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<div id="table_of_contents">
<h4>Table of contents</h4>
<div id="full">
<ul>
<li><a href="#toc1">1: Introduction</a></li>
<li><a href="#toc2">2: Installation</a></li>
<li><a href="#toc3">3: Preparing the input</a>
    <ul>
    <li><a href="#toc3.1">3.1: Input from VCF</a></li>
    <li><a href="#toc3.2">3.2: Input from tab-delimited file</a></li>
    </ul>
</li>
<li><a href="#toc4">4: Estimating the number of mutational processes and their
                       signatures</a></li>
<li><a href="#toc5">5: Results and further analyses</a>
    <ul>
    <li><a href="#toc5.1">5.1: Plot the MCMC paths for the NMF parameters</a></li>
    <li><a href="#toc5.2">5.2: Plot the signatures</a>
         <ul>
         <li><a href="#toc5.2.1"> Signature barplot</a></li>
         <li><a href="#toc5.2.2"> Signature heatmap</a></li>
         </ul></li>
    <li><a href="#toc5.3">5.3: Plot the exposures</a>
         <ul>
         <li><a href="#toc5.3.1"> Exposure boxplot</a></li>
         <li><a href="#toc5.3.2"> Exposure barplot</a></li>
         <li><a href="#toc5.3.3"> Exposure heatmap</a></li>
         </ul></li>
    <li><a href="#toc5.4">5.4: Differential Exposure Analysis</a></li>
    <li><a href="#toc5.5">5.5: Sample Classification</a></li>
    </ul>
</li>
<li><a href="#toc6">6. Frequently Asked Questions</a>
    <ul>
    <li><a href="#toc6.1">6.1: Citing signeR</a></li>
    <li><a href="#toc6.2">6.2: Compilation errors on OS X</a></li>
    </ul>
</li>
<li><a href="#toc7">7. References</a></li>
</ul>
</div>
</div>

<h1>signeR</h1>
<h4 style='text-align:center'><em>Rodrigo Drummond,
Renan Valieris, Rafael Rosales and Israel Tojal da Silva</em></h4>

<h2 id="toc1">1: Introduction</h2>

<p>Motivation: Cancer is an evolutionary process driven by continuous
acquisition of genetic variations in individual cells. The diversity and
complexity of somatic mutational processes is a conspicuous feature
orchestrated by DNA damage agents and repair processes, including exogenous or
endogenous mutagen exposures, defects in DNA mismatch repair and enzymatic
modification of DNA. The identification of the underlying mutational processes
is central to understanding of cancer origin and evolution.</p>
<p>The <b>signeR</b> package focuses on the estimation and further analysis of
mutational signatures. The functionalities of this package can be divided into
three categories. First, it provides tools to process VCF files and generate
matrices of SNV mutation counts and mutational opportunities, both defined
according to a 3bp context (mutation site and its neighbouring 3' and 5' bases).
 Second, these count matrices are considered as input for the estimation of the
underlying mutational signatures and the number of active mutational processes.
Third, the package provides tools to correlate the activities of those
signatures with other relevant information such as clinical data, in order to
draw conclusions about the analysed genome samples, which can be useful for
clinical applications. These include the Differential Exposure Score and the a
posteriori sample classification.</p>
<p>Although signeR is intended for the estimation of mutational signatures, it
actually provides a full Bayesian treatment to the non-negative matrix
factorisation (NMF) model. Further details about the method can be found in
Rosales & Drummond <i>et al.</i>, 2016 (see <a href="#toc6.1">section 6.1</a>
below).</p>
<p>This vignette briefly explains the use of <b>signeR</b> through examples.</p>


<h2 id="toc2">2: Installation</h2>

<p>Before installing, please make sure you have the latest version of R and
<a href="http://bioconductor.org/">Bioconductor</a> installed.</p>

<p>To install <b>signeR</b>, start R and enter:</p>
<!--begin.rcode eval=FALSE
source("https://bioconductor.org/biocLite.R")
biocLite("signeR")
end.rcode-->

<p>For more information, see
<a href="http://bioconductor.org/packages/signeR">this page</a>.</p>

<p>Once installed the library can be loaded as</p>

<!--begin.rcode results='hide', message=FALSE
library(signeR)
end.rcode-->

<p class='message'>OS X users might experience compilation errors due to
missing gfortran libraries. Please read <a href="#toc6.2">this section</a>.</p>

<h2 id="toc3">3: Preparing the input</h2>

<p><b>signeR</b> takes as input a count matrix of samples x features.
Each feature is usually a SNV mutation within a 3bp context (96 features, 6
types of SNV mutations and 4 possibilities for the bases at each side of the
SNV change). Optionally, an opportunity matrix can also be provided containing
the count frequency of the features in the whole analyzed region for each
sample. Although not required, this argument is highly recommended because it
allows <b>signeR</b> to normalize the features frequency over the analyzed
region.</p>

<p>Input matrices can be read both from a VCF or a tab-delimited files, as
described next.</p>

<h3 id="toc3.1">3.1: Input from VCF</h3>

<p>The <a target="_blank"
href="http://www.1000genomes.org/wiki/Analysis/vcf4.0">VCF</a> file format is
the most common format for storing genetic variations, the <b>signeR</b>
package includes a utility function for generating a count matrix from the VCF:
</p>

<!--begin.rcode eval=FALSE
library(VariantAnnotation)

# BSgenome, equivalent to the one used on the variant call
library(BSgenome.Hsapiens.UCSC.hg19)

vcfobj <- readVcf("/path/to/a/file.vcf", "hg19")
mut <- genCountMatrixFromVcf(BSgenome.Hsapiens.UCSC.hg19, vcfobj)
end.rcode-->

<p>This function will generate a matrix of mutation counts for each sample in
the provided VCF. The opportunity matrix can also be generated from the
reference genome (hg19 in the following case):</p>

<!--begin.rcode eval=FALSE
library(rtracklayer)

target_regions <- import(con="/path/to/a/target.bed", format="bed")
opp <- genOpportunityFromGenome(BSgenome.Hsapiens.UCSC.hg19,
    target_regions, nsamples=nrow(mut))
end.rcode-->

<p>Where <code>target.bed</code> is a <a target="_blank"
href="https://genome.ucsc.edu/FAQ/FAQformat.html#format1">bed file</a>
containing the genomic regions analyzed by the variant caller.</p>

<h3 id="toc3.2">3.2: Input from tab-delimited file</h3>

<p>By convention, the input file should be tab-delimited with sample names as
row names and features as column names. Features should be refered in the
format "base change:triplet", e.g. "C&gt;A:TCG", as can be seen in the example
below. Similarly, the opportunity matrix can be provided in a tab-delimited
file with the same structure as the mutation counts file. An example of the
required matrix format can be seen
<a target="_blank"
href="https://github.com/rvalieris/signeR/blob/devel/inst/extdata/21_breast_cancers.mutations.txt">
here</a>.</p>

<p>This tutorial uses as input the 21 breast cancer dataset described in
<a target="_blank" href="http://dx.doi.org/10.1016/j.cell.2012.04.023">
Nik-Zainal et al 2012</a>. For the sake of convenience this dataset is
included with the package and can be accessed by using the
<code>system.file</code> function:</p>

<!--begin.rcode
mut <- read.table(system.file("extdata","21_breast_cancers.mutations.txt",
    package="signeR"), header=TRUE, check.names=FALSE)
opp <- read.table(system.file("extdata","21_breast_cancers.opportunity.txt",
    package="signeR"))
end.rcode-->

<h2 id="toc4">4: Estimating the number of mutational processes and their
signatures</h2>

<p><b>signeR</b> takes a count matrix as its only required parameter, but the
user can provide an opportunity matrix as well. The algorithm allows the
assessment of the number of signatures by three options, as follows.</p>

<ol type="i">
<li>signeR detects the number of signatures at run time by considering the best
NMF factorisation rank between 1 and min(G, K)-1, with G = number of genomes and
K = number of features (i.e. 96):
<!--begin.rcode eval=FALSE
signatures <- signeR(M=mut, Opport=opp)
end.rcode-->
</li>
<li>The user can give a interval of the possible numbers of signatures as the
parameter nlim. <b>signeR</b> will calculate the optimal number of signatures
within this range, for example:
<!--begin.rcode eval=FALSE
signatures <- signeR(M=mut, Opport=opp, nlim=c(2,11))
end.rcode-->
</li>
<li>Finally, <b>signeR</b> can also be run by passing the number of signatures
as the parameter nsig. In this setting, the algorithm is faster. For example,
the following command will make <b>signeR</b> consider only the rank N=5 to
estimate the signatures and their exposures:
<!--begin.rcode results='hide', message=FALSE
signatures <- signeR(M=mut, Opport=opp, nsig=5, main_eval=100, EM_eval=50, EMit_lim=20)
end.rcode-->
</li>
</ol>

<p>The parameters <code>testing_burn</code> and <code>testing_eval</code>
control the number of iterations used to estimate the number of signatures
(default value is 1000 for both parameters). There are other
arguments that may be passed on to signeR. Please have a look at signeR's
manual, issued by typing <code>help(signeR)</code>.</p>
<p>Whenever <b>signeR</b> is left to decide which number of signatures is
optimal, it will search for the rank Nsig that maximizes the median Bayesian
Information Criterion (BIC). After the processing is done, this information can
be plotted by the following command:</p>

<!--begin.rcode eval=FALSE
BICboxplot(signatures)
end.rcode-->

<!--begin.rcode echo=FALSE, results='asis'
cat(sprintf("<img src=\"%s\" />\n",image_uri("Model_selection_BICs.png")))
end.rcode-->

<p class='legend'>Boxplot of BIC values, showing that the optimal number of signatures for this
dataset is 5.</p>

<h2 id="toc5">5: Results and further analyses</h2>

<h3 id="toc5.1">5.1: Plot the MCMC paths for the NMF parameters (P and E
matrices)</h3>

<p>The following instruction plots the MCMC sampled paths for each entry of the
signature matrix P and their exposures, i.e. the E matrix. Only post-burnin
paths are available for plotting. Those plots are useful for checking if
entries have leveled off, reflecting the sampler convergence.</p>

<!--begin.rcode
Paths(signatures$SignExposures)
end.rcode-->
<p class='legend'>Each plot shows the entries and exposures of
one signature along sampler iterations.</p>

<h3 id="toc5.2">5.2: Plot the signatures</h3>

<p>Once the processing is done, the estimated signatures can be displayed in two
charts, described below. </p>

<h4 id="toc5.2.1"> Signature barplot</h4>

<p>Signatures can be visualized in a barplot by issuing the following command:</p>

<!--begin.rcode
SignPlot(signatures$SignExposures)
end.rcode-->

<p class='legend'>Signatures barplot with error bars reflecting
the sample percentiles 0.05, 0.25, 0.75, and 0.95 for each entry.</p>

<h4 id="toc5.2.2"> Signature heatmap</h4>

<p>Estimated signatures can also be visualized in a heatmap, generated by the
following command:</p>

<!--begin.rcode
SignHeat(signatures$SignExposures)
end.rcode-->
<p class='legend'>Heatmap showing the entries of each signature.</p>

<h3 id="toc5.3">5.3: Plot the exposures</h3>

<p>The relative contribution of each signature to the inspected genomes can be
displayed in several ways. <b>signeR</b> currently provides three alternatives.
</p>

<h4 id="toc5.3.1"> Exposure boxplot</h4>

<p>The levels of exposure to each signature in all genome samples can also be
plotted:</p>

<!--begin.rcode
ExposureBoxplot(signatures$SignExposures)
end.rcode-->

<h4 id="toc5.3.2"> Exposure barplot</h4>

<p>The relative contribution of the signatures to the mutations found on each
genome sample can also be visualized by the following command:</p>

<!--begin.rcode
ExposureBarplot(signatures$SignExposures)
end.rcode-->
<p class='legend'>Barplot showing the contributions of the signatures to genome samples
mutation counts.</p>


<h4 id="toc5.3.3"> Exposure heatmap</h4>

<p>The levels of exposure to each signature can also be plotted in a heatmap:
</p>

<!--begin.rcode
ExposureHeat(signatures$SignExposures)
end.rcode-->
<p class='legend'>Heatmap showing the exposures for each genome
sample. Samples are grouped according to their levels of exposure to the
signatures, as can be seen in the dendrogram on the left.</p>

<h3 id="toc5.4">5.4: Differential Exposure Analysis</h3>

<p><b>signeR</b> can highlight signatures that are differentially active among
previously defined groups of samples. To perform this task <b>signeR</b> needs
a vector of group labels. In this example the samples were divided according to
germline mutations at BRCA genes: groups <code>wt</code>,
<code>BRCA1+</code> and <code>BRCA2+</code>, taken from the description of the
21 breast cancer data set.</p>

<!--begin.rcode
# group labels, respective to each row of the mutation count matrix
BRCA_labels <- c("wt","BRCA1+","BRCA2+","BRCA1+","BRCA2+","BRCA1+","BRCA1+",
    "wt","wt","wt","wt","BRCA1+","wt","BRCA2+","BRCA2+","wt","wt","wt",
    "wt","wt","wt")

diff_exposure <- DiffExp(signatures$SignExposures, labels=BRCA_labels)
end.rcode-->
<p class='legend'><b>Top chart:</b> DES plot showing that the
BRCA+ samples were significantly more exposed to signatures S3, S4 and S5.
<b>Bottom chart:</b> plots showing the significant differences found when groups
are compared against each other. These last plots are generated only when there
are more than two groups in the analysis and any signature is found to be
differentially active. Groups marked by the same letter are not significantly
different for the corresponding signature.</p>

<p>The <code>Pvquant</code> vector holds the pvalues quantile of the test for
each signature (by default, the 0.5 quantile, i.e. the median). The logarithms
of those are considered the Differential Exposure Scores (DES). Signatures with
<code>Pvquant</code> values below the cutoff, 0.05 by default, are considered as
differentially exposed.</p>

<!--begin.rcode
# pvalues
diff_exposure$Pvquant
end.rcode-->

<p>The <code>MostExposed</code> vector contains the name of the group where each
differentially exposed signature showed the highest levels of activity.</p>

<!--begin.rcode
# most exposed group
diff_exposure$MostExposed
end.rcode-->

<h3 id="toc5.5">5.5: Sample Classification</h3>

<p><b>signeR</b> can also classify unlabeled samples based on the given labels.
In order to do this, those samples must correspond to <code>NA</code> values in
the labels vector and the Classify function can be used to assign them to one of
the defined groups. This example uses the sample labels defined in the DES
analysis performed previously.</p>

<!--begin.rcode
# note that BRCA_labels [15],[20] and [21] are set to NA
BRCA_labels <- c("wt","BRCA+","BRCA+","BRCA+","BRCA+","BRCA+","BRCA+","wt","wt",
    "wt","wt","BRCA+","wt","BRCA+",NA,"wt","wt","wt","wt",NA,NA)

Class <- Classify(signatures$SignExposures, labels=BRCA_labels)
end.rcode-->
<p class='legend'>Barplot showing the relative frequencies of
assignment of each unlabeled sample to the selected group.</p>

<!--begin.rcode
# Final assignments
Class$class

# Relative frequencies of assignment to selected groups
Class$freq

# All assigment frequencies
Class$allfreqs
end.rcode-->


<h2 id="toc6">6: Frequently Asked Questions</h2>

<h3 id="toc6.1">6.1: Citing signeR</h3>
If you use the <b>signeR</b> package in your work, please cite it:
<!--begin.rcode
citation("signeR")
end.rcode-->

<h3 id="toc6.2">6.2: Compilation errors on OS X</h3>

<p>OS X users might get compilation errors similar to these:</p>
<pre>
<code>ld: warning: directory not found for option '-L/usr/local/lib/x86_64'
ld: library not found for -lgfortran
ld: library not found for -lquadmath</code></pre>

<p>This problem arises when the machine is missing gfortran libraries
necessary to compile RcppArmadillo and signeR.
To install the missing libraries, execute these lines on a terminal:</p>

<pre>
<code>curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /</code></pre>
<p>For more information <a
href="http://thecoatlessprofessor.com/programming/rcpp-rcpparmadillo-and-os-x-mavericks-lgfortran-and-lquadmath-error/">
see this post</a> and the
<a href="http://dirk.eddelbuettel.com/code/rcpp/Rcpp-FAQ.pdf">
Rcpp FAQ, section 2.16</a>.</p>


<h2 id="toc7">7: References</h2>

<p>L. B. Alexandrov, S. Nik-Zainal, D. C. Wedge, P. J. Campbell, and M. R.
Stratton. Deciphering Signatures of Mutational Processes Operative in Human
Cancer. Cell Reports, 3(1):246-259, Jan. 2013. [
<a href="http://dx.doi.org/10.1016/j.celrep.2012.12.008">DOI</a> ]</p>

<p>A. Fischer, C. J. Illingworth, P. J. Campbell, and V. Mustonen. EMu:
probabilistic inference of mutational processes and their localization in the
cancer genome. Genome biology, 14(4):R39, Apr. 2013. [
<a href="http://dx.doi.org/10.1186/gb-2013-14-4-r39">DOI</a> ]</p>


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