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\begin{document}

\title{LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution models}

\author[1]{Gianluca Ascolani} 
\author[1]{Fabrizio Angaroni}
\author[1]{Davide Maspero} 
\author[2]{Narra Lakshmi Sai Bhavesh} 
\author[3,4]{Rocco Piazza} 
\author[5,6]{Chiara Damiani} 
\author[3]{Daniele Ramazzotti}
\author[1,4]{Marco Antoniotti} 
\author[1,4,7]{Graudenzi Alex}

\affil[1]{Dept. of Informatics, Systems and Communication, Universit\'{a} degli Studi di Milano-Bicocca, Milan, Italy.}
\affil[2]{Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, India.}
\affil[3]{Dept.of Medicine and Surgery, Universit\'{a} degli Studi di Milano-Bicocca, Milan, Italy.}
\affil[4]{Bicocca Bioinformatics Biostatistics and Bioimaging Centre -- B4, Milan, Italy.}
\affil[5]{Dept. of Biotechnology and Biosciences, Universit\'{a} degli Studi di Milano-Bicocca, Milan, Italy.}
\affil[6]{ISBE/SYSBIO Centre of Systems Biology, Milan, Italy.}
\affil[7]{Inst. of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy.}

\date{\today}
\maketitle

\begin{tcolorbox}{\bf Overview.}

LACE 2.0 includes: (a) an easy-to-use module for data processing and organization, which allows one to test and adjust several quality metrics and relevance filters, and to annotate gene variants; (b) a module for the interactive visualization of the inferred cancer evolution models, which returns both the \emph{longitudinal clonal tree} and the \emph{fishplot}, and allows one to examine the different parts of the model (i.e., clones and temporal relations), also by querying external databases such as Ensembl.org. Importantly, LACE 2.0 requires a minimum set of parameters, which are sufficient to mitigate the aforementioned noise sources and infer reliable models of cancer evolution.

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{\em In this vignette, we give an overview of the Shiny App by presenting its main functions.}

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\tableofcontents

LACE 2.0 is a new release of the LACE R package. LACE 2.0 is capable of performing 
clonal evolution analyses for single-cell sequencing data including longitudinal 
experiments. LACE 2.0 allows to annotate variants and retrieve the relevant mutations 
interactively based on user-defined filtering criteria; it infers the maximum 
likelihood clonal tree, cell matrix attachments and false positive/negative rates 
using boolean matrix factorization. Furthermore, LACE 2.0 allows to investigate 
cancer clonal evolution under different experimental conditions and the occurrence 
of single mutations which can be queried via \emph{ensembl} database.

\section{Installation of LACE 2.0 R
package}\label{installation-of-lace-2.0-r-package}

The package is available on GitHub and Bioconductor. LACE 2.0 requires R
\textgreater{} 4.1.0 and Bioconductor. To install Bioconductor run:

\begin{verb}
if (!require("BiocManager", quietly = TRUE))

    install.packages("BiocManager")
\end{verb}

To install LACE 2.0 run:

\begin{verb}
remotes::install_github("https://github.com/BIMIB-DISCo/LACE", dependencies = TRUE)
\end{verb}

LACE 2.0 uses \emph{Annovar} and \emph{Samtools suite} as back-ends for
variant calling annotation and depth computation, respectively. Please
refer to the next section to install them.


\section{Installation of other required
softwares}\label{installation-of-other-required-softwares}

\emph{Annovar} is a widely used variant calling software freely
available upon registration to their website at
https://annovar.openbioinformatics.org/en/latest/. The package contains
\emph{Perl} scripts and variant calling annotation reference databases
for the human species. For other databases, please refer to their
website. If the scripts are installed in binary search path, then LACE
2.0 will detect them automatically.

\emph{Perl} (https://www.perl.org/) is required to run \emph{Annovar}.

\emph{Samtools suite} is a standard set of tools and libraries to handle
SAM/BAM/BED file format and perform a variety of common operations on
sequencing data. It is freely available at http://www.htslib.org/ and
https://github.com/samtools/htslib. To install \emph{Samtools} follow
the instructions in their website.


\section{Running LACE 2.0}\label{run-lace-2.0}

To start LACE 2.0 user interface run:

\begin{verb}
library(LACE)

LACE2()
\end{verb}


\section{Using LACE 2.0}\label{using-lace-2.0}

LACE 2.0 has been thought to be used on single cell sequencing data for
which it is available variant calling data in standard VCF format and
binary aligned data in standard BAM format.

The user is provided with an interface to initiate a project and to set
filter thresholds after which annotation of variants, filtering of data
and depth at variant sites are retrieved. Both annotation and depth
derivation are computationally expensive steps. LACE 2.0 reduces
possible re-computation by detecting parameter variations of the user
interface and by comparison of the timestamps of interface state, inputs
and outputs. Intermediary and final data are stored in the designated
folders.

The operation is followed by the possibility for the user to select
variants which are drivers of the understudied biological problem.

At this point, the inferential step is executed so that the most
likelihood longitudinal clonal tree and clonal prevalences are retrieved
together with the best set of false positive/negative rates among those
provided by the user.

The results are displayed via an interactive interface.


\section{Interface}\label{interface}

The interface is divided in two parts interleaved by variant selection
and inferential computation parts: the processing interface and the
results interface.


\subsection{Project creation}\label{project-creation}

To begin the clonal analyses the user needs to create a project by
choosing a folder and a meaningful name for the project.

%\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/Project_tab.png}

If the project path does not contain former LACE projects, it asks the
user to create a new one (``Create project'') and all intermediate and
final results will be stored in structured sub-folders of the chosen
project path.

LACE 2.0 automatically recognizes if the selected path is a LACE project
and proposes the user to load it
(\includegraphics[width=0.1\textwidth,height=\textheight]{./resources/load_project.png})


\subsection{Sidebar and Demos}\label{sidebar-and-demos}

The sidebar is divided in Projects and Current Project. Projects contain
a set of recently opened projects which can be reloaded by selecting the
respective project name.

\includegraphics[width=0.3\textwidth,height=\textheight]{./resources/Projects_menu.png}

Under Projects, there is the sub-menu Demos. LACE 2.0 provides the user
with two demos:

\begin{itemize}

\item
  Melanoma dataset {[}Rambow, Florian, et al.~``Toward minimal residual
  disease-directed therapy in melanoma.'' Cell 174.4 (2018): 843-855.{]}
  containing single cell variant annotations and depths for a total of
  674 cells sampled at 4 different time points:

  \begin{enumerate}
  \def\labelenumi{\arabic{enumi}.}

  \item
    sample before treatment

    \begin{itemize}

    \item
      BRAF inhibitor treatment
    \end{itemize}
  \item
    sample after 4 days
  \item
    sample after 28 days
  \item
    sample after 57 days
  \end{enumerate}
\item
  Small dataset containing only 3 cells per 4 sampling time points.
\end{itemize}

Current Project allows the user to save and load the parameters of the
whole project or of the active tab.


\subsection{Processing interface}\label{processing-interface}


\subsubsection{Single Cell Metadata}\label{single-cell-metadata}

In order to perform clonal analyses it is required to provide some
information about the experiment such as the cell IDs which are used to
retrieve the VCF/BAM files and the sample name each cell belongs to.
These metadata are generally included with the experimental sequencing
data as part of the library preparation. LACE 2.0 accepts tsv/csv
tabular formats with headers and rds standard data R format. Metadata
file can include more information relative to the experimental setup and
the platform used.

\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/SCmeta_tab.png}

The user should identify and select the ID and sampling columns. The
user can also reorder the sampling names

\includegraphics[width=0.6\textwidth,height=\textheight]{./resources/samples_order.png}

so to recreate the chronological order of longitudinal events of the
experiment, or to explore different orders.


\subsubsection{Annotations}\label{annotations}

Annotations of variant calling data is performed using \emph{Annovar} as
back-end. Each mutation of each cell is annotated based on the
annotation database used. \emph{Annovar} provides a database for the
human species useful to tag cancer mutations and whenever possible their
functional effect.

%\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/Annotation_tab.png}

If \emph{Annovar}'s \emph{Perl} scripts are available in the standard
binary paths, they are detected and the \emph{Annovar} folder field is
autocompleted; otherwise, the user should provide the folder containing
the \emph{Perl} scripts. The user should also provide the database to
use for the annotation and the folder containing the variant calling
files (VCF).



\subsubsection{Variant filtering}\label{variant-filtering}

All types of single cell sequencing data is characterized by various
sources of noise which depends on the technology used. Detected mutation
might be characterized by low quality score or low statistical power.
Some mutations can be neglected, while others might cause relevant
effects, especially on exonic regions where variations can result in
changes of the translational process and modify their functional form.

In order to avoid small and possibly spurious fluctuations on sequencing
data, variants can be filtered if they have low supporting evidences.
The user can set the minimum values for:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  the number of reads supporting the alternative alleles in a cell,
\item
  the frequency of the minor allele for each referenced SNP included in
  a default global population,
\item
  the cell frequency per sample showing the mutation at the same site.
\end{enumerate}

\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/Filters_tab.png}

Furthermore, the user can choose which functional exonic variation
should be considered or neglected. For example, there are cases in which
unknown and synonymous mutations in exonic regions are disregarded
because their effect cannot be explicitly related to the experimental
condition. All possible variant functions and their explanations are
resumed in the following table:

\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright
Annotation (+)\strut
\end{minipage} & \begin{minipage}[b]{0.78\columnwidth}\raggedright
Explanation\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.16\columnwidth}\raggedright
frameshift insertion\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
an insertion of one or more nucleotides that cause frameshift changes in
protein coding sequence\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
frameshift deletion\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
a deletion of one or more nucleotides that cause frameshift changes in
protein coding sequence\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
frameshift block substitution\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
a block substitution of one or more nucleotides that cause frameshift
changes in protein coding sequence\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
stopgain\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
a nonsynonymous SNV, frameshift insertion/deletion, nonframeshift
insertion/deletion or block substitution that lead to the immediate
creation of stop codon at the variant site. For frameshift mutations,
the creation of stop codon downstream of the variant will not be counted
as ``stopgain''!\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
stoploss\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
a nonsynonymous SNV, frameshift insertion/deletion, nonframeshift
insertion/deletion or block substitution that lead to the immediate
elimination of stop codon at the variant site\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
nonframeshift insertion\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
an insertion of 3 or multiples of 3 nucleotides that do not cause
frameshift changes in protein coding sequence\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
nonframeshift deletion\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
a deletion of 3 or mutliples of 3 nucleotides that do not cause
frameshift changes in protein coding sequence\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
nonframeshift block substitution\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
a block substitution of one or more nucleotides that do not cause
frameshift changes in protein coding sequence\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
nonsynonymous SNV\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
a single nucleotide change that cause an amino acid change\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
synonymous SNV\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
a single nucleotide change that does not cause an amino acid
change\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright
unknown\strut
\end{minipage} & \begin{minipage}[t]{0.78\columnwidth}\raggedright
unknown function (due to various errors in the gene structure definition
in the database file)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

(+) see \emph{Annovar}


\subsubsection{Single cell sampling
depth}\label{single-cell-sampling-depth}

The number of reads per SNV site represents an optimal filter to
retrieve relevant mutations. Depth at specific sites is usually not
provided in standard alignment or variant calling pipelines and it is
computationally expensive to retrieve. The user should provide the
folder with aligned data and, if not found already, the \emph{samtools}
executable folder location to compute depth only on sites passing the
variant filters set in the previous tab.

\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/Depth_tab.png}


\subsection{Selection of relevant
variants}\label{selection-of-relevant-variants}

Not all mutations are distinctive of the disease or experiment
understudied. Identifying relevant and driver variants allows to
reproduce a more significant longitudinal clonal tree. The user can
select a set of filters based on gold standards and other analyses such
as:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  the minimum number of reads at given mutation site
\item
  the maximum number of missing data per gene
\item
  the minimum median depth per locus
\item
  the minimum median depth supporting the mutation
\item
  subset of known genes
\end{enumerate}

\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/Variant_tab.png}

Finding the parameters to select variants is not an easy task, and the
user might not know in advance how to choose the best set of filters.
Hence, the user can press ``Select variants''
(\includegraphics[width=0.1\textwidth,height=\textheight]{./resources/select_variant.png})
to perform the aforementioned computations on VCF and BAM files to
derive all the necessary aggregated information on the sampled cells.
Afterward, the user is presented with a interactive live preview of
variants passing the filters (including relevant parameters which can
help in the selection) while values are changed.

\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/Interactive_Variant_tab.png}


\subsection{Inference}\label{inference}

The inferential tab allows the user to set all the parameters to solve
the boolean matrix factorization problem and estimate the model
parameters of the Bayesian model using a MCMC to maximize the
likelihood.

Inferential step uses the following set of parameters:

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item
  Learning rate
\item
  False positive rates for each sample
\item
  False negative rates for each sample
\item
  Number of iterations in each MCMC search
\item
  Number of restart for the MCMC 5 Early stopping number of iterations
  with no growing likelihood\\
\item
  Number of parallel processes
\item
  Random seed to recreate simulations
\item
  Initialize the clonal tree randomly
\item
  Marginalize the cell attachment matrix
\item
  Keep equivalent solutions and return all of them
\item
  Check indistinguishable event and remove them
\item
  Estimate error rates of MCMC moves
\item
  Show results
\end{enumerate}

\includegraphics[width=0.6\textwidth,height=\textheight]{./resources/Inference_tab2.png}

The user must insert at least one false positive rate and one false
negative rate value for each sample. By double clicking on any cell
belonging to the row ``add row'', the user can insert a new set of false
or positive rates for each sample. During the inferential step, the
maximization of the likelihood for each set of rates is performed. The
best results are returned.

\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/Inference_tab1.png}

Press ``Run LACE'' to perform all computation and estimation steps and
visualize the results.


\subsection{Longitudinal display and outputs
interface}\label{longitudinal-display-and-outputs-interface}

The longitudinal display tab shows the longitudinal clonal tree, the
fishplot and the clonal tree, together with the best false positive and
negative rate parameters.

The longitudinal clonal tree is placed on the top left side. Continuous
edges are used to represent parental relations between clones, while
dashed edges show persistence relations. The size of the nodes is
proportional to the clonal prevalence. Moving the mouse over a node, it
is possible to see the clonal prevalence value. While moving the mouse
over the mutation labels, it shows the complete set of mutations for
that clone under the longitudinal clonal tree.

The fishplot is on the top right side of the tab. Passing the mouse over
the ribbons, the clonal prevalences of the clones at different time
points are displayed. Similarly, with the mouse over the time tags, the
clonal prevalences of all clones at the specified time point are
visualized.

By clicking on a mutation label of the longitudinal clonal tree as well
as on a fishplot ribbon or a clonal tree node, the details of the
mutation characterizing the clone can be queried and retrieved from the
\emph{Ensembl} database.

The bottom part of the tab is used to display the tree legend and the
best estimated set false positive/negative rates.

\includegraphics[width=0.95\textwidth,height=\textheight]{./resources/Display_tab.png}
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