---
title: "1. Usage of YAPSA"
author: "Daniel Huebschmann"
date: "26/08/2015"
vignette: >
  %\VignetteIndexEntry{1. Usage of YAPSA}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}  
output:
  BiocStyle::html_document:
    toc: yes
references:
- author:
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    given: LB
  - family: Nik-Zainal
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    volume: 500
    year: 2013
  publisher: Nature Publishing Group
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  title: > 
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  container-title: Genome Biology
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  publisher: BioMed Central
  title: > 
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  title: >
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  id: Alex2020
  issued:
    month: 02
    year: 2020
  title: 'The Repertoire of Mutational Signatures in Human Cancer'
---

  
```{r load_style, warning=FALSE, message=FALSE, results="hide"}
library(BiocStyle)
```

# Introduction {#introduction}

The concept of mutational signatures was introduced in a series of papers by 
Ludmil Alexandrov et al. [@Alex2013] and [@Alex_CellRep2013]. A computational 
framework was published [@Alex_package2012] 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 [@Alex2013] 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 [@Alex2013], 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 <https://en.wikipedia.org/wiki/Non-negative_matrix_factorization>):

![NMF](NMF.png)

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. 
[@Alex2013] performs not only the NMF decomposition itself, but also identifies 
the appropriate number of signatures by an iterative procedure.

Another software, the Bioconductor package `r Biocpkg("SomaticSignatures")` to 
perform analyses of mutational signatures, is available [@Gehring_article2015]. 
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.

  
# The YAPSA package {#YAPSA_package}

In a context where mutational signatures $W$ are already known (because they 
were decribed and published as in [@Alex2013] or they are available in a 
database as under <http://cancer.sanger.ac.uk/cosmic/signatures>), 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` (**l**inear **c**ombination **d**ecomposition)
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.


## Linear Combination Decomposition (LCD) {#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:

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

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  
`r CRANpkg("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: `r CRANpkg("deconstructSigs")` [@Rosenthal_2016]. One difference 
between `LCD_complex_cutoff` as described here in `YAPSA` and the corresponding
function `whichSignatures` in `r CRANpkg("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 `r CRANpkg("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.

## 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](#sigSpecCutoffs), optimal signature-specific cutoffs are presented in 
[2. Signature-specific cutoffs](http://bioconductor.org/packages/3.11/bioc/vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html).

## 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_Bioinformatics2009]. Details 
can be found in 
[3. Confidence Intervals](http://bioconductor.org/packages/3.11/bioc/vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.html).

## 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](http://bioconductor.org/packages/3.11/bioc/vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.html)

## Indel signatures

Recently a new and extended set of mutational signatures was published by the 
Pan Cancer Analysis of Whole Genomes (PCAWG) consortium [@Alex2020]. 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](http://bioconductor.org/packages/3.11/bioc/vignettes/YAPSA/inst/doc/vignettes_Indel.html)

  
# 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. [-@Alex2013]. First we have 
to load this data and get an overview ([first subsection](#example-data)). Then
we will load data on published signatures 
([second subsection](#loading-the-signature-information)). Only in the 
[third subsection](#performing-an-lcd-analysis) we will actually start using 
the YAPSA functions.

```{r, warning=FALSE, message=FALSE}
library(YAPSA)
library(knitr)
opts_chunk$set(echo=TRUE)
opts_chunk$set(fig.show='asis')
```

## Example data

In the following, we will load and get an overview of the data used in the 
analysis by Alexandrov et al. [@Alex2013]

### Loading example data

```{r loadLymphoma}
data("lymphoma_Nature2013_raw")
```

This creates a dataframe with `r dim(lymphoma_Nature2013_raw_df)[1]` rows. It 
is equivalent to executing the R code

```{r loadLymphomaFtp, eval=FALSE}
lymphoma_Nature2013_ftp_path <- paste0(
  "ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/",
  "somatic_mutation_data/Lymphoma B-cell/",
  "Lymphoma B-cell_clean_somatic_mutations_",
  "for_signature_analysis.txt")
lymphoma_Nature2013_raw_df <- read.csv(file=lymphoma_Nature2013_ftp_path,
                                       header=FALSE,sep="\t")
```

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:

```{r adaptData}
names(lymphoma_Nature2013_raw_df) <- c("PID","TYPE","CHROM","START",
                                       "STOP","REF","ALT","FLAG")
lymphoma_Nature2013_df <- subset(lymphoma_Nature2013_raw_df,TYPE=="subs",
                                 select=c(CHROM,START,REF,ALT,PID))
names(lymphoma_Nature2013_df)[2] <- "POS"
kable(head(lymphoma_Nature2013_df), 
      caption = "First rows of the file containing the SNV variant calls.")
```

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 
`r dim(lymphoma_Nature2013_df)[1]` variants or rows. Note that there are
`r length(unique(lymphoma_Nature2013_df$PID))` different samples:

```{r PIDinfo}
unique(lymphoma_Nature2013_df$PID)
```

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:

```{r annotateSubgroup, warning=FALSE, message=FALSE}
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$PID)
lymphoma_Nature2013_df <- lymphoma_Nature2013_df[MMML_ind,]
data(lymphoma_PID)
for(my_PID in rownames(lymphoma_PID_df)) {
  PID_ind <- which(as.character(lymphoma_Nature2013_df$PID)==my_PID)
  lymphoma_Nature2013_df$SUBGROUP[PID_ind] <-
    lymphoma_PID_df$subgroup[which(rownames(lymphoma_PID_df)==my_PID)]
}
lymphoma_Nature2013_df$SUBGROUP <- factor(lymphoma_Nature2013_df$SUBGROUP)
unique(lymphoma_Nature2013_df$SUBGROUP)
```


### 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...

```{r intermutDistances, warning=FALSE, message=FALSE, results="hide"}
lymphoma_Nature2013_df <- translate_to_hg19(lymphoma_Nature2013_df,"CHROM")
lymphoma_Nature2013_df$change <- 
  attribute_nucleotide_exchanges(lymphoma_Nature2013_df)
lymphoma_Nature2013_df <- 
  lymphoma_Nature2013_df[order(lymphoma_Nature2013_df$PID,
                               lymphoma_Nature2013_df$CHROM,
                               lymphoma_Nature2013_df$POS),]
lymphoma_Nature2013_df <- annotate_intermut_dist_cohort(lymphoma_Nature2013_df,
                                                        in_PID.field="PID")
data("exchange_colour_vector")
lymphoma_Nature2013_df$col <- 
  exchange_colour_vector[lymphoma_Nature2013_df$change]
```

Now we can select one sample and make the rainfall plot. The plot function used
here relies on the package `r Biocpkg("gtrellis")` by Zuguang Gu 
[@gtrellis2015].

```{r makeRainfallPlot, fig.cap="Rainfall plot in a trellis structure."}
choice_PID <- "4121361"
PID_df <- subset(lymphoma_Nature2013_df,PID==choice_PID)
#trellis_rainfall_plot(PID_df,in_point_size=unit(0.5,"mm"))
```

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


## Loading the signature information

As stated [above](#LCD), 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. [@Alex2013], and ii) an updated and curated
current set of mutational signatures is maintained by Ludmil Alexandrov at
<http://cancer.sanger.ac.uk/cosmic/signatures>. 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:

```{r, loadStoredSigData}
data(sigs)
```


### Loading the initial set of signatures

We first load the (older) set of signatures as published in Alexandrov et al. 
[@Alex2013]:

```{r processOldSigs}
Alex_signatures_path <- paste0("ftp://ftp.sanger.ac.uk/pub/cancer/",
                               "AlexandrovEtAl/signatures.txt")
AlexInitialArtif_sig_df <- read.csv(Alex_signatures_path,header=TRUE,sep="\t")
kable(AlexInitialArtif_sig_df[c(1:9),c(1:4)])
```

We will now reformat the dataframe:

```{r reformatOldSigs}
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",
                                       names(AlexInitialArtif_sig_df))
rownames(AlexInitialArtif_sig_df) <- Alex_rownames
kable(AlexInitialArtif_sig_df[c(1:9),c(1:6)],
      caption="Exemplary data from the initial Alexandrov signatures.")
```

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

```{r filterValidatedSignatures}
AlexInitialValid_sig_df <- AlexInitialArtif_sig_df[,grep("^A[0-9]+",
                                          names(AlexInitialArtif_sig_df))]
number_of_Alex_validated_sigs <- dim(AlexInitialValid_sig_df)[2]
```

We are left with `r number_of_Alex_validated_sigs` signatures.

### Loading the updated set of mutational signatures

An updated and curated set of mutational signatures is maintained by Ludmil 
Alexandrov at <http://cancer.sanger.ac.uk/cosmic/signatures>. We will use this 
set for the following analysis. In particular, we will use the signatures stored 
in <http://cancer.sanger.ac.uk/cancergenome/assets/signatures_probabilities.txt> 
which for convenience we have stored in a data frame in YAPSA.

```{r loadSigs}
data(AlexCosmicValid_sig_df)
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",
                                         names(AlexCosmicValid_sig_df))
rownames(AlexCosmicValid_sig_df) <- Alex_COSMIC_rownames
kable(AlexCosmicValid_sig_df[c(1:9),c(1:6)],
      caption="Exemplary data from the updated Alexandrov signatures.")
```

This results in a dataframe containing `r number_of_Alex_COSMIC_sigs` 
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. [@Alex2013] 
for the initial signatures.

```{r reorderFeatures}
COSMIC_order_ind <- match(Alex_rownames,Alex_COSMIC_rownames)
AlexCosmicValid_sig_df <- AlexCosmicValid_sig_df[COSMIC_order_ind,]
kable(AlexCosmicValid_sig_df[c(1:9),c(1:6)],
      caption=paste0("Exemplary data from the updated Alexandrov ",
                     "signatures, rows reordered."))
```

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.


### 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.

```{r, buildSigIndDf}
signature_colour_vector <- c("darkgreen","green","pink","goldenrod",
                             "lightblue","blue","orangered","yellow",
                             "orange","brown","purple","red",
                             "darkblue","magenta","maroon","yellowgreen",
                             "violet","lightgreen","sienna4","deeppink",
                             "darkorchid","seagreen","grey10","grey30",
                             "grey50","grey70","grey90")
bio_process_vector <- c("spontaneous deamination","spontaneous deamination",
                        "APOBEC","BRCA1_2","Smoking","unknown",
                        "defect DNA MMR","UV light exposure","unknown",
                        "IG hypermutation","POL E mutations","temozolomide",
                        "unknown","APOBEC","unknown","unknown","unknown",
                        "unknown","unknown","unknown","unknown","unknown",
                        "nonvalidated","nonvalidated","nonvalidated",
                        "nonvalidated","nonvalidated")
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",
                                    "lightblue","blue","orangered","yellow",
                                    "orange","brown","purple","red",
                                    "darkblue","magenta","maroon",
                                    "yellowgreen","violet","lightgreen",
                                    "sienna4","deeppink","darkorchid",
                                    "seagreen","grey","darkgrey",
                                    "black","yellow4","coral2","chocolate2",
                                    "navyblue","plum","springgreen")
COSMIC_bio_process_vector <- c("spontaneous deamination","APOBEC",
                               "defect DNA DSB repair hom. recomb.",
                               "tobacco mutatgens, benzo(a)pyrene",
                               "unknown",
                               "defect DNA MMR, found in MSI tumors",
                               "UV light exposure","unknown","POL eta and SHM",
                               "altered POL E",
                               "alkylating agents, temozolomide",
                               "unknown","APOBEC","unknown",
                               "defect DNA MMR","unknown","unknown",
                               "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](http://bioconductor.org/packages/3.11/bioc/vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html) and 
[Indel signatures](http://bioconductor.org/packages/3.11/bioc/vignettes/YAPSA/inst/doc/vignettes_Indel.html).


## 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`.

### 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_article2015].

```{r loadLibraries, warning=FALSE, message=FALSE}
library(BSgenome.Hsapiens.UCSC.hg19)
```

```{r buildMutationalCatalogue, results="hide"}
word_length <- 3

lymphomaNature2013_mutCat_list <- 
  create_mutation_catalogue_from_df(
    lymphoma_Nature2013_df,
    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`.

```{r displayStructureMutationCatalogueList}
names(lymphomaNature2013_mutCat_list)
lymphomaNature2013_mutCat_df <- as.data.frame(
  lymphomaNature2013_mutCat_list$matrix)
kable(lymphomaNature2013_mutCat_df[c(1:9),c(5:10)])
```
\newpage


### 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
[@Alex2013] from the COSMIC website
(<https://cancer.sanger.ac.uk/cosmic/signatures_v2>).

```{r LCD, warning=FALSE}
current_sig_df <- AlexCosmicValid_sig_df
current_sigInd_df <- AlexCosmicValid_sigInd_df
lymphomaNature2013_COSMICExposures_df <-
  LCD(lymphomaNature2013_mutCat_df,current_sig_df)
```

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

```{r makeSubgroupsDfLCD}
COSMIC_subgroups_df <- 
  make_subgroups_df(lymphoma_Nature2013_df,
                    lymphomaNature2013_COSMICExposures_df)
```

The resulting signature exposures can be plotted using custom plotting 
functions. First as absolute exposures:
```{r captionBarplot, echo=FALSE}
cap <- "Absoute exposures of the COSMIC signatures in the lymphoma mutational
        catalogues, no cutoff for the LCD (Linear Combination Decomposition)"
```

```{r exposuresBarplotLCD, fig.width=6, fig.height=5, fig.cap=cap}
exposures_barplot(
  in_exposures_df = lymphomaNature2013_COSMICExposures_df,
  in_subgroups_df = COSMIC_subgroups_df)
```

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`.
```{r captionBarplot2, echo=FALSE}
cap <- "Absoute exposures of the COSMIC signatures in the lymphoma mutational
    catalogues, no cutoff for the LCD (Linear Combination Decomposition)"
```

```{r exposuresBarplotLCDsigInd, fig.width=6, fig.height=5, fig.cap=cap}
exposures_barplot(
  in_exposures_df = lymphomaNature2013_COSMICExposures_df,
  in_signatures_ind_df = current_sigInd_df,
  in_subgroups_df = COSMIC_subgroups_df)
```

This figure has a colour coding which suits our needs, but there is one slight 
inconsistency: colour codes are assigned to all `r dim(current_sig_df)[2]` 
provided signatures, even though some of them might not have any contributions 
in this cohort:

```{r rowSumsInCohort}
rowSums(lymphomaNature2013_COSMICExposures_df)
```

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.

```{r LCDcomplexCutoffZero}
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):

```{r applyMakeSubgroupsDfZero}
COSMIC_subgroups_df <- 
  make_subgroups_df(lymphoma_Nature2013_df,
                    CosmicValid_cutoffZero_LCDlist$exposures)
```

And if we plot this, we obtain:
```{r captionBarplot3, echo=FALSE}
cap="Absoute exposures of the COSMIC signatures in the lymphoma mutational
    catalogues, no cutoff for the LCD (Linear Combination Decomposition)."
```

```{r exposuresBarplotAbsCutoffZero, fig.width=6, fig.height=5, fig.cap=cap}
exposures_barplot(
  in_exposures_df = CosmicValid_cutoffZero_LCDlist$exposures,
  in_signatures_ind_df = CosmicValid_cutoffZero_LCDlist$out_sig_ind_df,
  in_subgroups_df = COSMIC_subgroups_df)
```

Note that this time, only the 
`r dim(CosmicValid_cutoffZero_LCDlist$exposures)[1]` signatures which actually 
have a contribution to this cohort are displayed in 
the legend.

Of course, also relative exposures may be plotted:
```{r caption_barplot_4, echo=FALSE}
cap="Relative exposures of the COSMIC signatures in the lymphoma mutational
    catalogues, no cutoff for the LCD (Linear Combination Decomposition)."
```

```{r exposuresBarplotRelCutoffZero, fig.width=6, fig.height=5, fig.cap=cap}
exposures_barplot(
  in_exposures_df = CosmicValid_cutoffZero_LCDlist$norm_exposures,
  in_signatures_ind_df = CosmicValid_cutoffZero_LCDlist$out_sig_ind_df,
  in_subgroups_df = COSMIC_subgroups_df)
```


### LCD analysis with a generalized cutoff

Now let's rerun the analysis with a cutoff to discard signatures with 
insufficient cohort-wide contribution. 

```{r definitionCutoff}
my_cutoff <- 0.06
```

The cutoff of `r my_cutoff` means that a signature is kept if it's exposure 
represents at least `r my_cutoff*100`% of all SNVs in the cohort. We will use 
the function `LCD_complex_cutoff` instead of `LCD`.

```{r LCDcomplexCutoffCutoffGen, warning=FALSE}
general_cutoff_vector <- rep(my_cutoff,dim(current_sig_df)[2])
CosmicValid_cutoffGen_LCDlist <- LCD_complex_cutoff(
  in_mutation_catalogue_df = lymphomaNature2013_mutCat_df,
  in_signatures_df = current_sig_df,
  in_cutoff_vector = general_cutoff_vector,
  in_sig_ind_df = current_sigInd_df)
```

At the chosen cutoff of `r my_cutoff`, we are left with 
`r dim(CosmicValid_cutoffGen_LCDlist$out_sig_ind_df)[1]` signatures. We can 
look at these signatures in detail and their attributed biological processes:

```{r}
kable(CosmicValid_cutoffGen_LCDlist$out_sig_ind_df, row.names=FALSE,
      caption=paste0("Signatures with cohort-wide exposures > ",my_cutoff))
```

Again we can plot absolute exposures:

```{r captionBarplot5, echo=FALSE}
cap="Absoute exposures of the COSMIC signatures in the lymphoma mutational
    catalogues, cutoff of 6% for the LCD (Linear Combination Decomposition)."
```

```{r exposuresBarplotAbsCutoffGen, fig.width=6, fig.height=4, fig.cap=cap}
exposures_barplot(
  in_exposures_df = CosmicValid_cutoffGen_LCDlist$exposures,
  in_signatures_ind_df = CosmicValid_cutoffGen_LCDlist$out_sig_ind_df,
  in_subgroups_df = COSMIC_subgroups_df)
```

And relative exposures:
```{r captionBarplot6, echo=FALSE}
cap="Relative exposures of the COSMIC signatures in the lymphoma mutational
    catalogues, cutoff of 6% for the LCD (Linear Combination Decomposition)."
```

```{r exposuresBarplotRelGutoffGen, fig.width=6, fig.height=4, fig.cap=cap}
exposures_barplot(
  in_exposures_df = CosmicValid_cutoffGen_LCDlist$norm_exposures,
  in_signatures_ind_df = CosmicValid_cutoffGen_LCDlist$out_sig_ind_df,
  in_subgroups_df = COSMIC_subgroups_df)
```


### LCD analysis with signature-specific cutoffs {#sigSpecCutoffs}

When using `LCD_complex_cutoff`, we have to supply a vector of cutoffs with as 
many entries as there are signatures. In the analysis carried out above, these 
were all equal, but this is not a necessary condition. Indeed it may make sense
to provide different cutoffs for different signatures.

```{r makeSpecificCutoffVector}
specific_cutoff_vector <- general_cutoff_vector
specific_cutoff_vector[c(1,5)] <- 0
specific_cutoff_vector
```

In this example, the cutoff for signatures AC1 and AC5 is thus set to 0, 
whereas the cutoffs for all other signatures remains at 0.06. Running the 
function `LCD_complex_cutoff` is completely analogous:

```{r LCDcomplexCutoffCutoffSpec, warning=FALSE}
CosmicValid_cutoffSpec_LCDlist <- LCD_complex_cutoff(
  in_mutation_catalogue_df = lymphomaNature2013_mutCat_df,
  in_signatures_df = current_sig_df,
  in_cutoff_vector = specific_cutoff_vector,
  in_sig_ind_df = current_sigInd_df)
```

Plotting absolute exposures for visualization:
```{r captionBarplot7, echo=FALSE}
cap="Absoute exposures of the COSMIC signatures in the lymphoma mutational
    catalogues, cutoff of 6% for the LCD (Linear Combination Decomposition)."
```

```{r exposuresBarplotAbsCutoffSpec, fig.width=6, fig.height=4, fig.cap=cap}
exposures_barplot(
  in_exposures_df = CosmicValid_cutoffSpec_LCDlist$exposures,
  in_signatures_ind_df = CosmicValid_cutoffSpec_LCDlist$out_sig_ind_df,
  in_subgroups_df = COSMIC_subgroups_df)
```

And relative exposures:
```{r captionBarplot8, echo=FALSE}
cap="Relative exposures of the COSMIC signatures in the lymphoma mutational
    catalogues, cutoff of 6% for the LCD (Linear Combination Decomposition)."
```

```{r exposuresBarplotRelCutoffSpec, fig.width=6, fig.height=4, fig.cap=cap}
exposures_barplot(
  in_exposures_df = CosmicValid_cutoffSpec_LCDlist$norm_exposures,
  in_signatures_ind_df = CosmicValid_cutoffSpec_LCDlist$out_sig_ind_df,
  in_subgroups_df = COSMIC_subgroups_df)
```

Note that the signatures extracted with the signature-specific cutoffs are the 
same in the example displayed here. Depending on the analyzed cohort and the 
choice of cutoffs, the extracted signatures may vary considerably. A unique 
feature of YAPSA is that it also provides optimal signature-specific cutoffs, a 
topic explained in a separate [vignette](http://bioconductor.org/packages/3.11/bioc/vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html).



## Cluster samples based on their signature exposures

To identify groups of samples which were exposed to similar mutational 
processes, the exposure vectors of the samples can be compared. The YAPSA 
package provides a custom function for this task: `complex_heatmap_exposures`, 
which uses the package `r Biocpkg("ComplexHeatmap")` by Zuguang Gu 
[@ComplexHeatmap2015]. It produces output as follows:
```{r captionHeatmap, echo=FALSE}
cap="Clustering of Samples and Signatures based on the relative exposures of
    the COSMIC signatures in the lymphoma mutational catalogues."
```

```{r applyHeatmapExposures, fig.width=6, fig.height=4, fig.cap=cap}
complex_heatmap_exposures(CosmicValid_cutoffGen_LCDlist$norm_exposures,
                          COSMIC_subgroups_df,
                          CosmicValid_cutoffGen_LCDlist$out_sig_ind_df,
                          in_data_type="norm exposures",
                          in_subgroup_colour_column="col",
                          in_method="manhattan",
                          in_subgroup_column="subgroup")
```

If you are interested only in the clustering and not in the heatmap 
information, you could also use `hclust_exposures`:
```{r caption_hclust, echo=FALSE}
cap="Clustering of the Samples based on the relative exposures of the
    COSMIC signatures in the lymphoma mutational catalogues."
```

```{r applyHclustExposures, fig.width=6, fig.height=3, fig.cap=cap}
hclust_list <- 
  hclust_exposures(CosmicValid_cutoffGen_LCDlist$norm_exposures,
                   COSMIC_subgroups_df,
                   in_method="manhattan",
                   in_subgroup_column="subgroup")
```

The dendrogram produced by either the function `complex_heatmap_exposures` or 
the function `hclust_exposures` can be cut to yield signature exposures specific
to subgroups of PIDs.
```{r caption_heatmap_2, echo=FALSE}
cap=paste0("PIDs labelled by the clusters extracted from the
            signature analysis.")
```

```{r clusterPIDsSigInfo, fig.width=6, fig.height=4, fig.cap=cap}
cluster_vector <- cutree(hclust_list$hclust,k=4)
COSMIC_subgroups_df$cluster <- cluster_vector
subgroup_colour_vector <- rainbow(length(unique(COSMIC_subgroups_df$cluster)))
COSMIC_subgroups_df$cluster_col <- 
  subgroup_colour_vector[factor(COSMIC_subgroups_df$cluster)]
complex_heatmap_exposures(CosmicValid_cutoffGen_LCDlist$norm_exposures,
                          COSMIC_subgroups_df,
                          CosmicValid_cutoffGen_LCDlist$out_sig_ind_df,
                          in_data_type="norm exposures",
                          in_subgroup_colour_column="cluster_col",
                          in_method="manhattan",
                          in_subgroup_column="cluster")
```


# References
