating somatic signatures between samples from different studies, corrections for technical confounding factors should be considered. In our use case of the TCGA WES studies, this is of minor influence due to similar sequencing technology and variant calling methods across the studies. Approaches for the identification of so termed batch effects have been proposed [10,14] and existing implementations can be used in identifying confounding variables as well as correcting for them. The best strategy in addressing technical effects depends strongly on the experimental design; we recommend reading the respective literature and software documentation for finding an optimal solution in complex settings.
From the metadata of the TCGA studies, we have noticed that two different
sequencing approaches have been employed, constituting a potential technical
batch effect. The ComBat function of the sva package allows us to adjust
for this covariate, which yields a mutational spectrum corrected for
contributions related to sequencing technology. We can then continue with the
identification of somatic signatures as we have seen before.
library(sva)
sca_anno = as.data.frame(lapply(sca_metadata, unlist)) model_null = model.matrix(~ 1, sca_anno) sca_mm_batch = ComBat(sca_mm, batch = sca_anno$Sequence_Source, mod = model_null)
4.8 Extension: Normalization of Sequence Motif Frequencies
If comparisons are performed across samples or studies with different capture
targets, for example by comparing whole exome with whole genome sequencing,
further corrections for the frequency of sequence motifs can be taken into
account [11]. The kmerFrequency function provides
the basis for calculating the occurrence of k-mers over a set of ranges of a
reference sequence.
As an example, we compute the frequency of 3-mers for the human toplevel chromosomes, based on a sample of 10'000 locations.
k = 3 n = 1e4 hs_chrs = as(seqinfo(BSgenome.Hsapiens.1000genomes.hs37d5), "GRanges") hs_chrs = keepSeqlevels(hs_chrs, c(1:22, "X", "Y"), pruning.mode = "coarse") k3_hs_chrs = kmerFrequency(BSgenome.Hsapiens.1000genomes.hs37d5, n, k, hs_chrs) k3_hs_chrs
Analogously, the k-mer occurrence across a set of enriched regions, such as in exome or targeted sequencing, can be obtained easily. The following outlines how to apply the approach to the human exome.
library(TxDb.Hsapiens.UCSC.hg19.knownGene) k = 3 n = 1e4 hs_exons = reduce(exons(TxDb.Hsapiens.UCSC.hg19.knownGene)) hs_exons = ncbi(keepStandardChromosomes(hs_exons)) k3_exons = kmerFrequency(BSgenome.Hsapiens.1000genomes.hs37d5, n, k, hs_exons)
With the normalizeMotifs function, the frequency of motifs can be adjusted.
Here, we will transform our results of the TCGA WES studies to have the same
motif distribution as of a whole-genome analysis. The kmers dataset contains
the estimated frequency of 3-mers across the human genome and exome.
data(kmers) norms = k3wg / k3we head(norms)
seq
AAA AAC AAG AAT ACA ACC
1.4519030 1.1022862 1.0248394 1.5075775 1.1089220 0.8910534
sca_mm_norm = normalizeMotifs(sca_mm, norms)
4.9 Extension: Motifs from Non-Reference Genomes
When we determine the sequence context for each alteration, we typically use one
of the reference BSgenome packages in Bioconductor. But we are not restricted
to those, and derive the somatic motifs from different types of sequence
sources, for example 2bit and FASTA files. More precisely, the
mutationContext function will work on any object for which a getSeq method
is defined. You can get the full list available on your system, the results may
vary depending on which packages you have loaded.
showMethods("getSeq")
Function: getSeq (package Biostrings) x="BSgenome" x="FaFile" x="FaFileList" x="TwoBitFile" x="XStringSet"
This allows us to perform our analysis also on non-standard organisms and
genomes, for which a BSgenome package is not available, for example the
1000genomes human reference sequence. Or we can generate genomic references for
specific populations, by updating the standard genomes with a set of known
variants; see the documentation of the BSgenome package and the injectSNPs
function in particular for this.
Taking further, we can base our analysis on the personalized genomic sequence
for each individual, in case it is available. If we imagined that we had a set
of somatic variant calls as VCF files and the personalized genomic sequence as
FASTA files for two individuals A and B at hand, here a simple outline on
how our analysis could work.
## Somatic variant calls vr_A = readVcfAsVRanges(vcf_A_path, "GenomeA") vr_B = readVcfAsVRanges(vcf_B_path, "GenomeB") ## Genomic sequences fa_A = FaFile(fasta_A_path) fa_B = FaFile(fasta_B_path) ## Somatic motifs vr_A = mutationContext(vr_A, fa_A) vr_B = mutationContext(vr_B, fa_B) ## Combine for further analysis vr = c(vr_A, vr_B)
5 Alternatives: Inferring Somatic Signatures with Different Approaches
For the identification of somatic signatures, other methods and implementations exist. The original framework [11,3] proposed for this is based on the NMF and available for the Matlab programming language [1]. In extension, a probabilistic approach based on Poisson processes has been proposed [6] and implemented [5].
6 Frequently Asked Questions
6.1 Citing SomaticSignatures
If you use the SomaticSignatures package in your work, please cite it:
citation("SomaticSignatures")
To cite package 'SomaticSignatures' in publications use:
Julian S. Gehring, Bernd Fischer, Michael Lawrence, Wolfgang
Huber (2015): SomaticSignatures: Inferring Mutational
Signatures from Single Nucleotide Variants. Bioinformatics
July 10, 2015, btv408. doi:10.1093/bioinformatics/btv408
A BibTeX entry for LaTeX users is
@Article{,
title = {SomaticSignatures: Inferring Mutational Signatures from Single Nucleotide Variants.},
author = {Julian S. Gehring and Bernd Fischer and Michael Lawrence and Wolfgang Huber},
year = {2015},
journal = {Bioinformatics},
doi = {10.1093/bioinformatics/btv408},
}
6.2 Getting Help
We welcome questions or suggestions about our software, and want to ensure that we eliminate issues if and when they appear. We have a few requests to optimize the process:
- All questions and follow-ups should take place over the Bioconductor support site, which serves as a repository of information. First search the site for past threads which might have answered your question.
- The subject line should contain SomaticSignatures and a few words describing the problem.
- If you have a question about the behavior of a function, read the sections of
the manual page for this function by typing a question mark and the function
name, e.g.
?mutationContext. Additionally, read through the vignette to understand the interplay between different functions of the package. We spend a lot of time documenting individual functions and the exact steps that the software is performing. - Include all of your R code and its output related to the question you are asking.
- Include complete warning or error messages, and conclude your message with the
full output of
sessionInfo().
6.3 Installing and Upgrading
Before you want to install the SomaticSignatures package, please ensure that
you have the latest version of R and Bioconductor installed. For details on
this, please have a look at the help packages for R and Bioconductor. Then you
can open R and run the following commands which will install the latest
release version of SomaticSignatures:
source("http://bioconductor.org/biocLite.R") biocLite("SomaticSignatures")
Over time, the packages may also receive updates with bug fixes. These installed packages can be updated with:
source("http://bioconductor.org/biocLite.R") biocLite()
6.4 Working with VRanges
A central object in the workflow of SomaticSignatures is the VRanges class
which is part of the VariantAnnotation package. It builds upon the commonly
used GRanges class of the GenomicRanges package. Essentially, each row
represents a variant in terms of its genomic location as well as its reference
and alternative allele.
library(VariantAnnotation)
There are multiple ways of converting its own variant calls into a VRanges
object. One can for example import them from a VCF file with the readVcf
function or employ the readMutect function for importing variant calls from
the MuTect caller directly. Further, one can also construct it from any other
format in the form of:
vr = VRanges( seqnames = "chr1", ranges = IRanges(start = 1000, width = 1), ref = "A", alt = "C") vr
VRanges object with 1 range and 0 metadata columns:
seqnames ranges strand ref alt
<Rle> <IRanges> <Rle> <character> <characterOrRle>
[1] chr1 1000 * A C
totalDepth refDepth altDepth sampleNames
<integerOrRle> <integerOrRle> <integerOrRle> <factorOrRle>
[1] <NA> <NA> <NA> <NA>
softFilterMatrix
<matrix>
[1]
-------
seqinfo: 1 sequence from an unspecified genome; no seqlengths
hardFilters: NULL
7 References
References
| [1] | L. Alexandrov. WTSI Mutational Signature Framework, Oct. 2012. [ http ] |
| [2] | L. B. Alexandrov, S. Nik-Zainal, D. C. Wedge, S. A. J. R. Aparicio, S. Behjati, A. V. Biankin, G. R. Bignell, N. Bolli, A. Borg, A.-L. Børresen-Dale, S. Boyault, B. Burkhardt, A. P. Butler, C. Caldas, H. R. Davies, C. Desmedt, R. Eils, J. E. Eyfjörd, J. A. Foekens, M. Greaves, F. Hosoda, B. Hutter, T. Ilicic, S. Imbeaud, M. Imielinsk, N. Jäger, D. T. W. Jones, D. Jones, S. Knappskog, M. Kool, S. R. Lakhani, C. López-Otín, S. Martin, N. C. Munshi, H. Nakamura, P. A. Northcott, M. Pajic, E. Papaemmanuil, A. Paradiso, J. V. Pearson, X. S. Puente, K. Raine, M. Ramakrishna, A. L. Richardson, J. Richter, P. Rosenstiel, M. Schlesner, T. N. Schumacher, P. N. Span, J. W. Teague, Y. Totoki, A. N. J. Tutt, R. Valdés-Mas, M. M. van Buuren, L. v. . Veer, A. Vincent-Salomon, N. Waddell, L. R. Yates, Australian Pancreatic Cancer Genome Initiative, ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, Icgc PedBrain, J. Zucman-Rossi, P. Andrew Futreal, U. McDermott, P. Lichter, M. Meyerson, S. M. Grimmond, R. Siebert, E. Campo, T. Shibata, S. M. Pfister, P. J. Campbell, and M. R. Stratton. Signatures of mutational processes in human cancer. Nature, 500(7463):415-421, Aug. 2013. [ DOI | .html ] |
| [3] | L. B. Alexandrov, S. Nik-Zainal, D. C. Wedge, P. J. Campbell, and M. R. Stratton. Deciphering Signatures of Mutational Processes Operative in Human Cancer. Cell Reports, 3(1):246-259, Jan. 2013. [ DOI | http ] |
| [4] | K. Cibulskis, M. S. Lawrence, S. L. Carter, A. Sivachenko, D. Jaffe, C. Sougnez, S. Gabriel, M. Meyerson, E. S. Lander, and G. Getz. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature Biotechnology, advance online publication, Feb. 2013. [ DOI | .html ] |
| [5] | A. Fischer, C. J. Illingworth, P. J. Campbell, and V. Mustonen. EMu: probabilistic inference of mutational processes and their localization in the cancer genome. Genome biology, 14(4):R39, Apr. 2013. [ DOI ] |
| [6] | A. Fischer. EMu: Expectation-Maximisation inference of mutational signatures, 2013. [ http ] |
| [7] | R. Gaujoux and C. Seoighe. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics, 11(1):367, July 2010. [ DOI | http ] |
| [8] | R. C. Gentleman, V. J. Carey, D. M. Bates, B. Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, J. Gentry, K. Hornik, T. Hothorn, W. Huber, S. Iacus, R. Irizarry, F. Leisch, C. Li, M. Maechler, A. J. Rossini, G. Sawitzki, C. Smith, G. Smyth, L. Tierney, J. Y. Yang, and J. Zhang. Bioconductor: open software development for computational biology and bioinformatics. Genome Biology, 5(10):R80, Sept. 2004. [ DOI | http ] |
| [9] | L. N. Hutchins, S. M. Murphy, P. Singh, and J. H. Graber. Position-dependent motif characterization using non-negative matrix factorization. Bioinformatics, 24(23):2684-2690, Dec. 2008. [ DOI | http ] |
| [10] | J. T. Leek and J. D. Storey. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genet, 3(9):e161, Sept. 2007. [ DOI | http ] |
| [11] | S. Nik-Zainal, L. B. Alexandrov, D. C. Wedge, P. Van Loo, C. D. Greenman, K. Raine, D. Jones, J. Hinton, J. Marshall, L. A. Stebbings, A. Menzies, S. Martin, K. Leung, L. Chen, C. Leroy, M. Ramakrishna, R. Rance, K. W. Lau, L. J. Mudie, I. Varela, D. J. McBride, G. R. Bignell, S. L. Cooke, A. Shlien, J. Gamble, I. Whitmore, M. Maddison, P. S. Tarpey, H. R. Davies, E. Papaemmanuil, P. J. Stephens, S. McLaren, A. P. Butler, J. W. Teague, G. Jönsson, J. E. Garber, D. Silver, P. Miron, A. Fatima, S. Boyault, A. Langerød, A. Tutt, J. W. Martens, S. A. Aparicio, A. Borg, A. V. Salomon, G. Thomas, A.-L. Børresen-Dale, A. L. Richardson, M. S. Neuberger, P. A. Futreal, P. J. Campbell, and M. R. Stratton. Mutational Processes Molding the Genomes of 21 Breast Cancers. Cell, 149(5):979-993, May 2012. [ DOI | http ] |
| [12] | V. Obenchain, M. Morgan, and M. Lawrence. VariantAnnotation: Annotation of Genetic Variants, 2011. [ .html ] |
| [13] | W. Stacklies, H. Redestig, M. Scholz, D. Walther, and J. Selbig. pcaMethodsa bioconductor package providing PCA methods for incomplete data. Bioinformatics, 23(9):1164-1167, May 2007. [ DOI | http ] |
| [14] | Y. Sun, N. R. Zhang, and A. B. Owen. Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data. The Annals of Applied Statistics, 6(4):1664-1688, Dec. 2012. Zentralblatt MATH identifier: 06141543; Mathematical Reviews number (MathSciNet): MR3058679. [ DOI | http ] |
| [15] | H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer, New York, 1st ed. 2009. corr. 3rd printing 2010 edition edition, Feb. 2010. [ http ] |
8 Session Information
R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB LC_COLLATE=C
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets
[7] methods base
other attached packages:
[1] sva_3.54.0
[2] BiocParallel_1.40.0
[3] genefilter_1.88.0
[4] mgcv_1.9-1
[5] nlme_3.1-166
[6] ggdendro_0.2.0
[7] ggplot2_3.5.1
[8] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
[9] BSgenome_1.74.0
[10] rtracklayer_1.66.0
[11] BiocIO_1.16.0
[12] SomaticCancerAlterations_1.41.0
[13] SomaticSignatures_2.42.0
[14] NMF_0.28
[15] bigmemory_4.6.4
[16] cluster_2.1.6
[17] rngtools_1.5.2
[18] registry_0.5-1
[19] VariantAnnotation_1.52.0
[20] Rsamtools_2.22.0
[21] Biostrings_2.74.0
[22] XVector_0.46.0
[23] SummarizedExperiment_1.36.0
[24] Biobase_2.66.0
[25] GenomicRanges_1.58.0
[26] GenomeInfoDb_1.42.0
[27] IRanges_2.40.0
[28] S4Vectors_0.44.0
[29] MatrixGenerics_1.18.0
[30] matrixStats_1.4.1
[31] BiocGenerics_0.52.0
[32] knitr_1.48
loaded via a namespace (and not attached):
[1] splines_4.4.1 bitops_1.0-9
[3] filelock_1.0.3 tibble_3.2.1
[5] graph_1.84.0 XML_3.99-0.17
[7] rpart_4.1.23 lifecycle_1.0.4
[9] httr2_1.0.5 edgeR_4.4.0
[11] doParallel_1.0.17 lattice_0.22-6
[13] ensembldb_2.30.0 MASS_7.3-61
[15] OrganismDbi_1.48.0 backports_1.5.0
[17] magrittr_2.0.3 limma_3.62.0
[19] Hmisc_5.2-0 rmarkdown_2.28
[21] yaml_2.3.10 ggbio_1.54.0
[23] DBI_1.2.3 RColorBrewer_1.1-3
[25] abind_1.4-8 zlibbioc_1.52.0
[27] purrr_1.0.2 AnnotationFilter_1.30.0
[29] biovizBase_1.54.0 RCurl_1.98-1.16
[31] nnet_7.3-19 rappdirs_0.3.3
[33] GenomeInfoDbData_1.2.13 annotate_1.84.0
[35] codetools_0.2-20 DelayedArray_0.32.0
[37] xml2_1.3.6 tidyselect_1.2.1
[39] UCSC.utils_1.2.0 farver_2.1.2
[41] BiocFileCache_2.14.0 base64enc_0.1-3
[43] GenomicAlignments_1.42.0 jsonlite_1.8.9
[45] Formula_1.2-5 survival_3.7-0
[47] iterators_1.0.14 foreach_1.5.2
[49] tools_4.4.1 progress_1.2.3
[51] Rcpp_1.0.13 glue_1.8.0
[53] gridExtra_2.3 SparseArray_1.6.0
[55] xfun_0.48 dplyr_1.1.4
[57] withr_3.0.2 BiocManager_1.30.25
[59] fastmap_1.2.0 GGally_2.2.1
[61] fansi_1.0.6 digest_0.6.37
[63] R6_2.5.1 mime_0.12
[65] colorspace_2.1-1 dichromat_2.0-0.1
[67] biomaRt_2.62.0 RSQLite_2.3.7
[69] utf8_1.2.4 tidyr_1.3.1
[71] generics_0.1.3 data.table_1.16.2
[73] prettyunits_1.2.0 httr_1.4.7
[75] htmlwidgets_1.6.4 S4Arrays_1.6.0
[77] ggstats_0.7.0 pkgconfig_2.0.3
[79] gtable_0.3.6 blob_1.2.4
[81] htmltools_0.5.8.1 RBGL_1.82.0
[83] ProtGenerics_1.38.0 scales_1.3.0
[85] png_0.1-8 bigmemory.sri_0.1.8
[87] rstudioapi_0.17.1 reshape2_1.4.4
[89] rjson_0.2.23 uuid_1.2-1
[91] checkmate_2.3.2 curl_5.2.3
[93] proxy_0.4-27 cachem_1.1.0
[95] stringr_1.5.1 parallel_4.4.1
[97] foreign_0.8-87 AnnotationDbi_1.68.0
[99] restfulr_0.0.15 pillar_1.9.0
[101] grid_4.4.1 vctrs_0.6.5
[103] pcaMethods_1.98.0 dbplyr_2.5.0
[105] xtable_1.8-4 htmlTable_2.4.3
[107] evaluate_1.0.1 GenomicFeatures_1.58.0
[109] locfit_1.5-9.10 cli_3.6.3
[111] compiler_4.4.1 rlang_1.1.4
[113] crayon_1.5.3 labeling_0.4.3
[115] plyr_1.8.9 stringi_1.8.4
[117] gridBase_0.4-7 txdbmaker_1.2.0
[119] munsell_0.5.1 lazyeval_0.2.2
[121] Matrix_1.7-1 hms_1.1.3
[123] bit64_4.5.2 statmod_1.5.0
[125] KEGGREST_1.46.0 highr_0.11
[127] memoise_2.0.1 bit_4.5.0