%\VignetteKeywords{runAbsoluteCN}
%\VignetteEngine{knitr::knitr}
%\VignetteDepends{PureCN}
%\VignettePackage{PureCN}
%\VignetteIndexEntry{Quick start and command line usage}

\documentclass{article}

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

\usepackage{booktabs} % book-quality tables

\begin{document}

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

\section*{PureCN - Quick Start}

This tutorial provides a quick overview of the command line tools shipping with
\Biocpkg{PureCN}. For the R package and more detailed information, see the main
vignette. 

\subsection*{Update from previous stable versions}

\Biocpkg{PureCN} is fully backward compatible with input generated by versions
1.10 and 1.12. \Biocpkg{PureCN} 1.10 introduced a new normal database format
with several important improvements such as not splitting the database by sample
sex for the normalization of autosomes. It is therefore necessary to at least
re-run the NormalDB.R script described below when upgrading from version 1.8. 

For upgrades from version 1.6, we highly recommend starting from scratch
following this tutorial.

\subsection*{Installation}

For the command line scripts described in this tutorial, we will need to 
install PureCN with suggested dependencies:

\begin{verbatim}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("PureCN", dependencies = TRUE)
\end{verbatim}

Alternatively, manually install the packages required by the command line
scripts:

\begin{verbatim}
BiocManager::install(c("PureCN", "optparse", 
    "TxDb.Hsapiens.UCSC.hg19.knownGene", "org.Hs.eg.db"))
\end{verbatim}

(Replace \Rcode{hg19} with your genome version).

\subsection*{Prepare environment and files}

\begin{itemize}
\item Start R and enter the following to get the path to the command line
    scripts:

<<paths>>=
system.file("extdata", package="PureCN")
@ 

\item Exit R and store this path in an environment variable, for example in 
    BASH:

\begin{verbatim}
$ export PURECN="/path/to/PureCN/extdata"
$ Rscript $PURECN/PureCN.R --help
Usage: /path/to/PureCN/inst/extdata/PureCN.R [options] ...
\end{verbatim}

\item Generate an interval file from a BED file containing baits coordinates:

\begin{verbatim}
# specify path where PureCN should store reference files
$ export OUT_REF="reference_files"
$ Rscript $PURECN/IntervalFile.R --infile baits_hg19.bed \ 
    --fasta hg19.fa --outfile $OUT_REF/baits_hg19_intervals.txt \
    --offtarget --genome hg19 \
    --export $OUT_REF/baits_optimized_hg19.bed \
    --mappability wgEncodeCrgMapabilityAlign100mer.bigWig \
    --reptiming wgEncodeUwRepliSeqK562WaveSignalRep1.bigWig
\end{verbatim}

Internally, this script uses \Biocpkg{rtracklayer} to parse the
\Rcode{infile}. Make sure that the file format matches the file extension. 
See the \Biocpkg{rtracklayer} documentation for problems loading the file.
Check that the genome version of the baits file matches the reference. 
Do not include chrM baits in case the capture kit includes some.

The \Rcode{--offtarget} flag will include off-target reads. Including them is
recommended except for Amplicon data. 

The \Rcode{--genome} version is needed to annotate exons with gene symbols.
Use hg19/hg38 for human genomes, not b37/b38. You might get a warning that an
annotation package is missing.  For hg19, install
\Biocpkg{TxDb.Hsapiens.UCSC.hg19.knownGene} in R.

The \Rcode{--export} argument is optional. If provided, this script will store
the modified intervals as BED file for example (again every
\Biocpkg{rtracklayer} format is supported). This is useful when the coverages
are calculated with third-party tools like GATK.

The \Rcode{--mappability} argument should provide a \Biocpkg{rtracklayer}
parsable file with a mappability score in the first meta data column. If
provided, off-target regions will be restricted to regions specified in this
file.  On-target regions with low mappability will be excluded. For hg19,
download the file from the UCSC website. Choose the kmer size that best fits
your average mapped read length. For hg38, download recommended 76-kmer or
100-kmer mappability files through the courtesy of the Waldron lab from: 

\begin{itemize}
\item \url{https://s3.amazonaws.com/purecn/GCA\_000001405.15\_GRCh38\_no\_alt\_analysis\_set\_76.bw}
\item \url{https://s3.amazonaws.com/purecn/GCA\_000001405.15\_GRCh38\_no\_alt\_analysis\_set\_100.bw}
\end{itemize}

See the FAQ section of the main vignette for instruction how to generate such a
file for other references.

Similarly, the \Rcode{--reptiming} argument takes a replication timing score in
the same format.  If provided, GC-normalized and log-transformed coverage is
tested for a linear relationship with this score and normalized accordingly.
This is optional and provides only a minor benefit for coverage normalization,
but can identify samples with high proliferation. Requires \Rcode{--offtarget}
to be useful. 

Generation of this interval file is not needed when CNVkit is used to generate
coverage data (covered at the end of this tutorial). 
\end{itemize}

\subsection*{Create VCF files}

\Biocpkg{PureCN} does not ship with a variant caller. Use a third-party tool to
generate a VCF for each sample. 

Important recommendations:

\begin{itemize}
    \item Use \software{MuTect} 1.1.7 if possible
    \item Support for \software{MuTect 2} and \software{FreeBayes} is available
    for tumor-only VCFs, but currently poorly tested and only very limited
        artifact filtering will be performed for these callers. See the FAQ
        section in the main vignette for common problems and questions related
        to input data.
    \item Since germline SNPs are needed to infer allele-specific copy numbers,
        the provided VCF needs to contain both somatic and germline variants.
        Make sure that upstream filtering does not remove high quality SNPs, in
        particular due to presence in germline databases.
    \item Run the variant caller with a 50-75 base pair interval padding to
        increase the number of heterozygous SNPs
\end{itemize}


\subsection*{Run PureCN with internal segmentation}

The following describes \Biocpkg{PureCN} runs with internal copy number
normalization and segmentation. 

\subsubsection*{Coverage}

For each sample, tumor and normal, calculate GC-normalized coverages:

\begin{verbatim}
# Calculate and GC-normalize coverage from a BAM file 
$ Rscript $PURECN/Coverage.R --outdir $OUT/$SAMPLEID \ 
    --bam ${SAMPLEID}.bam \
    --intervals $OUT_REF/baits_hg19_intervals.txt

# GC-normalize coverage from a GATK DepthOfCoverage file
Rscript $PURECN/Coverage.R --outdir $OUT/$SAMPLEID \
    --coverage ${SAMPLEID}.coverage.sample_interval_summary \ 
    --intervals $OUT_REF/baits_hg19_intervals.txt
\end{verbatim}

Similar to GATK, this script also takes a text file containing a list of BAM or
coverage file names (one per line). The file extension must be .list:

\begin{verbatim}
# Calculate and GC-normalize coverage from a list of BAM files 
$ Rscript $PURECN/Coverage.R --outdir $OUT \ 
    --bam normals.list \
    --intervals $OUT_REF/baits_hg19_intervals.txt \
    --cpu 4
\end{verbatim}

Important recommendations:

\begin{itemize}
    \item Only provide \Rcode{--keepduplicates} or \Rcode{--removemapq0}
        if you know what you are doing and always use the same command line
        arguments for tumor and the normals
\end{itemize}

\subsubsection*{NormalDB}

To build a normal database for coverage normalization, copy the paths to all
GC-normalized normal coverage files in a single text file, line-by-line:

\begin{verbatim}
ls -a normal*loess.txt | cat > example_normal.list

# From already GC-normalized files
$ Rscript $PURECN/NormalDB.R --outdir $OUT_REF \
    --coveragefiles example_normal.list \
    --genome hg19 --assay agilent_v6

# When normal panel VCF is available (highly recommended for unmatched samples)
$ Rscript $PURECN/NormalDB.R --outdir $OUT_REF \
    --coveragefiles example_normal.list \
    --genome hg19 --normal_panel $NORMAL_PANEL --assay agilent_v6
\end{verbatim}

Important recommendations:

\begin{itemize}
    \item Consider generating different databases when differences are
    significant, e.g. for samples with different read lengths or insert size
    distributions

    \item In particular, do not mix normal data obtained with different capture
    kits (e.g. Agilent SureSelect v4 and v6)

    \item Provide a normal panel VCF here to precompute mapping bias for faster
    runtimes. The only requirement for the VCF is an \Rcode{AD} format field
    containing the number of reference and alt reads for all samples. See the
    example file \Rcode{\$PURECN/normalpanel.vcf.gz}.

    \item For ideal results, examine the interval\_weights.png file to find good 
    off-target bin widths. You will need to re-run IntervalFile.R with the
    \Rcode{--offtargetwidth} parameter and re-calculate the coverages.

    \item The \Rcode{--assay} argument is optional and is only used to add the
    provided assay name to all output files

    \item A warning pointing to the likely use of a wrong baits file
    means that more than 5\% of targets have close to 0 coverage in all normal
    samples. A BED file with the low coverage targets will be generated in
    \Rcode{--outdir}.  If for any reason there is no access to the correct
    file, it is recommended to re-run the IntervalFile.R command and provide
    this BED file with \Rcode{--exclude}.

\end{itemize}

\subsubsection*{PureCN}

Now that the assay-specific files are created and all coverages calculated, we
run PureCN.R to normalize, segment and determine purity and ploidy:

\begin{verbatim}
mkdir $OUT/$SAMPLEID

# Without a matched normal (minimal test run)
$ Rscript $PURECN/PureCN.R --out $OUT/$SAMPLEID \
    --tumor $OUT/$SAMPLEID/${SAMPLEID}_coverage_loess.txt \
    --sampleid $SAMPLEID \
    --vcf ${SAMPLEID}_mutect.vcf \
    --normaldb $OUT_REF/normalDB_hg19.rds \
    --intervals $OUT_REF/baits_hg19_intervals.txt \
    --genome hg19 

# Production pipeline run
$ Rscript $PURECN/PureCN.R --out $OUT/$SAMPLEID \
    --tumor $OUT/$SAMPLEID/${SAMPLEID}_coverage_loess.txt \
    --sampleid $SAMPLEID \
    --vcf ${SAMPLEID}_mutect.vcf \
    --statsfile ${SAMPLEID}_mutect_stats.txt \
    --normaldb $OUT_REF/normalDB_hg19.rds \
    --normal_panel $OUT_REF/mapping_bias_hg19.rds \
    --intervals $OUT_REF/baits_hg19_intervals.txt \
    --intervalweightfile $OUT_REF/interval_weights_hg19.txt \
    --snpblacklist hg19_simpleRepeats.bed \
    --genome hg19 \
    --force --postoptimize --seed 123

# With a matched normal (test run; for production pipelines we recommend the
# unmatched workflow described above)
$ Rscript $PURECN/PureCN.R --out $OUT/$SAMPLEID \
    --tumor $OUT/$SAMPLEID/${SAMPLEID}_coverage_loess.txt \
    --normal $OUT/$SAMPLEID/${SAMPLEID_NORMAL}_coverage_loess.txt \
    --sampleid $SAMPLEID \
    --vcf ${SAMPLEID}_mutect.vcf \
    --normaldb $OUT_REF/normalDB_hg19.rds \
    --intervals $OUT_REF/baits_hg19_intervals.txt \
    --genome hg19

# Recreate output after manual curation of ${SAMPLEID}.csv
$ Rscript $PURECN/PureCN.R --rds $OUT/$SAMPLEID/${SAMPLEID}.rds
\end{verbatim}

Important recommendations:

\begin{itemize}
    \item Even if matched normals are available, it is often better to use the
        normal database for coverage normalization. \textbf{When a matched 
        normal coverage is provided with} \Rcode{--normal} \textbf{then the
        pool of normal coverage normalization and denoising steps are skipped!}

    \item Always provide the normal coverage database to ignore low quality
        regions in the segmentation and to increase the sensitivity for homozygous
        deletions in high purity samples.

    \item The normal panel VCF file is useful for mapping bias correction and
        especially recommended without matched normals. See the FAQ of the main
        vignette how to generate this file. It is not essential for test runs.  

    \item The \software{MuTect} 1.1.7 stats file (the main output file besides
        the VCF) should be provided for better artifact filtering. If the VCF
        was generated by a pipeline that performs good artifact filtering, this
        file is not needed.

    \item The \Rcode{--postoptimize} flag defines that purity should be
        optimized using both variant allelic fractions and copy number instead
        of copy number only.  This results in a significant runtime increase for
        whole-exome data.

    \item If \Rcode{--out} is a directory, it will use the sample id as file
        prefix for all output files. Otherwise \Biocpkg{PureCN} will use
        \Rcode{--out} as prefix.   
    
    \item The \Rcode{--parallel} flag will enable the parallel fitting of local
        optima. See \Biocpkg{BiocParallel} for details. This script will
        use the default backend.

    \item Defaults are well calibrated and should produce close to ideal results
        for most samples. A few common cases where changing defaults makes sense:
        \begin{description}
            \item[High purity and high quality:] For cancer types with a high
                expected purity, such as Ovarian cancer, AND when quality is
                expected to be very good (high coverage, young samples),
                \Rcode{--maxcopynumber 8}
            \item[Small panels with high coverage:] \Rcode{--padding 100} (or
                higher), requires running the variant caller with this padding
                or without interval file. Use the same settings for the panel of
                normals VCF so that SNPs in the flanking regions have reliable
                mapping bias estimates. The \Rcode{--maxhomozygousloss}
                parameter might also need some adjustment for very small panels
                with large gaps around captured deletions.
            \item[Cell lines:] Safely skip the search for low purity
                solutions in cell lines: \Rcode{--maxcopynumber 8},
                \Rcode{--minpurity 0.9}, \Rcode{--maxpurity 0.99}.
                Add \Rcode{--modelhomozygous} to find regions of LOH in samples
                without normal contamination (do not provide this flag when
                matched normal data are available in the VCF).
            \item[cfDNA:] \Rcode{--minpurity 0.1}, \Rcode{--minaf 0.01} (or
                lower) and \Rcode{--error 0.0005} (or lower, when there is
                UMI-based error correction). Note that the estimated purity can
                be very wrong when the true purity is below 5-7\%; these samples
                are usually flagged as non-aberrant.
            \item[Amplicon data:] \Rcode{--model betabin} (Amplicon data is not
                officially supported)
            \item[All assays:] \Rcode{--maxsegments} should set to a value so 
                that with few exceptions only poor quality samples exceed this 
                cutoff. For cancer types with high heterogenity, it is also
                recommended to increase \Rcode{--maxnonclonal} to 0.3-0.4 (this
                will increase the runtime significantly for whole-exome data).
        \end{description}
\end{itemize}

\subsection*{Run PureCN with third-party segmentation}

If you already have a segmentation from third-party tools (for example CNVkit,
EXCAVATOR2). For a test run:

\begin{verbatim}
Rscript $PURECN/PureCN.R --out $OUT/$SAMPLEID  \
    --sampleid $SAMPLEID \
    --segfile $OUT/$SAMPLEID/${SAMPLEID}.cnvkit.seg \
    --vcf ${SAMPLEID}_mutect.vcf \
    --intervals $OUT_REF/baits_hg19_intervals.txt \
    --genome hg19 
\end{verbatim}

See the main vignette for more details and file formats.

For a production pipeline run we provide again more information about the assay
and genome. Here an CNVkit example (CNVkit runs without normal reference
samples are not recommended):

\begin{verbatim}
# Provide a normal panel VCF to remove mapping biases, pre-compute 
# position-specific bias for much faster runtimes with large panels
# This needs to be done only once for each assay
Rscript $PURECN/NormalDB.R --outdir $OUT_REF --normal_panel $NORMAL_PANEL \
    --assay agilent_v6 --genome hg19 --force

# Export the segmentation in DNAcopy format
cnvkit.py export seg $OUT/$SAMPLEID/${SAMPLEID}_cnvkit.cns --enumerate-chroms \
    -o $OUT/$SAMPLEID/${SAMPLEID}_cnvkit.seg

# Run PureCN by providing the *.cnr and *.seg files 
Rscript $PURECN/PureCN.R --out $OUT/$SAMPLEID  \
    --sampleid $SAMPLEID \
    --tumor $OUT/$SAMPLEID/${SAMPLEID}_cnvkit.cnr \
    --segfile $OUT/$SAMPLEID/${SAMPLEID}_cnvkit.seg \
    --normal_panel $OUT_REF/mapping_bias_agilent_v6_hg19.rds \
    --vcf ${SAMPLEID}_mutect.vcf \
    --statsfile ${SAMPLEID}_mutect_stats.txt \
    --snpblacklist hg19_simpleRepeats.bed \
    --genome hg19 \
    --funsegmentation none \
    --force --postoptimize --seed 123
\end{verbatim}

Important recommendations:

\begin{itemize}
    \item The \Rcode{--funsegmentation} argument controls if the data should to
        be re-segmented using germline BAFs (default). Set this value to
        \Rcode{none} if the provided segmentation should be used as is. 
    \item Since CNVkit provides all necessary information in the *.cnr output
        files, the \Rcode{--intervals} argument is not required.     
\end{itemize}

\subsection*{Dx}

Dx.R extracts copy number and mutation metrics from PureCN.R output. 

\begin{verbatim}
# Provide a BED file with callable regions, for examples obtained by
# GATK CallableLoci. Useful to calculate mutations per megabase and
# to exclude low quality regions.
grep CALLABLE ${SAMPLEID}_callable_status.bed > \ 
    ${SAMPLEID}_callable_status_filtered.bed

# Only count mutations in callable regions, also subtract what was ignored
# in PureCN.R via --snpblacklist, like simple repeats, from the mutation per 
# megabase calculation
# Also search for the COSMIC mutation signatures
# (http://cancer.sanger.ac.uk/cosmic/signatures)
Rscript $PureCN/Dx.R --out $OUT/$SAMPLEID/$SAMPLEID \
    --rds $OUT/SAMPLEID/${SAMPLEID}.rds \
    --callable ${SAMPLEID}_callable_status_filtered.bed \
    --exclude hg19_simpleRepeats.bed \
    --signatures

# Restrict mutation burden calculation to coding sequences
Rscript $PureCN/FilterCallableLoci.R --genome hg19 \
    --infile ${SAMPLEID}_callable_status_filtered.bed \
    --outfile ${SAMPLEID}_callable_status_filtered_cds.bed \
    --exclude '^HLA'
 
Rscript $PureCN/Dx.R --out $OUT/$SAMPLEID/${SAMPLEID}_cds \
    --rds $OUT/SAMPLEID/${SAMPLEID}.rds \
    --callable ${SAMPLEID}_callable_status_filtered_cds.bed \
    --exclude hg19_simpleRepeats.bed
\end{verbatim}

Important recommendations:

\begin{itemize}
    \item Run GATK CallableLoci with \Rcode{--minDepth N} where N is roughly 
        20\% of the mean target coverage of all samples.
\end{itemize}

\clearpage
\section*{Reference}

\begin{table*}
\caption{IntervalFile}
\begin{tabular}{lll}
\toprule
Argument name   & Corresponding PureCN argument & PureCN function \\
\midrule
--fasta         & reference.file & \Rfunction{preprocessIntervals} \\
--infile        & interval.file & \Rfunction{preprocessIntervals} \\
--offtarget     & off.target    & \Rfunction{preprocessIntervals} \\
--targetwidth   & average.target.width & \Rfunction{preprocessIntervals} \\
--mintargetwidth & min.target.width & \Rfunction{preprocessIntervals} \\
--smalltargets   & small.targets & \Rfunction{preprocessIntervals} \\
--offtargetwidth & average.off.target.width & \Rfunction{preprocessIntervals} \\
--offtargetseqlevels & off.target.seqlevels & \Rfunction{preprocessIntervals} \\
--mappability   & mappability   & \Rfunction{preprocessIntervals} \\
--minmappability & min.mappability   & \Rfunction{preprocessIntervals} \\
--reptiming     & reptiming   & \Rfunction{preprocessIntervals} \\
--reptimingwidth & average.reptiming.width & \Rfunction{preprocessIntervals}  \\
--genome        & txdb, org & \Rfunction{annotateTargets} \\
--outfile       & & \\
--export        & & \Rfunction{rtracklayer::export}  \\
--version -v    & & \\
--force -f      & & \\
--help -h       & & \\
\bottomrule
\end{tabular}
\end{table*}

\begin{table*}
\caption{Coverage}
\begin{tabular}{llp{5.5cm}}
\toprule
Argument name       & Corresponding PureCN argument & PureCN function \\
\midrule
--bam         & bam.file & \Rfunction{calculateBamCoverageByInterval} \\
--bai         & index.file & \Rfunction{calculateBamCoverageByInterval} \\
--coverage    & coverage.file & \Rfunction{correctCoverageBias} \\
--intervals      & interval.file  & \Rfunction{correctCoverageBias} \\
--method      & method  & \Rfunction{correctCoverageBias} \\
--keepduplicates  & keep.duplicates  & \Rfunction{calculateBamCoverageByInterval} \\
--removemapq0  & mapqFilter  & \Rfunction{ScanBamParam} \\
--outdir      & & \\
--cpu         & & Number of CPUs to use when multiple BAMs are provided (deprecated) \\
--cores       & & Number of CPUs to use when multiple BAMs are provided \\
--parallel    & & Use default BiocParallel backend when multiple BAMs are provided \\
--seed        & & \\
--version -v  & & \\
--force -f    & & \\
--help -h     & & \\
\bottomrule
\end{tabular}
\end{table*}

\begin{table*}
\caption{NormalDB}
\begin{tabular}{lll}
\toprule
Argument name       & Corresponding PureCN argument & PureCN function \\
\midrule
--coveragefiles    & normal.coverage.files & \Rfunction{createNormalDatabase} \\
--normal\_panel    & normal.panel.vcf.file & \Rfunction{calculateMappingBiasVcf} \\
--maxmeancoverage  & max.mean.coverage & \Rfunction{createNormalDatabase} \\ 
--assay -a          & Optional assay name & Used in output file names. \\
--genome -g        & Optional genome version & Used in output file names. \\
--outdir -o         & & \\
--version -v        & & \\
--force -f          & & \\
--help -h           & & \\
\bottomrule
\end{tabular}
\end{table*}

\begin{table*}
\caption{PureCN}
\begin{tabular}{llp{5.5cm}}
\toprule
Argument name      & Corresponding PureCN argument & PureCN function \\
\midrule
--sampleid -i      & sampleid  & \Rfunction{runAbsoluteCN}  \\
--normal           & normal.coverage.file   & \Rfunction{runAbsoluteCN}  \\
--tumor            & tumor.coverage.file     & \Rfunction{runAbsoluteCN} \\
--vcf              & vcf.file          & \Rfunction{runAbsoluteCN}   \\
--rds              & file.rds          & \Rfunction{readCurationFile}   \\
--normal\_panel    & normal.panel.vcf.file & \Rfunction{setMappingBiasVcf} \\
--normaldb         & normalDB (serialized with \Rfunction{saveRDS}) & 
    \Rfunction{calculateTangentNormal}, \Rfunction{filterTargets} \\
--segfile          & seg.file           & \Rfunction{runAbsoluteCN}  \\
--sex              & sex                & \Rfunction{runAbsoluteCN}  \\
--genome           & genome             & \Rfunction{runAbsoluteCN}  \\
--intervals        & interval.file      & \Rfunction{runAbsoluteCN}   \\
--statsfile        & stats.file & \Rfunction{filterVcfMuTect}        \\
--minaf            & af.range & \Rfunction{filterVcfBasic} \\
--snpblacklist     & snp.blacklist & \Rfunction{filterVcfBasic}     \\
--error            & error & \Rfunction{runAbsoluteCN}     \\
--dbinfoflag       & DB.info.flag & \Rfunction{runAbsoluteCN}     \\
--popafinfofield   & POPAF.info.field & \Rfunction{runAbsoluteCN}     \\
--mincosmiccnt     & min.cosmic.cnt & \Rfunction{setPriorVcf}     \\
--funsegmentation  & fun.segmentation & \Rfunction{runAbsoluteCN} \\ 
--alpha            & alpha & \Rfunction{segmentationCBS} \\
--undosd           & undo.SD & \Rfunction{segmentationCBS} \\
--maxsegments      & max.segments & \Rfunction{runAbsoluteCN} \\
--intervalweightfile & interval.weight.file & \Rfunction{segmentationCBS} \\
--minpurity        & test.purity & \Rfunction{runAbsoluteCN} \\
--maxpurity        & test.purity & \Rfunction{runAbsoluteCN} \\
--minploidy        & min.ploidy & \Rfunction{runAbsoluteCN} \\
--maxploidy        & max.ploidy & \Rfunction{runAbsoluteCN} \\
--maxcopynumber    & test.num.copy & \Rfunction{runAbsoluteCN} \\
--postoptimize     & post.optimize & \Rfunction{runAbsoluteCN} \\    
--bootstrapn       & n & \Rfunction{bootstrapResults} \\
--modelhomozygous  & model.homozygous & \Rfunction{runAbsoluteCN} \\ 
--model            & model & \Rfunction{runAbsoluteCN} \\ 
--logratiocalibration & log.ratio.calibration & \Rfunction{runAbsoluteCN} \\ 
--maxnonclonal     & max.non.clonal & \Rfunction{runAbsoluteCN} \\ 
--maxhomozygousloss & max.homozygous.loss & \Rfunction{runAbsoluteCN} \\ 
--outvcf           & return.vcf & \Rfunction{predictSomatic} \\
--out -o           & &                            \\
--parallel         & BPPARAM & \Rfunction{runAbsoluteCN} \\
--cores            & BPPARAM & \Rfunction{runAbsoluteCN} \\
--seed             & & \\
--version -v       & & \\
--force -f         & & \\
--help -h          & & \\
\bottomrule
\end{tabular}
\end{table*}

\begin{table*}
\caption{Dx}
\begin{tabular}{lll}
\toprule
Argument name  & Corresponding PureCN argument & PureCN function \\
\midrule
--rds         & file.rds & \Rfunction{readCurationFile}   \\
--callable    & callable & \Rfunction{callMutationBurden} \\
--exclude     & exclude  & \Rfunction{callMutationBurden} \\
--maxpriorsomatic & max.prior.somatic  & \Rfunction{callMutationBurden} \\
--signatures & & \Rfunction{deconstructSigs::whichSignatures} \\
--out         & & \\
--version -v  & & \\
--force -f    & & \\
--help -h     & & \\
\bottomrule
\end{tabular}
\end{table*}
\clearpage

\subsection*{Session Info}
<<sessioninfo, results='asis', echo=FALSE>>=
toLatex(sessionInfo())
@

\end{document}
