%\VignetteKeywords{runAbsoluteCN}
%\VignetteEngine{knitr::knitr}
%\VignetteDepends{PureCN}
%\VignettePackage{PureCN}
%\VignetteIndexEntry{PureCN}

\documentclass{article}

<<style-knitr, eval=TRUE, echo=FALSE, results="asis">>=
BiocStyle::latex2()
@ 

\usepackage{booktabs} % book-quality tables

\title{Copy number calling and SNV classification using 
targeted short read sequencing}
\author[1]{Markus Riester}
\affil[1]{Novartis Institutes for BioMedical Research, Cambridge, MA}

\begin{document}

\maketitle

\begin{abstract}
\Biocpkg{PureCN} is a purity and ploidy aware copy number caller for cancer
samples inspired by the \software{ABSOLUTE} algorithm \cite{Carter2012}.  It was
designed for hybrid capture sequencing data, especially with medium-sized
targeted gene panels without matching normal samples in mind (matched whole
exome data is of course supported). 

It can be used to supplement existing normalization and segmentation
algorithms, i.e. the software can start from BAM files, from target-level
coverage data, from copy number log-ratios or from already segmented
data.  If the correct purity and ploidy solution was identified,
\Biocpkg{PureCN} can also help in classifying variants as germline vs. somatic
or clonal vs.  sub-clonal.

\Biocpkg{PureCN} was further designed to integrate well with industry standard
pipelines \cite{McKenna2010}, but it is straightforward to generate input data
from other pipelines.
\end{abstract}

\packageVersion{\Sexpr{BiocStyle::pkg_ver("PureCN")}}

\tableofcontents
\newpage

<<load-purecn, echo=FALSE, message=FALSE>>=
library(PureCN)
set.seed(1234)
@

\section{Quick start}

This tutorial will demonstrate on a toy example how we recommend running
\Biocpkg{PureCN} on targeted sequencing data. To estimate tumor purity, we
jointly utilize both target-level\footnote{The captured genomic regions, e.g.
exons.} coverage data and allelic fractions of single nucleotide
variants (SNVs), inside - and optionally outside - the targeted regions.
Knowledge of purity will in turn allow us to accurately (i) infer integer copy
number and (ii) classify variants (somatic vs.  germline, mono-clonal vs.
sub-clonal, heterozygous vs.  homozygous etc.).

This requires 3 basic input files:

\begin{enumerate}
    \item A VCF file containing germline SNPs and somatic mutations. Somatic
status is not required in case the variant caller was run without matching
normal sample.
    \item The tumor BAM file.
    \item A BAM file from a normal control sample, either matched or
process-matched.
\end{enumerate}

In addition, we need to know a little bit more about the assay.  This is the
annoying step, since here you need to provide some information.  Most
importantly, we need to know the positions of all targets.  Then we need to
correct for GC-bias, for which we need GC-content for each target.  Optionally,
if gene-level calls are wanted, we also need for each target a gene symbol.  To
obtain best results, we can finally use a pool of normal samples to
automatically learn more about our assay and its biases and common artifacts. 

The next sections will show how to do all this with \Biocpkg{PureCN} alone or
with the help of \software{GATK} and/or existing copy number pipelines. We tried
to make this as pain-free as possible.

\section{Basic input files}

\subsection{Single nucleotide variants}

Germline SNPs and somatic mutations are expected in a single VCF file. At the
bare minimum, this VCF should contain read depths of reference and alt alleles
in an AD genotype field and a DB info flag for dbSNP membership. If a matched
normal is available, then somatic status information is currently expected in a
SOMATIC info flag in the VCF. The \Biocpkg{VariantAnnotation} package provides
examples how to add info fields to a VCF in case the used variant caller does
not add this flag.

VCF files generated by \software{MuTect} \cite{Cibulskis2013} should work well
and in general require no post-processing. \Biocpkg{PureCN} can handle
\software{MuTect} VCF files generated in both single and matched normal mode. 

\subsection{Coverage data}
\label{gatkcoverage}
For the default segmentation function provided by \Biocpkg{PureCN}, the
algorithm first needs to calculate log-ratios of tumor vs.  normal control
coverage.  Coverage data needs to be provided in \software{GATK DepthOfCoverage}
format\footnote{\software{GATK} will generate additional columns and
\Biocpkg{PureCN} will ignore these when provided}:

\begin{verbatim}
    Target  total_coverage  average_coverage    
    chr1:69091-70009    0   0   0   
    chr1:367659-368598  6358    9.25    
    chr1:621096-622035  6294    9.16    
\end{verbatim}

The intervals define the captured genomic regions (targets) and are provided by
the manufacturer of your capture kit\footnote{While \software{PureCN} can use a
pool of normal samples to learn which intervals are reliable and which not, it
is highly recommended to provide the correct intervals. Garbage in, garbage
out.}.  Default parameters assume that these intervals do NOT include a
"padding" to include flanking regions of targets. \software{PureCN} can include
variants in flanking regions if the variant caller was run with interval
padding (See Sections~\ref{seqofftarget} and \ref{faqinput}). 

It is recommended to GC-normalize coverage (how this done is described later in
Section~\ref{secgcbias}). The GC-content for each target is expected in
\software{GATK GCContentByInterval} format:

\begin{verbatim}
    Target    gc_bias    Gene
    chr1:69091-70009    0.427638737758433    OR4F5
    chr1:367659-368598    0.459574468085106    OR4F29
    chr1:621096-622035    0.459574468085106    OR4F3
\end{verbatim}

The \Rcode{Gene} column is optional and only required for providing gene-level
copy number and LOH calls. This information should be available in the
technical documentation of your capture kit. Simplistic example code for
annotating target intervals with gene symbols is given in the Appendix of this
vignette for testing purposes.

\subsection{Generating coverage data without \software{GATK}}
\label{purecncoverage}

The \Rfunction{calculateBamCoverageByInterval} function can be used to generate
the required coverage data from BAM files:

<<examplecoverage>>=
bam.file <- system.file("extdata", "ex1.bam", package="PureCN", 
    mustWork = TRUE)
interval.file <- system.file("extdata", "ex1_intervals.txt", 
    package="PureCN", mustWork = TRUE)

calculateBamCoverageByInterval(bam.file=bam.file, 
 interval.file=interval.file, output.file="ex1_coverage.txt")
@

To calculate GC-content, \Biocpkg{PureCN} provides the 
\Rfunction{calculateGCContentByInterval} function:

<<examplegc>>=
interval.file <- system.file("extdata", "ex2_intervals.txt", 
        package = "PureCN", mustWork = TRUE)
reference.file <- system.file("extdata", "ex2_reference.fa", 
    package = "PureCN", mustWork = TRUE)
calculateGCContentByInterval(interval.file, reference.file, 
    output.file = "ex2_gc_file.txt")
@

\subsection{Third-party segmentation tools}

\Biocpkg{PureCN} integrates well with existing copy number pipelines. Instead of
coverage data, the user then needs to provide either already segmented data or a
wrapper function. This is described in Section~\ref{customseg}.

\subsection{Example data}

We now load a few example files that we will use throughout this tutorial:

<<example_files, message=FALSE, warning=FALSE, results='hide'>>=
normal.coverage.file <- system.file("extdata", "example_normal.txt",
    package="PureCN") 
normal2.coverage.file <- system.file("extdata", "example_normal2.txt", 
    package="PureCN") 
normal.coverage.files <- c(normal.coverage.file, normal2.coverage.file) 
tumor.coverage.file <- system.file("extdata", "example_tumor.txt", 
    package="PureCN") 
seg.file <- system.file("extdata", "example_seg.txt",
    package = "PureCN")
vcf.file <- system.file("extdata", "example_vcf.vcf", package="PureCN")
gc.gene.file <- system.file("extdata", "example_gc.gene.file.txt", 
    package="PureCN")
@

\section{GC-bias}
\label{secgcbias}
The algorithm works best when the coverage files are GC-normalized. We can
easily create GC-normalized coverage files:

<<gccorrect>>=
correctCoverageBias(normal.coverage.file, gc.gene.file, 
    "example_normal_loess.txt")
@

\textbf{All the following steps in this vignette assume that the coverage data
are GC-normalized.} The example coverage files are already GC-normalized.
Section~\ref{cmdcoverage} presents a convenient command line script for
generating GC-normalized coverage data from BAM files or from \software{GATK}
coverage files.


\section{Pool of normals}

\subsection{Selection of normals for log-ratio calculation}
\label{bestnormal}

For calculating copy number log-ratios of tumor vs. normal, \Biocpkg{PureCN}
requires coverage from a process-matched normal sample.  Using a normal that
was sequenced using a similar, but not identical assay, rarely works, since
differently covered genomic regions result in too many log-ratio outliers. This
section describes how to identify a good process-matched normal in case no
matched normal is available or in case the matched normal has low or uneven
coverage.
 
The \Rfunction{createNormalDatabase} function builds a database of coverage
files:

<<normaldb>>=
normalDB <- createNormalDatabase(normal.coverage.files)
# serialize, so that we need to do this only once for each assay
saveRDS(normalDB, file="normalDB.rds")
@

Again, please make sure that all coverage files were GC-normalized prior to
building the database (Section~\ref{secgcbias}).  Internally,
\Rfunction{createNormalDatabase} determines the sex of the samples and trains a
PCA that is later used for clustering a tumor file with all normal samples in
the database. This clustering is performed by the \Rfunction{findBestNormal}
function:

<<normaldbpca>>=
normalDB <- readRDS("normalDB.rds")
# get the best normal
best.normal.coverage.file <- findBestNormal(tumor.coverage.file, 
    normalDB)
@

This function can also return multiple normal files that can be averaged into a
single pool:  

<<normaldbpcapool>>=
# get the best 2 normals and average them
best.normal.coverage.files <- findBestNormal(tumor.coverage.file, 
    normalDB, num.normals=2)
pool <- poolCoverage(lapply(best.normal.coverage.files, 
    readCoverageGatk), remove.chrs=c('chrX', 'chrY'))
@

Pooling is only recommended when the coverage in normals is significantly lower
than in tumor.  Otherwise the PCA will typically do a good job in selecting a
normal with decent coverage and similar biases compared to tumor. But it is
worth experimenting with different strategies using the
\Rfunction{plotBestNormal} function.  Note that this example removes coverage
from sex chromosomes; if the normal database contains a sufficient number of
samples with matching sex, \Rfunction{findBestNormal} will return only normal
samples with matching sex.

\subsection{Artifact filtering}
\label{artifactfiltering}

It is important to remove as many artifacts as possible, since low ploidy
solutions are typically punished more by artifacts than high ploidy solutions.
High ploidy solutions are complex and usually find ways of explaining artifacts
reasonably well. This following steps in this section are optional, but
recommended especially when matched normals are not available.

We first use coverage data of normal samples to estimate the expected variance
in coverage per target:

<<targetweightfile1>>=
target.weight.file <- "target_weights.txt"
createTargetWeights(tumor.coverage.file, normal.coverage.files, 
    target.weight.file)
@

This function calculates target-level copy number log-ratios for all normal
samples provided in the \Rcode{normal.coverage.files} argument.  Assuming that all
normal samples are in general diploid, an unusual high variance in log-ratio is
indicative of an target with either common germline alterations or frequent
artifacts; high or low copy number log-ratios in these targets are unlikely
measuring somatic copy number events.  For the log-ratio calculation, we provide
a coverage file that is used as tumor in the log-ratio calculation. The
corresponding \Rcode{tumor.coverage.file} argument can also be an array of coverage
files, in which case the target coverage variance is averaged over all provided
tumor files. As long as the coverage of the tumor files is even, only 1 or 2
tumor files are necessary, however. 

We can also use the pool of normals to find SNPs with biased allelic fractions
in low quality regions (very significantly different from 0.5 for heterozygous
SNPs). If a matching normal is provided, most variants in low quality regions
with biased allelic fractions should be automatically ignored. Note that this is
not meant to model non-reference bias leading in expected allelic ratio only
slightly below 0.5 (typically around 0.48 on average). This bias is typically
not strong enough to influence selection of SNV states.

<<snpblacklist>>=
snp.blacklist <- c("SNP_blacklist.csv", "SNP_blacklist_segmented.csv")
recreateBlacklists <- FALSE

if (recreateBlacklists) {
    mutect.normal.files <- dir("poolofnormals", pattern="vcf$", 
        full.names=TRUE)
    snp.bl <- createSNPBlacklist(mutect.normal.files)
    write.csv(snp.bl[[1]], file=snp.blacklist[1])
    write.csv(snp.bl[[2]], file=snp.blacklist[2], row.names=FALSE, 
       quote=FALSE)
}
@

Finally, we recommend running \software{MuTect} with a pool of normal samples
to filter common sequencing errors and alignment artifacts from the VCF.
\software{MuTect} requires a single VCF containing all normal samples, for
example generated by the \software{GATK CombineVariants} tool. It is possible
to provide \Biocpkg{PureCN} this combined VCF as well; it might help the
software correcting non-reference read mapping biases. This is described in the
\Rfunction{setMappingBiasVcf} documentation.

\subsection{Artifact filtering without a pool of normals}
\label{artifactfiltering2}

By default, \Biocpkg{PureCN} will exclude targets with coverage below 15X from
segmentation. For SNVs, the same 15X cutoff is applied. \software{MuTect}
applies more sophisticated artifact tests and flags suspicious variants. If
\software{MuTect} was run in matched normal mode, then both potential artifacts
and germline variants are rejected, that means we cannot just filter by the
PASS/REJECT \software{MuTect} flags. The \Rfunction{filterVcfMuTect} function
optionally reads the \software{MuTect 1.1.7} stats file and will keep germline
variants, while removing potential artifacts.  Without the stats file,
\Biocpkg{PureCN} will use only the filters based on read depths as defined in
\Rfunction{filterVcfBasic}.  Both functions are automatically called by
\Biocpkg{PureCN}, but can be easily modified and replaced if necessary.

Instead of using a pool of normals to find SNPs with biased allelic fractions,
we can also use a BED file to blacklist regions. For example the segmental
duplications and simple repeats tracks from the UCSC. This is again recommended
when matching normals are not available.

<<ucsc_segmental>>=
# Instead of using a pool of normals to find low quality regions,
# we use suitable BED files, for example from the UCSC genome browser.

# We do not download these in this vignette to avoid build failures 
# due to internet connectivity problems.
downloadFromUCSC <- FALSE
if (downloadFromUCSC) {
    library(rtracklayer)
    mySession <- browserSession("UCSC")
    genome(mySession) <- "hg19"
    tbl.segmentalDups <- getTable( ucscTableQuery(mySession, 
        track="Segmental Dups", table="genomicSuperDups"))
    tbl.simpleRepeats <- getTable( ucscTableQuery(mySession, 
        track="Simple Repeats", table="simpleRepeat"))
    write.table(tbl.segmentalDups[,-1],
        file="hg19_segmentalDuplications.txt", sep="\t", 
        row.names=FALSE)
    write.table(tbl.simpleRepeats[,-1],
        file="hg19_simpleRepeats.txt", sep="\t", 
        row.names=FALSE)
}

snp.blacklist <- c('hg19_segmentalDuplications.txt', 
    'hg19_simpleRepeats.txt')
@

\section{Recommended run}
\label{secrecommended}
Finally, we can run \Biocpkg{PureCN} with all that information:

<<runpurecn>>=
ret <-runAbsoluteCN(normal.coverage.file=normal.coverage.file, 
    tumor.coverage.file=tumor.coverage.file, vcf.file=vcf.file, 
    genome="hg19", sampleid='Sample1', 
    gc.gene.file=gc.gene.file, 
# args.filterVcf=list(snp.blacklist=snp.blacklist, 
# stats.file=mutect.stats.file), 
    args.filterTargets=list(normalDB=normalDB),
    args.segmentation=list(target.weight.file=target.weight.file), 
    post.optimize=FALSE, plot.cnv=FALSE, verbose=FALSE)
@

The \Rcode{normal.coverage.file} argument points to a coverage file obtained from
either a matched or a process-matched normal sample, but can be also a small
pool of best normals (Section~\ref{bestnormal}).  The files specified in
\Rcode{args.filterVcf} help \Biocpkg{PureCN} filtering SNVs more efficiently for
artifacts as described in Sections~\ref{artifactfiltering} and
\ref{artifactfiltering2}. The \Rcode{normalDB} argument
(Section~\ref{bestnormal}) in \Rcode{args.filterTargets} allows the segmentation
function to skip targets with low coverage in the pool of normals. If possible,
these files should be generated and provided.  The \Rcode{post.optimize} flag
will increase the runtime by about a factor of 4-5, but might return slightly
more accurate purity estimates. For high quality whole exome data, this is
typically not necessary for copy number calling (but might be for variant
classification, see Section~\ref{predictsomatic}).  The \Rcode{plot.cnv}
argument allows the segmentation function to generate additional plots if set to
\Rcode{TRUE}.  Finally, \Rcode{verbose} outputs important and helpful
information about all the steps performed and is therefore set to \Rcode{TRUE}
by default.

We now create a few output files:

<<createoutput>>=
file.rds <- 'Sample1_PureCN.rds'
saveRDS(ret, file=file.rds)
pdf('Sample1_PureCN.pdf', width=10, height=12)
plotAbs(ret, type='all')
dev.off()
@

The RDS file now contains the serialized return object of the
\Rfunction{runAbsoluteCN} call. The PDF contains helpful plots for all local
minima, sorted by likelihood.  The first plot in the generated PDF is displayed
in Figure~\ref{figure/figureexample1-1} and shows the purity and ploidy local
optima, sorted by final likelihood score after fitting both copy number and
allelic fractions.

<<figureexample1, fig.show='hide', fig.width=6, fig.height=6>>=
plotAbs(ret, type="overview")
@
\incfig{figure/figureexample1-1}{0.75\textwidth}{Overview.}
{The colors visualize the copy number fitting score from low (blue) to high
(red). The numbers indicate the ranks of the local optima.}

We now look at the main plots of the maximum likelihood solution in more detail.

<<figureexample2, fig.show='hide', fig.width=6, fig.height=6>>=
plotAbs(ret, 1, type="hist")
@
\incfig{figure/figureexample2-1}{0.75\textwidth}{Log-ratio histogram.}

Figure~\ref{figure/figureexample2-1} displays a histogram of tumor vs. normal
copy number log-ratios for the maximum likelihood solution (number 1 in
Figure~\ref{figure/figureexample1-1}). The height of a bar in this plot is
proportional to the fraction of the genome falling into the particular
log-ratio copy number range. The vertical dotted lines and numbers visualize
the, for the given purity/ploidy combination, expected log-ratios for all
integer copy numbers from 0 to 7. It can be seen that most of the log-ratios of
the maximum likelihood solution align well to expected values for copy numbers
of 0, 1 and 2. 

<<figureexample3, fig.show='hide', fig.width=8, fig.height=8>>=
plotAbs(ret, 1, type="BAF")
@
\incfig{figure/figureexample3-1}{0.95\textwidth}{B-allele frequency plot.}{Each
dot is a (predicted) germline SNP. The first panel shows the allelic
fractions.  The black lines visualize the expected (not the average!) allelic
fractions in the segment. These are calculated using the estimated purity and
the total and minor segment copy numbers. These are visualized in black and grey,
respectively, in the second and third panel.  The second panel shows the
copy number log-ratios, the third panel the integer copy numbers.}

Germline variant data are informative for calculating integer copy number,
because unbalanced maternal and paternal chromosome numbers in the tumor portion
of the sample lead to unbalanced germline allelic fractions.
Figure~\ref{figure/figureexample3-1} shows the allelic fractions of predicted
germline SNPs.  In the middle panel, the corresponding copy number log-ratios
are shown. The lower panel displays the calculated integer copy numbers,
corrected for purity and ploidy. 

<<figureexample4, fig.show='hide', fig.width=8, fig.height=8>>=
plotAbs(ret, 1, type="AF")
@
\incfig{figure/figureexample4-1}{0.95\textwidth}{Allele fraction plots.}{Each
dot is again a (predicted) germline SNP. This plot normally also shows somatic
mutations in two additional panels. This toy example contains only germline
SNPs however.}

Finally, Figure~\ref{figure/figureexample4-1} provides a little bit more
insight into how well the variants fit the expected values. The left panel
shows the correlation of expected and observed allelic fractions. The expected
value is determined by the most likely state. High ploidy solutions have a lot
of states, so this correlation is expected to be good for high ploidy
solutions.  The right panel plots the observed allelic fractions against copy
number. The labels show the expected values for all called states; 2m1 would be
diploid, heterozygous, 2m2 diploid, homozygous.  \clearpage

\section{Curation}

\subsection{Manual}

For prediction of SNV status (germline vs. somatic, sub-clonal vs. clonal,
homozygous vs. heterozygous), it is important that both purity and ploidy are
correct. We provide functionality for curating results:

<<curationfile>>=
createCurationFile(file.rds) 
@

This will generate a CSV file in which the correct purity and ploidy values
can be manually entered. It also contains a column "Curated", which should be
set to \Rcode{TRUE}, otherwise the file will be overwritten when re-run.

Then in R, the correct solution (closest to the combination in the CSV file)
can be loaded with the \Rfunction{readCurationFile} function:

<<readcurationfile>>=
ret <- readCurationFile(file.rds)
@

This function has various handy features, but most importantly it will re-order
the local optima so that the curated purity and ploidy combination is ranked
first. This means \Rcode{plotAbs(ret,1,type="hist")} would show the plot for
the curated purity/ploidy combination, for example.

The default curation file will list the maximum likelihood solution:

<<curationfileshow>>=
read.csv('Sample1_PureCN.csv')
@

\Biocpkg{PureCN} currently only flags samples with warnings, it does not mark
any samples as failed. The \Rcode{Failed} column in the curation file can be
used to manually flag samples for exclusion in downstream analyses.

\subsection{Automatic}

If \Biocpkg{PureCN} is mainly used for copy number calling, not for classifying
variants, then a deterministic algorithm that does not require manual curation is
important and can produce good results for most samples. A default workflow for
automatic curation is available in the \Rfunction{autoCurateResults} function.
This is a work in progress and implements various heuristics that try to call
the correct solution in difficult samples (i.e., noisy or quiet samples).  We
recommend copying and renaming the function when reproducibility is crucial.

The number of considered local optima is typically high and potentially
confusing. The first step in the auto curation is thus to remove low likelihood
optima.  Internally, this is done with the \Rfunction{bootstrapResults}
function. This function bootstraps variants in the provided VCF and re-ranks
local optima. Solutions that never rank high are then excluded
(Figure~\ref{figure/figureexample1b-1}).  Large-scale copy number artifacts can
decrease the likelihood scores of correct solutions, however, potentially
resulting in a removal of purity/ploidy estimates closest to the true values. 

The next step in the auto curation workflow is rescuing low ranking diploid
solutions. True high ploidy solutions typically have a rather uniform copy
number distribution, while diploid solutions in general have only a small
number of heterozygous losses and single copy gains; a large fraction of the
genome would be by definition normal diploid.  The \Rfunction{getDiploid}
function is internally used to find diploid solutions.  Since there should not
be any diploid solutions in true high ploidy samples, these diploid solutions
are then moved to the top of the ranking. If multiple diploid solutions exist,
additional heuristics try to guess the correct one (for example the diploid
purity should not be too different from the maximum likelihood purity).

<<figureexample1b, fig.show='hide', fig.width=6, fig.height=6>>=
ret <- autoCurateResults(ret)
plotAbs(ret, type="overview")
@
\incfig{figure/figureexample1b-1}{0.75\textwidth}{Overview.}
{As in Figure~\ref{figure/figureexample1-1}, but with low likelihood solutions
automatically removed.}

These automatically curated samples can of course be manually curated as well:

<<curationauto>>=
file.curated.rds <- 'Sample1_PureCN_autocurated.rds'
saveRDS(ret, file=file.curated.rds)
pdf('Sample1_PureCN_autocurated.pdf', width=10, height=12)
plotAbs(ret, type='all')
dev.off()
createCurationFile(file.curated.rds) 
@

\section{Custom normalization and segmentation}
\label{customseg}
Copy number normalization and segmentation are crucial for obtaining good
purity and ploidy estimates. If you have a well-tested pipeline that produces
clean results for your data, you might want (and maybe should) use
\Biocpkg{PureCN} as add-on to your pipeline. By default, we will use
\CRANpkg{DNAcopy} \cite{Venkatraman2007} to segment normalized target-level
coverage log-ratios. It is straightforward to replace the default with other
methods and the \Rfunction{segmentationCBS} function can serve as an example.

The next section describes how to replace the default segmentation. For the
probably more uncommon case that only the coverage normalization is performed by
third-party tools, see Section~\ref{customnorm}.

\subsection{Custom segmentation}
It is possible to provide already segmented data, which is especially
recommended when matched SNP6 data are available or when third-party
segmentation tools are not written in R. Otherwise it is usually however better
to customize the default segmentation function, since the algorithm then has
access to the raw log-ratio distribution\footnote{If the third-party tool
provides target-level log-ratios, then these can be provided via the
\Rcode{log.ratio} argument in addition to \Rcode{seg.file} though. See also
Section~\ref{customnorm}.}. The expected file format for already segmented copy
number data is:

\begin{verbatim}
    ID  chrom   loc.start   loc.end num.mark    seg.mean
    Sample1   1   61723   5773942 2681    0.125406444072723
    Sample1   1   5774674 5785170 10  -0.756511807441712
\end{verbatim}

Since its likelihood model is exon-based, \Biocpkg{PureCN} currently still
requires an interval file to generate simulated target-level log-ratios from a
segmentation file.  For simplicity, this interval file is expected either in
\software{GATK DepthOfCoverage} format and provided via the
\Rcode{tumor.coverage.file} argument or via the \Rcode{gc.gene.file} argument (see
Figure~\ref{figure/figurecustombaf-1}). Note that \Biocpkg{PureCN} will 
re-segment the simulated log-ratios using the default
\Rfunction{segmentationCBS} function, in particular to identify regions of
copy-number neutral LOH and to cluster segments with similar allelic imbalance
and log-ratio. The provided interval file should therefore cover
all significant copy number alterations\footnote{If this behaviour is not
wanted, because maybe the custom function already identifies CNNLOH reliably,
\Rfunction{segmentationCBS} can be replaced with a minimal version.}.

<<customseg>>=
retSegmented <- runAbsoluteCN(seg.file=seg.file, 
    gc.gene.file=gc.gene.file, vcf.file=vcf.file, 
    max.candidate.solutions=1, genome="hg19",
    test.purity=seq(0.3,0.7,by=0.05), verbose=FALSE, 
    plot.cnv=FALSE)
@

The \Rcode{max.candidate.solutions} and \Rcode{test.purity} arguments are set
to non-default values to reduce the runtime of this vignette. 

<<figurecustombaf, fig.show='hide', fig.width=8, fig.height=8>>=
plotAbs(retSegmented, 1, type="BAF")
@
\incfig{figure/figurecustombaf-1}{0.95\textwidth}{B-allele frequency plot for
segmented data.}{This plot shows the maximum likelihood solution for an example
where segmented data are provided instead of coverage data. Note that the middle
panel shows no variance in log-ratios, since only segment-level log-ratios are
available.}

\subsection{Custom normalization}
\label{customnorm}
If third-party tools such as \software{GATK4} are used to calculate
target-level copy number log-ratios, and \Biocpkg{PureCN} should be used for
segmentation and purity/ploidy inference only, it is possible to provide these
log-ratios:

<<customlogratio>>=
# We still use the log-ratio exactly as normalized by PureCN for this
# example
log.ratio <- calculateLogRatio(readCoverageGatk(normal.coverage.file),
    readCoverageGatk(tumor.coverage.file), verbose=FALSE)

retLogRatio <- runAbsoluteCN(log.ratio=log.ratio,
    gc.gene.file=gc.gene.file, vcf.file=vcf.file, 
    max.candidate.solutions=1, genome="hg19",
    test.purity=seq(0.3,0.7,by=0.05), verbose=FALSE, 
    args.filterTargets=list(normalDB=normalDB),
    plot.cnv=FALSE)
@

Again, the \Rcode{max.candidate.solutions} and \Rcode{test.purity} arguments
are set to non-default values to reduce the runtime of this vignette. It is
highly recommended to compare the log-ratios obtained by \Biocpkg{PureCN} and
the third-party tool, since some pipelines automatically adjust log-ratios for
a default purity value. Note that this example uses a pool of normals to filter
low quality targets. Interval coordinates are again expected in either a
\Rcode{gc.gene.file} or a \Rcode{tumor.coverage.file}. If a tumor coverage file is
provided, then all targets below the coverage minimum are further excluded.

\section{COSMIC annotation}

If a matched normal is not available, it is also helpful to provide
\Rfunction{runAbsoluteCN} the COSMIC database \cite{Forbes2015} via
\Rcode{cosmic.vcf.file}. While this has limited effect on purity and ploidy
estimation due the sparsity of hotspot mutations, it often helps in the manual
curation to compare how well high confidence germline (dbSNP) vs. somatic
(COSMIC) variants fit a particular purity/ploidy combination.

\section{Off-target reads}
\label{seqofftarget}

It is possible to use SNPs in off-target reads in the SNV fitting step by
setting the \Rfunction{runAbsoluteCN} argument \Rcode{remove.off.target.snvs}
to \Rcode{FALSE}.  An often better alternative to including all off-target
reads is running \software{MuTect} with interval "padding" (between 50-100bp).
Instead of including all off-target SNPs, this will only include SNPs in the
flanking regions of targets with \Rcode{remove.off.target.snvs=FALSE} (see
Section~\ref{faqinput}).

We recommend a large pool of normals and generating SNP blacklists as described
in Sections~\ref{artifactfiltering} and \ref{artifactfiltering2}. 

\section{Output}

The \Rcode{plotAbs()} call above will generate the main plots shown in the
manuscript. The R data file (file.rds) contains gene-level copy number calls,
SNV status and LOH calls.  The purity/ploidy combinations are sorted by
likelihood and stored in \Rcode{ret\$results}.

<<output1>>=
names(ret)
@

We provide convenient functions to extract information from this data structure
and show their usage in the next sections. We recommend using these functions
instead of accessing the data directly since data structures might change in
future versions. 

\subsection{Prediction of somatic status and cellular fraction}
\label{predictsomatic}
To understand allelic fractions of particular SNVs, we must know the (i)
somatic status, the (ii) tumor purity, the (iii) local copy number, as well as
the (iv) number of chromosomes harboring the mutations or SNPs. One of
\Biocpkg{PureCN} main functions is to find the most likely combination of these
four values. We further assign posterior probabilities to all possible
combinations or states. Availability of matched normals reduces the search space
by already providing somatic status. 

The \Rfunction{predictSomatic} function provides access to these probabilities.
For predicted somatic mutations, this function also provides cellular fraction
estimates, i.e. the fraction of tumor cells with mutation. Fractions
significantly below 1 indicate sub-clonality\footnote{This number can be above 1
when the observed allelic fraction is higher than expected for a clonal
mutation. This may be due to random sampling, wrong copy number, sub-clonal copy
number events, or wrong purity/ploidy estimates.}:

<<output3>>=
head(predictSomatic(ret), 3)
@

M0 to M7 are multiplicity values, i.e. the number of chromosomes harboring the
mutation (e.g.  1 heterozygous, 2 homozygous if copy number C is 2). Columns
with the ML prefix indicate maximum likelihood estimates, e.g. ML.AR is the
expected allelic ratio of the most likely state, AR is the observed allelic
ratio as provided in the VCF file.  GERMLINE.CONTHIGH and GERMLINE.CONTLOW are
the two contamination states.  The former are homozygous germline SNPs that
were not filtered out because reference alleles from another individual were
sequenced, resulting in allelic fractions smaller than 1. The latter are
non-reference alleles only present in the contamination. 

To annotate the input VCF file with these values:
<<output4>>=
vcf <- predictSomatic(ret, return.vcf=TRUE)
writeVcf(vcf, file="Sample1_PureCN.vcf") 
@

\textbf{Note that the posterior probabilities assume that the purity and ploidy
combination is correct. Before classifying variants, it is thus important to
manually curate samples.} Further note that small inaccuracies in purity can
decrease the classification performance significantly, and we currently
recommend the \Rcode{post.optimize} option when variant classification is
important.

\subsection{Amplifications and deletions}

To call amplifications, we recommend using a cutoff of 6 for focal
amplifications and a cutoff of 7 otherwise. For homozygous deletions, a cutoff
of 0.5 is useful to allow some heterogeneity in copy number.

For samples that failed \Biocpkg{PureCN} calling we recommended using
common log-ratio cutoffs to call amplifications, for example 0.9.

This strategy is implemented in the \Rfunction{callAlterations} function:

<<calling2>>=
gene.calls <- callAlterations(ret)
head(gene.calls)
@

It is also often useful to filter the list further by known biology, for
example to exclude non-focal amplifications of tumor suppressor genes. The
Sanger Cancer Gene Census \cite{Futreal2004} for example provides such a list.

\subsection{Find genomic regions in LOH}

The \Rcode{gene.calls} data.frame described above provides gene-level LOH
information. To find the corresponding genomic regions in LOH, we can use the
\Rfunction{callLOH} function:

<<loh>>=
loh <- callLOH(ret)
head(loh)
@
  
\section{Power to detect somatic mutations}

As final quality control step, we can test if coverage and tumor purity are
sufficent to detect mono-clonal or even sub-clonal somatic mutations. We
strictly follow the power calculation by Carter et al. \cite{Carter2012}. 

The following Figure~\ref{figure/power1-1} shows the power to detect mono-clonal
somatic mutations as a function of tumor purity and sequencing coverage
(reproduced from \cite{Carter2012}):

<<power1, fig.show='hide', fig.width=6, fig.height=6>>=
purity <- c(0.1,0.15,0.2,0.25,0.4,0.6,1)
coverage <- seq(5,35,1)
power <- lapply(purity, function(p) sapply(coverage, function(cv) 
    calculatePowerDetectSomatic(coverage=cv, purity=p, ploidy=2, 
    verbose=FALSE)$power))

# Figure S7b in Carter et al.
plot(coverage, power[[1]], col=1, xlab="Sequence coverage", 
    ylab="Detection power", ylim=c(0,1), type="l")

for (i in 2:length(power)) lines(coverage, power[[i]], col=i)
abline(h=0.8, lty=2, col="grey")     
legend("bottomright", legend=paste("Purity", purity),
    fill=seq_along(purity))
@
\incfig{figure/power1-1}{0.95\textwidth}{Power to detect mono-clonal somatic
mutations.}{Reproduced from \cite{Carter2012}.}

Figure~\ref{figure/power2-1} then shows the same plot for sub-clonal mutations
present in 20\% of all tumor cells: 

<<power2, fig.show='hide', fig.width=6, fig.height=6>>=
coverage <- seq(5,350,1)
power <- lapply(purity, function(p) sapply(coverage, function(cv) 
    calculatePowerDetectSomatic(coverage=cv, purity=p, ploidy=2, 
    cell.fraction=0.2, verbose=FALSE)$power))
plot(coverage, power[[1]], col=1, xlab="Sequence coverage", 
    ylab="Detection power", ylim=c(0,1), type="l")

for (i in 2:length(power)) lines(coverage, power[[i]], col=i)
abline(h=0.8, lty=2, col="grey")     
legend("bottomright", legend=paste("Purity", purity),
    fill=seq_along(purity))
@
\incfig{figure/power2-1}{0.95\textwidth}{Power to detect sub-clonal somatic
mutations present in 20\% of all tumor cells.}{Reproduced from \cite{Carter2012}.}

\clearpage

\section{Command line scripts}

\subsection{Coverage}
\label{cmdcoverage}

We provide a basic template for GC-normalizing BAM or \software{GATK
DepthOfCoverage} files from the command line. For example:

\begin{verbatim}
# From a BAM file
Rscript Coverage.R --outdir ~/tmp/ --bam ex1.bam \
     --gcgene ex1_intervals.txt

# From a GATK DepthOfCoverage file
Rscript Coverage.R --outdir ~/tmp/ --gatkcoverage example_tumor.txt \ 
    --gcgene  example_gc.gene.file.txt     
\end{verbatim}

\begin{table*}
\caption{Coverage command line script.}
\centering
\begin{tabular}{lll}
\toprule
Argument name       & Corresponding PureCN argument & PureCN function \\
\midrule
--help -h           & & \\
--outdir -o         & & \\
--bam -b            & bam.file & \Rfunction{calculateBamCoverageByInterval} \\
--gatkcoverage -g   & coverage.file & \Rfunction{correctCoverageBias} \\
--gcgene -c         & gc.gene.file  & \Rfunction{correctCoverageBias} \\
\bottomrule
\end{tabular}
\end{table*}

\subsection{PureCN}
\label{cmdpurecn}

PureCN.R is an example script to run PureCN with basic parameters:

\begin{verbatim}
# From GC-normalized coverage data
Rscript PureCN.R --outdir ~/tmp/ --tumor example_tumor.txt \ 
    --normal example_normal.txt --vcf example_vcf.vcf -i Sample1 \
     --genome hg19 --gcgene  example_gc.gene.file.txt

# From already segmented data
Rscript PureCN.R --outdir ~/tmp/ --segfile example_seg.txt \ 
    --vcf example_vcf.vcf -i Sample1 --genome hg19 \
    --gcgene  example_gc.gene.file.txt
\end{verbatim}

\begin{table*}
\caption{PureCN command line script.}
\centering
\begin{tabular}{lll}
\toprule
Argument name       & Corresponding PureCN argument & PureCN function \\
\midrule
--help -h           & & \\
--outdir -o         &          &                            \\
--normal -n         & normal.coverage.file   & \Rfunction{runAbsoluteCN}  \\
--tumor -t          & tumor.coverage.file     & \Rfunction{runAbsoluteCN} \\
--vcf -v            & vcf.file          & \Rfunction{runAbsoluteCN}   \\
--genome -g         & genome             & \Rfunction{runAbsoluteCN}  \\
--gcgene -c         & gc.gene.file      & \Rfunction{runAbsoluteCN}   \\
--segfile -f        & seg.file           & \Rfunction{runAbsoluteCN}  \\
--snpblacklist -s   & snp.blacklist & \Rfunction{filterVcfBasic}     \\
--statsfile -a      & stats.file & \Rfunction{filterVcfMuTect}        \\
--targetweightfile -e & target.weight.file & \Rfunction{segmentationCBS} \\
--normaldb -d       & normalDB (serialized with \Rfunction{saveRDS}) & 
    \Rfunction{findBestNormal}, \Rfunction{filterTargets} \\
--sampleid -i       & sampleid  & \Rfunction{runAbsoluteCN}  \\
\bottomrule
\end{tabular}
\end{table*}
 
These R scripts can be found in the extdata directory of the installed package.

\section{Limitations}

\Biocpkg{PureCN} currently assumes a completely diploid normal genome. For
human samples, it tries to detect sex by calculating the coverage ratio of
chromosomes X and Y and will then remove sex chromosomes in male 
samples\footnote{Loss of Y chromosome (LOY) can result in wrong female calls,
especially in high purity samples or if LOY is in both tumor and contaminating
normal cells. The software throws a warning in this case when germline SNPs
are provided.}. For non-human samples, the user needs to manually remove all
non-diploid chromosomes from the coverage data and specify
\Rcode{sex='diploid'} in the \Biocpkg{PureCN} call.

While \Biocpkg{PureCN} supports and models sub-clonal somatic copy number
alterations, it currently assumes that the majority of alterations are
mono-clonal. For most clinical samples, this is reasonable, but very
heterogeneous samples are likely not possible to call without manual curation
(but please note that other algorithms that model poly-genomic tumors do not
necessarily have a higher call rate, since they tend to overfit noisy samples).
Due to the lack of signal, manual curation is also recommended in low purity
samples or very quiet genomes. 

In the absence of matched normals, the software currently requires some normal
contamination to infer germline genotypes. Since cell lines are rarely matched,
\Biocpkg{PureCN} will likely not work well with cell line data.  

Finally, the software currently does not officially support VCF files containing
indels. Support for VCFs generated by \software{MuTect 2} that include both
single nucleotide variants (SNVs) and indels is planned for Bioconductor 3.5.
Experimental support for \software{MuTect 2} VCFs generated in tumor-only mode
is available now.


\section{FAQ}

\paragraph{If the ploidy is frequently too high, please check:}

\begin{itemize}
    \item Does the log-ratio histogram (Figure~\ref{figure/figureexample2-1})
look noisy? If yes, then
    \begin{itemize}
    \item Is the coverage sufficient? Tumor coverages below 80X can be
difficult, especially in low purity samples. Normal coverages below 50X might
result in high variance of log-ratios. See Section~\ref{bestnormal} for finding
a good normal sample for log-ratio calculation.
    \item Is the coverage data of both tumor and normal GC-normalized? If not, 
    see \Rfunction{correctCoverageBias}.
    \item Is the quality of both tumor and normal sufficient? A high AT or
GC-dropout might result in high variance of log-ratios. Challenging FFPE samples
also might need parameter tuning of the segmentation function. See 
\Rfunction{segmentationCBS}. A high expected tumor purity allows more aggressive
segmentation parameters, such as \Rcode{prune.hclust.h=0.2} or higher.
    \item Was the correct target interval file used (genome version and capture
    kit, see Section~\ref{gatkcoverage})? If unsure, ask the help desk of your
    sequencing center.
    \item Were the normal samples run with the same assay and pipeline? 
    \item Did you provide \Rfunction{runAbsoluteCN} all the recommended files
    as described in Section~\ref{secrecommended}?  
    \item For whole-genome data, you most likely get better results using a
    specialized third-party segmentation method as described in
    section~\ref{customseg}, since our default is optimized for targeted
    sequencing.
    \end{itemize}
    \item Otherwise, if log-ratio peaks are clean as in 
Figure~\ref{figure/figureexample2-1}:
    \begin{itemize}
        \item Was MuTect run without a matched normal? If yes, then make sure to 
    provide a SNP blacklist as described in Sections~\ref{artifactfiltering} and 
    \ref{artifactfiltering2}.
        \item A high fraction of sub-clonal copy-number alterations might also
    result in a low ranking of correct low ploidy solutions. 
    \end{itemize}
\end{itemize} 

\paragraph{Will PureCN work with my data?}
\begin{itemize}
    \item \Biocpkg{PureCN} was designed for medium-sized (>2-3Mb) targeted
    panels.  The more data, the better, best results are typically achieved
    in whole-exome data.
    \item The same is obviously true for coverage. Coverages below 80X are
    difficult unless purities are high and coverages are even.
    \item The number of heterozygous SNPs is also important. Copy number probes 
    enriched in SNPs are therefore very helpful.
    \item While \Biocpkg{PureCN} supports to some degree uneven tiling of
    targets, the more evenly distributed the better. Large gaps are problematic
    and might require third-party segmentation tools that support off-target
    reads (Section~\ref{customseg}). 
    \item Whole-genome data is not officially supported and specialized tools
    will likely provide better results.  Third-party segmentation tools
    designed for this data type would be again required.  
    \item \Biocpkg{PureCN} also needs process-matched normal samples, again,
    the more the better.    
\end{itemize}

\paragraph{If you have trouble generating input data PureCN accepts, please check:}
\label{faqinput}
\begin{itemize}
    \item For problems related to generating valid coverage data, either consult
    the \software{GATK} manual for the \software{DepthOfCoverage} tool or
    Section~\ref{purecncoverage} for the equivalent function in
    \Biocpkg{PureCN}.
    \item Currently only VCF files generated by \software{MuTect 1} are officially
supported and well tested. A minimal example \software{MuTect} call would be:
    \begin{verbatim}
    $JAVA -Xmx6g -jar $MUTECT \
    --analysis_type MuTect -R $REFERENCE \
    --dbsnp $DBSNP_VCF \
    --cosmic $COSMIC_VCF \
    -I:normal $BAM_NORMAL \
    -I:tumor $BAM_TUMOR  \
    -o $OUT/${ID}_bwa_mutect_stats.txt \
    -vcf $OUT/${ID}_bwa_mutect.vcf
    \end{verbatim}
    The default output file is the stats or call-stats file; this can be
provided in addition to the required VCF file via \Rcode{args.filterVcf} in
\Rfunction{runAbsoluteCN}. If provided, it may help \Biocpkg{PureCN} filter
    artifacts. This requires \software{MuTect} in version 1.1.7. Note that this
    \software {MuTect} call will keep all off-target calls. We recommend running
    \software{MuTect} with interval file, but include flanking regions:
    \begin{verbatim}
    $JAVA -Xmx6g -jar $MUTECT \
    --analysis_type MuTect -R $REFERENCE \
    --dbsnp $DBSNP_VCF \
    --cosmic $COSMIC_VCF \
    --interval_padding 75 \
    --L $INTERVAL_FILE \
    -I:normal $BAM_NORMAL \
    -I:tumor $BAM_TUMOR  \
    -o $OUT/${ID}_bwa_mutect_stats.txt \
    -vcf $OUT/${ID}_bwa_mutect.vcf
    \end{verbatim}
    We highly recommend finding good values for the \Rcode{interval\_padding}
    argument for each assay. A good cutoff will maximize the number of
    heterozygous SNPs and keep only an acceptable number of lower quality
    calls. To include SNPs in the flanking regions, it is necessary to set
    \Rcode{remove.off.target.snvs=FALSE}. Future versions will automatically
    keep these variants.
\end{itemize}

\paragraph{Questions related to manual curation.}

\Biocpkg{PureCN}, like most other related tools, essentially finds the most
simple explanation of the data. There are three major problems with this
approach:
\begin{itemize}
    \item First, hybrid
capture data can be noisy and the algorithm must distinguish signal from noise;
if the algorithm mistakes noise for signal, then this often results in wrong
high ploidy calls (see Sections~\ref{artifactfiltering} and
\ref{artifactfiltering2}). If all steps in this vignette were followed,
then \Biocpkg{PureCN} should do a good job in ignoring common artifacts. Noisy
samples thus often have outlier ploidy values and are often automatically
flagged by \Biocpkg{PureCN}. The correct solution is in most of these cases
ranked second or third.
    \item The second problem is that signal can be sparse, i.e. when the
tumor purity is very low or when there are only few somatic events.  Manual
curation is often easy in the latter case. For example when small losses are
called as homozygous, but corresponding germline allele-frequencies are
unbalanced (a complete loss would result in balanced germline allele
frequencies, since only normal DNA is left). Future versions might improve
calling in these cases by underweighting uninformative genomic regions.
    \item The third problem is that tumor evolution is fast and complex and very
difficult to incorporate into general likelihood models. Sometimes multiple
solutions explain the data equally well, but one solution is then often clearly
more consistent with known biology, for example LOH in tumor suppressor genes
such as \textit{TP53}.  A basic understanding of both the algorithm and the
tumor biology of the particular cancer type are thus important for curation.
Fortunately, in most cancer types, such ambiguity is rather rare.
\end{itemize}

\paragraph{If all or most of the samples are flagged as:}

\begin{description}
    \item[Noisy segmentation:] The default of 200 for \Rcode{max.segments} is
    calibrated for high quality and high coverage whole exome data. For whole
    genome data or lower coverage data, this value needs to be re-calibrated.
    In case the copy number data looks indeed noisy, please see the first FAQ.
    \item[High AT/GC dropout:] If the data is GC-normalized, then there might
    be issues with either the target intervals or the provided GC content.
    Please double check that all files are correct and that all the coverage
    files are GC-normalized (Section~\ref{secgcbias}).
\end{description}

\appendix

\bibliography{PureCN}

\section*{Annotating intervals with gene symbols}

A simple function that annotates intervals with the \textbf{first}
overlapping interval from the RefSeq gene track:

<<annotatesymbols>>=
tblGenes <- NULL
if (downloadFromUCSC) {
    library(rtracklayer)
    mySession <- browserSession("UCSC")
    genome(mySession) <- "hg19"
    tblGenes <- getTable( ucscTableQuery(mySession, 
        track="RefSeq Genes", table="refGene"))
}

.annotateIntervals <- function(gc.gene.file, tblGenes, 
    output.file = NULL) {
    grGenes <- GRanges(seqnames=tblGenes$chrom, 
        IRanges(start=tblGenes$cdsStart, end=tblGenes$cdsEnd))
    gc <- read.delim(gc.gene.file, as.is=TRUE)
    # misuse this function to convert interval string into data.frame
    gc.data <- readCoverageGatk(gc.gene.file)
    grGC <- GRanges(seqnames=gc.data$chr, 
        IRanges(start=gc.data$probe_start, end=gc.data$probe_end))
    ov <- findOverlaps(grGC, grGenes, select="first")
    gc$Gene <- tblGenes$name2[ov]
    if (!is.null(output.file)) {
        write.table(gc, file = output.file, row.names = FALSE, 
            quote = FALSE, sep = "\t")
    }
    invisible(gc)
}

if (!is.null(tblGenes)) .annotateIntervals(gc.gene.file, tblGenes)
@
\section*{Session Info}
<<sessioninfo, results='asis', echo=FALSE>>=
toLatex(sessionInfo())
@

\end{document}
