1 Introduction

The concept of mutational signatures was introduced in a series of papers by Ludmil Alexandrov et al. (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013) and (Alexandrov, Nik-Zainal, Wedge, Campbell, et al. 2013). A computational framework was published (Alexandrov 2012) with the purpose to detect a limited number of mutational processes which then describe the whole set of SNVs (single nucleotide variants) in a cohort of cancer samples. The general approach (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013) is as follows:

  1. The SNVs are categorized by their nucleotide exchange. In total there are \(4 \times 3 = 12\) different nucleotide exchanges, but if summing over reverse complements only \(12 / 2 = 6\) different categories are left. For every SNV detected, the motif context around the position of the SNV is extracted. This may be a trinucleotide context if taking one base upstream and one base downstream of the position of the SNV, but larger motifs may be taken as well (e.g. pentamers). Taking into account the motif context increases combinatorial complexity: in the case of the trinucleotide context, there are \(4 \times 6 \times 4 = 96\) different variant categories. These categories are called features in the following text. The number of features will be called \(n\).
  2. A cohort consists of different samples with the number of samples denoted by \(m\). For each sample we can count the occurences of each feature, yielding an \(n\)-dimensional vector (\(n\) being the number of features) per sample. For a cohort, we thus get an \(n \times m\) -dimensional matrix, called the mutational catalogue \(V\). It can be understood as a summary indicating which sample has how many variants of which category, but omitting the information of the genomic coordinates of the variants.
  3. The mutational catalogue \(V\) is quite big and still carries a lot of complexity. For many analyses a reduction of complexity is desirable. One way to achieve such a complexity reduction is a matrix decomposition: we would like to find two smaller matrices \(W\) and \(H\) which, if multiplied, would span a high fraction of the complexity of the big matrix \(V\) (the mutational catalogue). Remember that \(V\) is an \(n \times m\) -dimensional matrix, \(n\) being the number of features and \(m\) being the number of samples. \(W\) in this setting is an \(n \times l\) -dimensional matrix and \(H\) is an \(l \times m\) -dimensional matrix. According to the nomeclature defined in (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013), the columns of \(W\) are called the mutational signatures and the columns of \(H\) are called exposures. \(l\) denotes the number of mutational signatures. Hence the signatures are \(n\)-dimensional vectors (with \(n\) being the number of features), while the exposures are \(l\)-dimensional vectors (\(l\) being the number of signatures). Note that as we are dealing with count data, we would like to have only positive entries in \(W\) and \(H\). A mathematical method which is able to do such a decomposition is the NMF (non-negative matrix factorization). It basically solves the problem as illustrated in the following figure (image taken from


Note that the NMF itself solves the above problem for a given number of signatures \(l\). In order to achieve a reduction in complexity, the number of signatures has to be smaller than the number of features ($l < $n), as indicated in the above figure. The framework of Ludmil Alexandrov et al.  (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013) performs not only the NMF decomposition itself, but also identifies the appropriate number of signatures by an iterative procedure.

Another software, the Bioconductor package SomaticSignatures to perform analyses of mutational signatures, is available (Gehring et al. 2015). It allows the matrix decomposition to be performed by NMF and alternatively by PCA (principal component analysis). Both methods have in common that they can be used for discovery, i.e. for the extraction of new signatures. However, they only work well if the analyzed data set has sufficient statistical power, i.e. a sufficient number of samples and sufficient numbers of counts per feature per sample.

The package YAPSA introduced here is complementary to these existing software packages. It is designed for a supervised analysis of mutational signatures, i.e. an analysis with already known signatures \(W\), and with much lower requirements on statistical power of the input data.

2 The YAPSA package

In a context where mutational signatures \(W\) are already known (because they were decribed and published as in (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013) or they are available in a database as under, we might want to just find the exposures \(H\) for these known signatures in the mutational catalogue \(V\) of a given cohort. Mathematically, this is a different and potentially simpler task.

The YAPSA-package (Yet Another Package for Signature Analysis) presented here provides the function LCD (linear combination decomposition) to perform this task. The advantage of this method is that there are no constraints on the cohort size, so LCD can be run for as little as one sample and thus be used e.g. for signature analysis in personalized oncology. In contrast to NMF, LCD is very fast and requires very little computational resources. YAPSA has some other unique functionalities, which are briefly mentioned below and described in detail in separate vignettes.

2.1 Linear Combination Decomposition (LCD)

In the following, we will denote the columns of \(V\) by \(V_{(\cdot j)}\), which corresponds to the mutational catalogue of sample \(j\). Analogously we denote the columns of \(H\) by \(H_{(\cdot j)}\), which is the exposure vector of sample \(j\). Then LCD is designed to solve the optimization problem:

  1. \[ \begin{aligned} \min_{H_{(\cdot j)} \in \mathbb{R}^l}||W \cdot H_{(\cdot j)} - V_{(\cdot j)}|| \quad \forall j \in \{1...m\} \\ \textrm{under the constraint of non-negativity:} \quad H_{(ij)} >= 0 \quad \forall i \in \{1...l\} \quad \forall j \in \{1...m\} \end{aligned} \]

Remember that \(j\) is the index over samples, \(m\) is the number of samples, \(i\) is the index over signatures and \(l\) is the number of signatures. LCD uses a non-negative least squares (NNLS) algorithm (from the R package
nnls ) to solve this optimization problem. Note that the optimization procedure is carried out for every \(V_{(\cdot j)}\), i.e. for every column of \(V\) separately. Of course \(W\) is constant, i.e. the same for every \(V_{(\cdot j)}\).

This procedure is highly sensitive: as soon as a signature has a contribution or an exposure in at least one sample of a cohort, it will be reported (within the floating point precision of the operating system). This might blur the picture and counteracts the initial purpose of complexity reduction. Therefore there is a function LCD_complex_cutoff. This function takes as a second argument a cutoff (a value between zero and one). In the analysis, it will keep only those signatures which have a cumulative (over the cohort) normalized exposure greater than this cutoff. In fact it runs the LCD-procedure twice: once to find initial exposures, summing over the cohort and excluding the ones with too low a contribution as described just above, and a second time doing the analysis only with the signatures left over. Beside the exposures \(H\) corresponding to this reduced set of signatures, the function LCD_complex_cutoff also returns the reduced set of signatures itself.

Another R package for the supervised analysis of mutational signatures is available: deconstructSigs (Rosenthal et al. 2016). One difference between LCD_complex_cutoff as described here in YAPSA and the corresponding function whichSignatures in deconstructSigs is that LCD_complex_cutoff accepts different cutoffs and signature-specific cutoffs (accounting for potentially different detectability of different signatures), whereas in whichSignatures in deconstructSigs a general fixed cutoff is set to be 0.06. In the following, we briefly mention other features of the software package YAPSA and refer to the corresponding vignettes for detailed descriptions.

2.2 Signature-specific cutoffs

One special characteristic of YAPSA is that it provides the opportunity to perform analyses of mutational signtures with signature-specific cutoffs. Different signatures have different detectability. Those with high detectability will occur as false positive calls more often. In order to account for the different detectability, we introduced the concept of signature-specific cutoffs: a signature which leads to many false positive calls has to cross a higher threshold than a signature which rarely leads to false positive calls. While this vignette introduces how to work with signature-specific cutoffs in general, optimal signature-specific cutoffs are presented in 2. Signature-specific cutoffs.

2.3 Confidence intervals for mutational signature exposures

In order to evaluate the confidence of computed exposures to mutational signatures, YAPSA provides 95% confidence intervals (CIs). The computation relies on the concept of profile likelihood (Raue et al. 2009). Details can be found in 3. Confidence Intervals.

2.4 Stratification of the Mutational Catalogue (SMC)

For some questions it is useful to assign the SNVs detected in the samples of a cohort to categories. We call an analysis of mutational signatures which takes into account these strata a stratified analysis, which has the potential to reveal enrichment and depletion patterns. Of note, this is different from performing completely separate and independent NNLS analyses of mutational signatures on the different strata. Instead, the results of the unstratified analysis are used as input for a constrained analysis for the strata. Details can be found in 4. Stratified Analysis of Mutational Signatures

2.5 Indel signatures

Recently a new and extended set of mutational signatures was published by the Pan Cancer Analysis of Whole Genomes (PCAWG) consortium (Alexandrov et al. 2020). In addition to an extended set of SNV mutational signatures, that analysis for the first time had sufficient statistical power to also extract 17 Indel signatures, based on a classification of Indels into 83 categories or features. YAPSA also offers functionality to perform supervised analyses of mutational signatures on these Indel signatures, details can be found in 5. Indel signature analysis

3 Example: a cohort of B-cell lymphomas

We will now apply some functions of the YAPSA package to Whole Genome Sequencing datasets published in Alexandrov et al. (2013). First we have to load this data and get an overview (first subsection). Then we will load data on published signatures (second subsection). Only in the third subsection we will actually start using the YAPSA functions.


3.1 Example data

In the following, we will load and get an overview of the data used in the analysis by Alexandrov et al. (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013)

3.1.1 Loading example data


This creates a dataframe with 128639 rows. It is equivalent to executing the R code

lymphoma_Nature2013_ftp_path <- paste0(
lymphoma_Nature2013_raw_df <- read.csv(file=lymphoma_Nature2013_ftp_path,

The format is inspired by the vcf format with one line per called variant. Note that the files provided at that URL have no header information, therefore we have to add some. We will also slightly adapt the data structure:

names(lymphoma_Nature2013_raw_df) <- c("PID","TYPE","CHROM","START",
lymphoma_Nature2013_df <- subset(lymphoma_Nature2013_raw_df,TYPE=="subs",
names(lymphoma_Nature2013_df)[2] <- "POS"
      caption = "First rows of the file containing the SNV variant calls.")

Table 1: First rows of the file containing the SNV variant calls.
1 183502381 G A 07-35482
18 60985506 T A 07-35482
18 60985748 G T 07-35482
18 60985799 T C 07-35482
2 242077457 A G 07-35482
6 13470412 C T 07-35482

Here, we have selected only the variants characterized as subs (those are the SNVs we are interested in for the mutational signatures analysis, small indels are filtered out by this step), so we are left with 128212 variants or rows. Note that there are 48 different samples:

##  [1] 07-35482       1060           1061           1065           1093          
##  [6] 1096           1102           4101316        4105105        4108101       
## [11] 4112512        4116738        4119027        4121361        4125240       
## [16] 4133511        4135350        4142267        4158726        4159170       
## [21] 4163639        4175837        4177856        4182393        4189200       
## [26] 4189998        4190495        4193278        4194218        4194891       
## [31] 515            DLBCL-PatientA DLBCL-PatientB DLBCL-PatientC DLBCL-PatientD
## [36] DLBCL-PatientE DLBCL-PatientF DLBCL-PatientG DLBCL-PatientH DLBCL-PatientI
## [41] DLBCL-PatientJ DLBCL-PatientK DLBCL-PatientL DLBCL-PatientM EB2           
## [46] FL009          FL-PatientA    G1            
## 48 Levels: 07-35482 1060 1061 1065 1093 1096 1102 4101316 4105105 ... G1

For convenience later on, we annotate subgroup information to every variant (indirectly through the sample it occurs in). For reasons of simplicity, we also restrict the analysis to the Whole Genome Sequencing (WGS) datasets:

lymphoma_Nature2013_df$SUBGROUP <- "unknown"
DLBCL_ind <- grep("^DLBCL.*",lymphoma_Nature2013_df$PID)
lymphoma_Nature2013_df$SUBGROUP[DLBCL_ind] <- "DLBCL_other"
MMML_ind <- grep("^41[0-9]+$",lymphoma_Nature2013_df$PID)
lymphoma_Nature2013_df <- lymphoma_Nature2013_df[MMML_ind,]
for(my_PID in rownames(lymphoma_PID_df)) {
  PID_ind <- which(as.character(lymphoma_Nature2013_df$PID)==my_PID)
  lymphoma_Nature2013_df$SUBGROUP[PID_ind] <-
lymphoma_Nature2013_df$SUBGROUP <- factor(lymphoma_Nature2013_df$SUBGROUP)
## Levels: WGS_B WGS_D WGS_F WGS_I

3.1.2 Displaying example data

Rainfall plots provide a quick overview of the mutational load of a sample. To this end we have to compute the intermutational distances. But first we still do some reformatting…

lymphoma_Nature2013_df <- translate_to_hg19(lymphoma_Nature2013_df,"CHROM")
lymphoma_Nature2013_df$change <- 
lymphoma_Nature2013_df <- 
lymphoma_Nature2013_df <- annotate_intermut_dist_cohort(lymphoma_Nature2013_df,
lymphoma_Nature2013_df$col <- 

Now we can select one sample and make the rainfall plot. The plot function used here relies on the package gtrellis by Zuguang Gu (Gu, Eils, and Schlesner 2016).

choice_PID <- "4121361"
PID_df <- subset(lymphoma_Nature2013_df,PID==choice_PID)

This shows a rainfall plot typical for a lymphoma sample with clusters of increased mutation density e.g. at the immunoglobulin loci.

3.2 Loading the signature information

As stated above, one of the functions in the YAPSA package (LCD) is designed to do mutational signatures analysis with known signatures. There are (at least) two possible sources for signature data: i) the ones published initially by Alexandrov et al. (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013), and ii) an updated and curated current set of mutational signatures is maintained by Ludmil Alexandrov at The following three subsections describe how you can load the data from these resources. Alternatively, you can bypass the three following subsections because the signature datasets are also included in this package:


3.2.1 Loading the initial set of signatures

We first load the (older) set of signatures as published in Alexandrov et al.  (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013):

Alex_signatures_path <- paste0("",
AlexInitialArtif_sig_df <- read.csv(Alex_signatures_path,header=TRUE,sep="\t")
Substitution.Type Trinucleotide Somatic.Mutation.Type Signature.1A
C>A ACA A[C>A]A 0.0112
C>A ACC A[C>A]C 0.0092
C>A ACG A[C>A]G 0.0015
C>A ACT A[C>A]T 0.0063
C>A CCA C[C>A]A 0.0067
C>A CCC C[C>A]C 0.0074
C>A CCG C[C>A]G 0.0009
C>A CCT C[C>A]T 0.0073
C>A GCA G[C>A]A 0.0083

We will now reformat the dataframe:

Alex_rownames <- paste(AlexInitialArtif_sig_df[,1],
                       AlexInitialArtif_sig_df[,2],sep=" ")
select_ind <- grep("Signature",names(AlexInitialArtif_sig_df))
AlexInitialArtif_sig_df <- AlexInitialArtif_sig_df[,select_ind]
number_of_Alex_sigs <- dim(AlexInitialArtif_sig_df)[2]
names(AlexInitialArtif_sig_df) <- gsub("Signature\\.","A",
rownames(AlexInitialArtif_sig_df) <- Alex_rownames
      caption="Exemplary data from the initial Alexandrov signatures.")

Table 2: Exemplary data from the initial Alexandrov signatures.
A1A A1B A2 A3 A4 A5
C>A ACA 0.0112 0.0104 0.0105 0.0240 0.0365 0.0149
C>A ACC 0.0092 0.0093 0.0061 0.0197 0.0309 0.0089
C>A ACG 0.0015 0.0016 0.0013 0.0019 0.0183 0.0022
C>A ACT 0.0063 0.0067 0.0037 0.0172 0.0243 0.0092
C>A CCA 0.0067 0.0090 0.0061 0.0194 0.0461 0.0097
C>A CCC 0.0074 0.0047 0.0012 0.0161 0.0614 0.0050
C>A CCG 0.0009 0.0013 0.0006 0.0018 0.0088 0.0028
C>A CCT 0.0073 0.0098 0.0011 0.0157 0.0432 0.0111
C>A GCA 0.0083 0.0169 0.0093 0.0107 0.0376 0.0119

This results in a dataframe for signatures, containing 27 signatures as column vectors. It is worth noting that in the initial publication, only a subset of these 27 signatures were validated by an orthogonal sequencing technology. So we can filter down:

AlexInitialValid_sig_df <- AlexInitialArtif_sig_df[,grep("^A[0-9]+",
number_of_Alex_validated_sigs <- dim(AlexInitialValid_sig_df)[2]

We are left with 22 signatures.

3.2.2 Loading the updated set of mutational signatures

An updated and curated set of mutational signatures is maintained by Ludmil Alexandrov at We will use this set for the following analysis. In particular, we will use the signatures stored in which for convenience we have stored in a data frame in YAPSA.

Alex_COSMIC_rownames <- paste(AlexCosmicValid_sig_df[,1],
                              AlexCosmicValid_sig_df[,2],sep=" ")
COSMIC_select_ind <- grep("Signature",names(AlexCosmicValid_sig_df))
AlexCosmicValid_sig_df <- AlexCosmicValid_sig_df[,COSMIC_select_ind]
number_of_Alex_COSMIC_sigs <- dim(AlexCosmicValid_sig_df)[2]
names(AlexCosmicValid_sig_df) <- gsub("Signature\\.","AC",
rownames(AlexCosmicValid_sig_df) <- Alex_COSMIC_rownames
      caption="Exemplary data from the updated Alexandrov signatures.")

Table 3: Exemplary data from the updated Alexandrov signatures.
C>A ACA 0.0110983 0.0006827 0.0221723 0.0365 0.0149415 0.0017
C>A ACC 0.0091493 0.0006191 0.0178717 0.0309 0.0089609 0.0028
C>A ACG 0.0014901 0.0000993 0.0021383 0.0183 0.0022078 0.0005
C>A ACT 0.0062339 0.0003239 0.0162651 0.0243 0.0092069 0.0019
C>G ACA 0.0018011 0.0002635 0.0240026 0.0097 0.0116710 0.0013
C>G ACC 0.0025809 0.0002699 0.0121603 0.0054 0.0072921 0.0012
C>G ACG 0.0005925 0.0002192 0.0052754 0.0031 0.0023038 0.0000
C>G ACT 0.0029640 0.0006110 0.0232777 0.0054 0.0116962 0.0018
C>T ACA 0.0295145 0.0074416 0.0178722 0.0120 0.0218392 0.0312

This results in a dataframe containing 30 signatures as column vectors. For reasons of convenience and comparability with the initial signatures, we reorder the features. To this end, we adhere to the convention chosen in the initial publication by Alexandrov et al. (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013) for the initial signatures.

COSMIC_order_ind <- match(Alex_rownames,Alex_COSMIC_rownames)
AlexCosmicValid_sig_df <- AlexCosmicValid_sig_df[COSMIC_order_ind,]
      caption=paste0("Exemplary data from the updated Alexandrov ",
                     "signatures, rows reordered."))

Table 4: Exemplary data from the updated Alexandrov signatures, rows reordered.
C>A ACA 0.0110983 0.0006827 0.0221723 0.0365 0.0149415 0.0017
C>A ACC 0.0091493 0.0006191 0.0178717 0.0309 0.0089609 0.0028
C>A ACG 0.0014901 0.0000993 0.0021383 0.0183 0.0022078 0.0005
C>A ACT 0.0062339 0.0003239 0.0162651 0.0243 0.0092069 0.0019
C>A CCA 0.0065959 0.0006774 0.0187817 0.0461 0.0096749 0.0101
C>A CCC 0.0073424 0.0002137 0.0157605 0.0614 0.0049523 0.0241
C>A CCG 0.0008928 0.0000068 0.0019634 0.0088 0.0028006 0.0091
C>A CCT 0.0071866 0.0004163 0.0147229 0.0432 0.0110135 0.0571
C>A GCA 0.0082326 0.0003520 0.0096965 0.0376 0.0118922 0.0024

Note that the order of the features, i.e. nucleotide exchanges in their trinucleotide content, is changed from the fifth line on as indicated by the row names.

3.2.3 Preparation for later analysis

For every set of signatures, the functions in the YAPSA package require an additional dataframe containing meta information about the signatures. In that dataframe you can specify the order in which the signatures are going to be plotted and the colours asserted to the different signatures. In the following subsection we will set up such a dataframe. However, the respective dataframes are also stored in the package. If loaded by data(sigs) the following code block can be bypassed.

signature_colour_vector <- c("darkgreen","green","pink","goldenrod",
bio_process_vector <- c("spontaneous deamination","spontaneous deamination",
                        "defect DNA MMR","UV light exposure","unknown",
                        "IG hypermutation","POL E mutations","temozolomide",
AlexInitialArtif_sigInd_df <- data.frame(sig=colnames(AlexInitialArtif_sig_df))
AlexInitialArtif_sigInd_df$index <- seq_len(dim(AlexInitialArtif_sigInd_df)[1])
AlexInitialArtif_sigInd_df$colour <- signature_colour_vector
AlexInitialArtif_sigInd_df$process <- bio_process_vector

COSMIC_signature_colour_vector <- c("green","pink","goldenrod",
COSMIC_bio_process_vector <- c("spontaneous deamination","APOBEC",
                               "defect DNA DSB repair hom. recomb.",
                               "tobacco mutatgens, benzo(a)pyrene",
                               "defect DNA MMR, found in MSI tumors",
                               "UV light exposure","unknown","POL eta and SHM",
                               "altered POL E",
                               "alkylating agents, temozolomide",
                               "defect DNA MMR","unknown","unknown",
                               "associated w. small indels at repeats",
                               "unknown","aristocholic acid","unknown",
                               "aflatoxin","unknown","defect DNA MMR",
                               "unknown","unknown","tobacco chewing","unknown")
AlexCosmicValid_sigInd_df <- data.frame(sig=colnames(AlexCosmicValid_sig_df))
AlexCosmicValid_sigInd_df$index <- seq_len(dim(AlexCosmicValid_sigInd_df)[1])
AlexCosmicValid_sigInd_df$colour <- COSMIC_signature_colour_vector
AlexCosmicValid_sigInd_df$process <- COSMIC_bio_process_vector

YAPSA can also perform analyses based on other sets of mutational signatures. Details can be found in additional vignettes on signature-specific cutoffs and Indel signatures.

3.3 Performing an LCD analysis

Now we can start using the functions from the YAPSA package. We will start with a mutational signatures analysis using known signatures (the ones we loaded in the above paragraph). For this, we will use the functions LCD and LCD_complex_cutoff.

3.3.1 Building a mutational catalogue

This section uses functions which are to a large extent wrappers for functions in the package SomaticSignatures by Julian Gehring (Gehring et al. 2015).

word_length <- 3

lymphomaNature2013_mutCat_list <- 
    this_seqnames.field = "CHROM", this_start.field = "POS",
    this_end.field = "POS", this_PID.field = "PID",
    this_subgroup.field = "SUBGROUP",
    this_refGenome = BSgenome.Hsapiens.UCSC.hg19,
    this_wordLength = word_length)

The function create_mutation_catalogue_from_df returns a list object with several entries. We will use the one called matrix.

## [1] "matrix" "frame"
lymphomaNature2013_mutCat_df <-
4116738 4119027 4121361 4125240 4133511 4135350
C>A ACA 127 31 72 34 49 75
C>A ACC 104 36 39 19 36 80
C>A ACG 13 2 2 1 6 8
C>A ACT 102 33 48 22 47 56
C>A CCA 139 43 47 29 51 70
C>A CCC 66 34 35 7 25 42
C>A CCG 9 7 6 3 7 11
C>A CCT 167 47 50 32 58 84
C>A GCA 90 47 66 29 45 66

3.3.2 LCD analysis without any cutoff

The LCD function performs the decomposition of a mutational catalogue into a priori known signatures and the respective exposures to these signatures as described in the second section of this vignette. We use the signatures from (Alexandrov, Nik-Zainal, Wedge, Aparicio, et al. 2013) from the COSMIC website (

current_sig_df <- AlexCosmicValid_sig_df
current_sigInd_df <- AlexCosmicValid_sigInd_df
lymphomaNature2013_COSMICExposures_df <-

Some adaptation (extracting and reformatting the information which sample belongs to which subgroup):

COSMIC_subgroups_df <- 

The resulting signature exposures can be plotted using custom plotting functions. First as absolute exposures:

  in_exposures_df = lymphomaNature2013_COSMICExposures_df,
  in_subgroups_df = COSMIC_subgroups_df)
## Warning: `offset` is deprecated, use `location` instead.
Absoute exposures of the COSMIC signatures in the lymphoma mutational
        catalogues, no cutoff for the LCD (Linear Combination Decomposition)

Figure 1: Absoute exposures of the COSMIC signatures in the lymphoma mutational
catalogues, no cutoff for the LCD (Linear Combination Decomposition)

Here, as no colour information was given to the plotting function exposures_barplot, the identified signatures are coloured in a rainbow palette. If you want to assign colours to the signatures, this is possible via a data structure of type sigInd_df.

  in_exposures_df = lymphomaNature2013_COSMICExposures_df,
  in_signatures_ind_df = current_sigInd_df,
  in_subgroups_df = COSMIC_subgroups_df)
## Warning: `offset` is deprecated, use `location` instead.
Absoute exposures of the COSMIC signatures in the lymphoma mutational
    catalogues, no cutoff for the LCD (Linear Combination Decomposition)

Figure 2: Absoute exposures of the COSMIC signatures in the lymphoma mutational
catalogues, no cutoff for the LCD (Linear Combination Decomposition)

This figure has a colour coding which suits our needs, but there is one slight inconsistency: colour codes are assigned to all 30 provided signatures, even though some of them might not have any contributions in this cohort:

##         AC1         AC2         AC3         AC4         AC5         AC6 
##  7600.27742  6876.08962  7532.33628     0.00000 11400.47725   165.58975 
##         AC7         AC8         AC9        AC10        AC11        AC12 
##  1360.82451 10792.42576 40780.45251   750.23999  2330.47206  1416.84002 
##        AC13        AC14        AC15        AC16        AC17        AC18 
##  1278.21673   972.57536  1277.88738  1616.08615 10715.25907  1345.94448 
##        AC19        AC20        AC21        AC22        AC23        AC24 
##  1269.86003   231.99919   909.70554    48.66650    61.22061     0.00000 
##        AC25        AC26        AC27        AC28        AC29        AC30 
##   639.25443   258.02212   382.52388  4768.13630    76.81403  4745.16264

This can be overcome by using LCD_complex_cutoff. It requires an additional parameter: in_cutoff_vector; this is already the more general framework which will be explained in more detail in the following section.

zero_cutoff_vector <- rep(0,dim(current_sig_df)[2])
CosmicValid_cutoffZero_LCDlist <- LCD_complex_cutoff(
  in_mutation_catalogue_df = lymphomaNature2013_mutCat_df,
  in_signatures_df = current_sig_df,
  in_cutoff_vector = zero_cutoff_vector,
  in_sig_ind_df = current_sigInd_df)

We can re-create the subgroup information (even though this is identical to the already determined one):

COSMIC_subgroups_df <- 

And if we plot this, we obtain:

  in_exposures_df = CosmicValid_cutoffZero_LCDlist$exposures,
  in_signatures_ind_df = CosmicValid_cutoffZero_LCDlist$out_sig_ind_df,
  in_subgroups_df = COSMIC_subgroups_df)
## Warning: `offset` is deprecated, use `location` instead.