---
title: "YAPSA"
author: "Daniel Huebschmann"
date: "26/08/2015"
vignette: >
%\VignetteIndexEntry{YAPSA}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
output:
BiocStyle::html_document:
toc: yes
references:
- author:
- family: Alexandrov
given: LB
- family: Nik-Zainal
given: S
- family: Wedge
given: DC
- family: Aparicio
given: SA
- family: Behjati
given: S
- family: Biankin
given: AV
- family: Bignell
given: GR
- family: Bolli
given: N
- family: Borg
given: A
- family: Borresen-Dale
given: AL
- family: Boyault
given: S
- family: Burkhardt
given: B
- family: Butler
given: AP
- family: Caldas
given: C
- family: Davies
given: HR
- family: Desmedt
given: C
- family: Eils
given: R
- family: Eyfjörd
given: JE
- family: Greaves
given: M
- family: Hosoda
given: F
- family: Hutter
given: B
- family: Ilicic
given: T
- family: Imbeaud
given: S
- family: Imielinski
given: M
- family: Jäger
given: N
- family: Jones
given: DT
- family: Jones
given: D
- family: Knappskog
given: S
- family: Kool
given: M
- family: Lakhani
given: SR
- family: Lopez-Otin
given: C
- family: Martin
given: S
- family: Munshi
given: NC
- family: Nakamura
given: H
- family: Northcott
given: PA
- family: Pajic
given: M
- family: Papaemmanuil
given: E
- family: Paradiso
given: A
- family: Pearson
given: JV
- family: Puente
given: XS
- family: Raine
given: K
- family: Ramakrishna
given: M
- family: Richardson
given: AL
- family: Richter
given: J
- family: Rosenstiel
given: P
- family: Schlesner
given: M
- family: Schumacher
given: TN
- family: Span
given: PN
- family: Teague
given: JW
- family: Tokoti
given: Y
- family: Tutt
given: AN
- family: Valdes-Mas
given: R
- family: van Buuren
given: MM
- family: van't Veer
given: L
- family: Vincent-Salomon
given: A
- family: Waddell
given: N
- family: Yates
given: LR
- family: Australian Pancreatic Cancer Initiative
given:
- family: ICGC Breast Cancer Consortium
given:
- family: ICGC MMML-Seq Consortium
given:
- family: ICGC PedBrain
given:
- family: Zucman-Rossi
given: J
- family: Futreal
given: PA
- family: McDermott
given: U
- family: Lichter
given: P
- family: Meyerson
given: M
- family: Grimmond
given: SM
- family: Siebert
given: R
- family: Campo
given: E
- family: Shibata
given: T
- family: Pfister
given: SM
- family: Campbell
given: PJ
- family: Stratton
given: MR
container-title: Nature
id: Alex2013
issued:
month: August
volume: 500
year: 2013
publisher: Nature Publishing Group
title: 'Signatures of Mutational Processes in Cancer'
- author:
- family: Alexandrov
given: LB
id: Alex_package2012
issued:
year: 2012
title: 'WTSI Mutational Signature Framework'
- author:
- family: Alexandrov
given: LB
- family: Nik-Zainal
given: S
- family: Wedge
given: DC
- family: Campbell
given: PJ
- family: Stratton
given: MR
container-title: Cell Reports
id: Alex_CellRep2013
issued:
year: 2013
title: >
Deciphering signatures of mutational processes operative in human cancer
- author:
- family: Gehring
given: Julian
- family: Fischer
given: Bernd
- family: Lawrence
given: Michael
- family: Huber
given: Wolfgang
container-title: Bioinformatics
id: Gehring_article2015
issued:
year: 2015
publisher: Oxford Journals
title: >
SomaticSignatures: inferring mutational signatures from single-nucleotide
variants
- author:
- family: Rosenthal
given: Rachel
- family: McGranahan
given: Nicholas
- family: Herrero
given: Javier
- family: Taylor
given: Barry S.
- family: Swanton
given: Charles
container-title: Genome Biology
id: Rosenthal_2016
issued:
year: 2016
publisher: BioMed Central
title: >
DeconstructSigs: delineating mutational processes in single tumors
distinguishes DNA repair deficiencies and patterns of carcinoma evolution.
- author:
- family: Wang
given: Yong
- family: Lawson
given: Charles L.
- family: Hanson
given: Richard J
container-title: R-package version 1.1-1
id: lsei_package2015
issued:
year: 2015
title: >
lsei: Solving Least Squares Problems under Equality/Inequality Constraints
- title: 'Genome Level Trellis Layout'
author:
- family: Gu
given: Zuguang
container-title: R package version 1.0.0
id: gtrellis2015
issued:
year: 2015
- author:
- family: Gu
given: Z
container-title: R package version 1.4.4
id: ComplexHeatmap2015
issued:
year: 2015
title: 'ComplexHeatmap: Making Complex Heatmaps'
---
```{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** (**nonnegative 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>):
Of course the whole concept only leads to a reduction in complexity if $l < n$,
i.e. if the number of signatures is smaller than the number of features, as
indicated in the above figure. Note that the NMF itself solves the above
problem for a given number of signatures $l$. 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]
which 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 a minimum size, i.e.
a minimum number of samples and minimum 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. The YAPSA package provides additional functions for signature
analysis, e.g. for stratifying the mutational catalogue to determine signature
exposures in different strata, part of which will be discussed later in this
vignette.
## 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 the function `lsei` from the package `r CRANpkg("lsei")`
[@lsei_package2015]. 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. Furthermore, YAPSA has a function for a stratified
analysis as described in the following paragraph.
## 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. In the following these categories have to be exclusive,
i.e. one SNV must be in one and only one category. The categories will be
called **strata** in the following and the procedure **stratification**. The
number of strata will be denoted by $s$. For example one could evaluate whether
an SNV falls into a region of high, intermediate or low mutation density by
applying meaningful cutoffs on intermutation distance. Following the above
convention, there are three strata: high, intermediate and low mutation
density. If we have already performed an analysis of mutational signatures for
the whole mutational catalogue of the cohort, we have identified a set of
signatures of interest and the corresponding exposures. We now could ask the
question if some of the signatures are enriched or depleted in one or the other
stratum, yielding a strata-specific signature enrichment and depletion pattern.
The function `SMC` (Stratification of the Mutational Catalogue) solves the
stratified optimization problem:
(@SMC_formula) $$
\begin{aligned}
\min_{H_{(\cdot j)}^k \in \mathbb{R}^l}||W \cdot H_{(\cdot j)}^{k} -
V_{(\cdot j)}^{k}|| \quad \forall j,k \\
\textrm{under the constraint of non-negativity:} \quad H_{(ij)}^{k} >= 0
\quad \forall i,j,k \\
\textrm{and the additional contraint:} \quad \sum_{k=1}^{s} H^{k} = H \\
\textrm{where H is defined by the optimization:} \quad \min_{H_{(\cdot j)}
\in \mathbb{R}^l}||W \cdot H_{(\cdot j)} - V_{(\cdot j)}|| \quad \forall j \\
\textrm{also under the constraint of non-negativity:} \quad H_{(ij)} >= 0
\quad \forall i,j \quad \textrm{and} \quad V = \sum_{k=1}^{s} V^{k}
\end{aligned}
$$
Remember that $j$ is the index over samples, $m$ is the number of samples, $i$
is the index over signatures, $l$ is the number of signatures, $k$ is the index
over strata and $s$ is the number of strata. Note that the last two lines of
equation (@SMC_formula) correspond to equation (@LCD_formula). The very last
part of equation (@SMC_formula) reflects the additivity of the stratified
mutational catalogues $V^{k}$ which is due to the fact that by definition the
sets of SNVs they were constructed from (i.e. the strata) are exclusive.
The SMC-procedure can also be applied when an NMF analysis has been performed
and the exposures $\widetilde{H}$ of this NMF analysis should be used as input
and constraint for the SMC. It then solves the task:
(@SMC_NMF_input_formula) $$
\begin{aligned}
\min_{H_{(\cdot j)}^k \in \mathbb{R}^l}||W \cdot H_{(\cdot j)}^{k} -
V_{(\cdot j)}^{k}|| \quad \forall j,k \\
\textrm{under the constraint of non-negativity:} \quad H_{(ij)}^{k} >= 0
\quad \forall i,j,k \\
\textrm{and the additional contraint:} \quad
\sum_{k=1}^{s} H^{k} = \widetilde{H} \\
\end{aligned}
$$
Applying `SMC` that way, the initial LCD decomposition of the unstratified
mutational catalogue is omitted and it's result replaced by the exposures
extracted by the NMF analysis.
# 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 load_lymphoma}
data("lymphoma_Nature2013_raw")
```
This created a data frame with `r dim(lymphoma_Nature2013_raw_df)[1]` rows. It
is equivalent to executing the R code
```{r load_lymphoma_ftp, 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 adapt_data}
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))
```
Here, we have selected only the variants characterized as `subs` (those are the
single nucleotide variants 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$PID))` different samples:
```{r PID_info}
unique(lymphoma_Nature2013_df$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 annotate_subgroup, 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$SUBGROUP)
```
### Displaying example data
Rainfall plots provide a quick overview of the mutational load of a sample. To
this end we have to compute the intermutational distances. But first we still
do some reformatting...
```{r intermut_distances, warning=FALSE, message=FALSE, results="hide"}
lymphoma_Nature2013_df <- translate_to_hg19(lymphoma_Nature2013_df,"CHROM")
lymphoma_Nature2013_df$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 make_rainfall_plot, fig.cap="Example 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, load_stored_sig_data}
data(sigs)
```
However, the curated set of signatures might change in the future, therefore it
is recommended to rebuild it from the above mentioned website as described in
the following subsections.
### Loading the initial set of signatures
We first load the (older) set of signatures as published in Alexandrov et al.
[@Alex2013]:
```{r process_old_sigs}
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 data frame:
```{r reformat_old_sigs}
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 data frame 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 filter_validated_signatures}
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:
```{r load_sigs}
Alex_COSMIC_signatures_path <-
paste0("http://cancer.sanger.ac.uk/cancergenome/",
"assets/signatures_probabilities.txt")
AlexCosmicValid_sig_df <- read.csv(Alex_COSMIC_signatures_path,
header=TRUE,sep="\t")
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 data frame 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 reorder_features}
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 data frame containing meta information about the signatures. In that
data frame 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 data frame. However, the respective data
frames are also stored in the package. If loaded by `data(sigs)` the following
code block can be bypassed.
```{r, build_sig_ind_df}
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$process <- COSMIC_bio_process_vector
```
## Performing an LCD analysis
Now we can start using the functions from the YAPSA package. We will start with
a mutational signatures analysis using known signatures (the ones we loaded in
the above paragraph). For this, we will use the functions `LCD` and
`LCD_complex_cutoff`.
### Building a mutational catalogue
This section uses functions which are to a large extent wrappers for functions
in the package SomaticSignatures by Julian Gehring [@Gehring_article2015].
```{r load_libraries, warning=FALSE, message=FALSE}
library(BSgenome.Hsapiens.UCSC.hg19)
```
```{r build_mutational_catalogue, results="hide"}
word_length <- 3
lymphomaNature2013_mutCat_list <-
create_mutation_catalogue_from_df(
lymphoma_Nature2013_df,
this_seqnames.field = "CHROM", this_start.field = "POS",
this_end.field = "POS", this_PID.field = "PID",
this_subgroup.field = "SUBGROUP",
this_refGenome = BSgenome.Hsapiens.UCSC.hg19,
this_wordLength = word_length)
```
The function `create_mutation_catalogue_from_df` returns a list object with
several entries. We will use the one called `matrix`.
```{r display_structure_mutation_catalogue_list}
names(lymphomaNature2013_mutCat_list)
lymphomaNature2013_mutCat_df <- as.data.frame(
lymphomaNature2013_mutCat_list$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 "new" signatures
from the COSMIC website.
```{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 make_subgroups_df_LCD}
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 exposures_barplot_LCD, fig.width=6, fig.height=5, fig.cap="Absoute exposures of the COSMIC signatures in the lymphoma mutational catalogues, no cutoff for the LCD (Linear Combination Decomposition)."}
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 exposures_barplot_LCD_sig_ind, fig.width=6, fig.height=5, fig.cap="Absoute exposures of the COSMIC signatures in the lymphoma mutational catalogues, no cutoff for the LCD (Linear Combination Decomposition)."}
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 rowSums_in_cohort}
rowSums(lymphomaNature2013_COSMICExposures_df)
```
This can be overcome by using `LCD_complex_cutoff`. Is 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 LCD_complex_cutoff_cutoffZero}
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 apply_make_subgroups_df_cutoffZero}
COSMIC_subgroups_df <-
make_subgroups_df(lymphoma_Nature2013_df,
CosmicValid_cutoffZero_LCDlist$exposures)
```
And if we plot this, we obtain:
```{r exposures_barplot_abs_cutoffZero, fig.width=6, fig.height=5, fig.cap="Absoute exposures of the COSMIC signatures in the lymphoma mutational catalogues, no cutoff for the LCD (Linear Combination Decomposition)."}
exposures_barplot(
in_exposures_df = CosmicValid_cutoffZero_LCDlist$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 exposures_barplot_rel_cutoffZero, fig.width=6, fig.height=5, fig.cap="Relative exposures of the COSMIC signatures in the lymphoma mutational catalogues, no cutoff for the LCD (Linear Combination Decomposition)."}
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 definition_of_cutoff}
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 LCD_complex_cutoff_cutoffGen, 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 exposures_barplot_abs_cutoffGen, fig.width=6, fig.height=4, fig.cap="Absoute exposures of the COSMIC signatures in the lymphoma mutational catalogues, cutoff of 6% for the LCD (Linear Combination Decomposition)."}
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 exposures_barplot_rel_cutoffGen, fig.width=6, fig.height=4, fig.cap="Relative exposures of the COSMIC signatures in the lymphoma mutational catalogues, cutoff of 6% for the LCD (Linear Combination Decomposition)."}
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
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 make_specific_cutoff_vector}
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 LCD_complex_cutoff_cutoffSpec, 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 exposures_barplot_abs_cutoffSpec, fig.width=6, fig.height=4, fig.cap="Absoute exposures of the COSMIC signatures in the lymphoma mutational catalogues, cutoff of 6% for the LCD (Linear Combination Decomposition)."}
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 exposures_barplot_rel_cutoffSpec, fig.width=6, fig.height=4, fig.cap="Relative exposures of the COSMIC signatures in the lymphoma mutational catalogues, cutoff of 6% for the LCD (Linear Combination Decomposition)."}
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.
## 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 apply_heatmap_exposures, fig.width=6, fig.height=4, fig.cap="Clustering of Samples and Signatures based on the relative exposures of the COSMIC signatures in the lymphoma mutational catalogues."}
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 apply_hclust_exposures, fig.width=6, fig.height=3, fig.cap="Clustering of the Samples based on the relative exposures of the COSMIC signatures in the lymphoma mutational catalogues."}
hclust_list <-
hclust_exposures(CosmicValid_cutoffGen_LCDlist$norm_exposures,
COSMIC_subgroups_df,
in_method="manhattan",
in_subgroup_column="subgroup")
```
The dendrogram produced by either the function `complex_heatmap_exposures` or
the function `hclust_exposures` can be cut to yield signature exposure specific
subgroups of the PIDs.
```{r cluster_PIDs_sig_info, fig.width=6, fig.height=4, fig.cap=paste0("PIDs labelled by the clusters extracted from the signature analysis.")}
cluster_vector <- cutree(hclust_list$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")
```
## Performing a stratification based on mutation density
We will now use the intermutational distances computed above. We set cutoffs
for the intermutational distance at 1000 and 100000 bp, leading to three
strata. We annotate to every variant in which stratum it falls.
```{r stratify_mut_density}
lymphoma_Nature2013_df$density_cat <- cut(lymphoma_Nature2013_df$dist,
c(0,1001,100001,Inf),
right=FALSE,
labels=c("high","intermediate",
"background"))
```
The following table shows the distribution of variants over strata:
```{r table_strata_mut_density}
temp_df <- data.frame(table(lymphoma_Nature2013_df$density_cat))
names(temp_df) <- c("Stratum","Cohort-wide counts")
kable(temp_df, caption=paste0("Strata for the SMC of mutation density"))
```
We now have everything at hand to carry out a stratified signature analysis:
```{r SMC_mut_density, results="hide", warning=FALSE, fig.width=8, fig.height=7, fig.cap="SMC (Stratification of the Mutational Catalogue) based on mutation density."}
strata_order_ind <- c(1,3,2)
mut_density_list <- run_SMC(lymphoma_Nature2013_df,
CosmicValid_cutoffGen_LCDlist$signatures,
CosmicValid_cutoffGen_LCDlist$out_sig_ind_df,
COSMIC_subgroups_df,
column_name="density_cat",
refGenome=BSgenome.Hsapiens.UCSC.hg19,
cohort_method_flag="norm_PIDs",
in_strata_order_ind=strata_order_ind)
```
This produces a multi-panel figure with
`r length(unique(lymphoma_Nature2013_df$density_cat))+1` rows of plots. The
first row visualizes the signature distribution over the whole cohort without
stratification, followed by one row of plots per stratum. Hence in our example
we have four rows of graphs with three (exclusive) strata as input. Each row
consists of three plots. The left plots show absolute exposures in the
respective stratum as stacked barplots on a per sample basis. The middle plots
show relative exposures in the respective stratum on a per sample basis as
stacked barplots. The right plots shows cohort-wide averages of the relative
exposures in the respective stratum. The error bars indicate the standard error
of the mean (SEM).
To test for statistical significance of potential differences in the signature
exposures (i.e. signature enrichment and depletion patterns) between the
different strata, we can use the Kruskal-Wallis test, as the data is grouped
into (potentially more than two) categories and might not follow a normal
distribution. As we are testing the different signatures on the same
stratification, we have to correct for multiple testing. In order to control
the false discovery rate (FDR), the Benjamini-Hochberg correction is
appropriate.
```{r stat_SMC_mut_density, warning=FALSE, message=FALSE}
stat_mut_density_list <- stat_test_SMC(mut_density_list,in_flag="norm")
kable(stat_mut_density_list$kruskal_df,
caption=paste0("Results of Kruskal tests for cohort-wide exposures over",
" strata per signature without and with correction for ",
"multiple testing."))
```
In the following paragraph we perform post-hoc tests for those signatures where
the Kruskal-Wallis test, as evaluated above, has given a significant result.
\newpage
```{r post_hoc_mut_density}
significance_level <- 0.05
for(i in seq_len(dim(stat_mut_density_list$kruskal_df)[1])){
if(stat_mut_density_list$kruskal_df$Kruskal_p_val_BH[i]<significance_level){
print(paste0("Signature: ",rownames(stat_mut_density_list$kruskal_df)[i]))
print(stat_mut_density_list$kruskal_posthoc_list[[i]])
}
}
```
From this analysis, we can see that a distinct signature enrichment and
depletion pattern emerges:
1. Stratum of high mutation density: Enrichment of signatures AC5 (significant)
and AC9, depletion of signatures AC1 (significant), AC2, AC8 (significant) and
AC17
2. Background: signature distribution very similar to the one of the complete
mutational catalogue (first row)
3. Stratum of intermediate mutation density: intermediate signature enrichment
and depletion pattern between the strata of high mutation density and
background.
# References