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\setlength{\emergencystretch}{3em}
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\usepackage{lipsum}
\usepackage[unicode=true]{hyperref}
\usepackage{longtable}



\usepackage{lipsum}

\title{ProcMod Variance partitionning of vectorial data.}

\author[1]{Christelle Melodelima}

\corrauthor[1]{Christelle Melodelima}{\href{mailto:christelle.melodelima@metabarcoding.org}{\nolinkurl{christelle.melodelima@metabarcoding.org}}}
\author[1]{Eric Coissac}


\affil[1]{Cold Spring Harbor Laboratory, One Bungtown Road Cold Spring Harbor, NY
11724}
\affil[2]{Down House, Luxted Rd, Downe, Orpington BR6 7JT, UK}


%
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% \author[2]{Second Author}
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% \affil[2]{Address of second author}
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\begin{abstract}
With new high-throughput technologies, especially next generation
sequencing platforms, ecologists are more and more faced with analysis
of data sets in which a reasonable number of individuals or sites are
characterized by several hundreds or even thousands of variables.
Classical data analysis procedures are usually failing on such
high-dimensional data sets and new techniques have to be envisaged to
treat them. Metabarcoding is probably one of such new methodology
providing such kind of data set. ProcMod is an R packages generalizing
Procrustean analysis allows for partitioning variance of a
multi-dimensional data set to be explained among several explanatory
multi-dimensional data tables.
% Dummy abstract text. Dummy abstract text. Dummy abstract text. Dummy abstract text. Dummy abstract text. Dummy abstract text. Dummy abstract text. Dummy abstract text. Dummy abstract text. Dummy abstract text. Dummy abstract text.
\end{abstract}

\begin{document}

\flushbottom
\maketitle
\thispagestyle{empty}

\section*{Introduction}\label{introduction}
\addcontentsline{toc}{section}{Introduction}

With new high-throughput technologies, especially next generation
sequencing platforms, ecologists are more and more faced with analysis
of data sets in which a reasonable number of individuals or sites are
characterized by several hundreds or even thousands of variables.
Classical data analysis procedures are usually failing on such
high-dimensional data sets and new techniques have to be envisaged to
treat them. Metabarcoding is probably one of such new methodology
providing such kind of data set. ProcMod is an R packages generalizing
Procrustean analysis allows for partitioning variance of a
multi-dimensional data set to be explained among several explanatory
multi-dimensional data tables.

\subsection*{Procrustean Analysis}\label{procrustean-analysis}
\addcontentsline{toc}{subsection}{Procrustean Analysis}

Procrustean analysis is aiming to compare to multi-dimentional data sets
\(X\) and \(Y\), both characterising \(n\) points through \(p\) and
\(q\) variables.

\subsection*{About PeerJ}\label{about-peerj}
\addcontentsline{toc}{subsection}{About PeerJ}

PeerJ is an award-winning open access publisher covering the biological
and medical sciences. PeerJ provides authors with three publication
venues: \emph{PeerJ} and \emph{PeerJ Computer Science} (peer-reviewed
academic journals) and \emph{PeerJ PrePrints} (a `pre-print server').
See \url{https://peerj.com/about/publications/} for more information.

The PeerJ model allows an author to publish articles in their
peer-reviewed journal via the purchase of a lifetime Publication Plan.
Prices start from just \$99 (a one-off payment) which entitles an author
to the lifetime ability to publish 1 article per year for free.
Publication in PeerJ PrePrints is entirely free.

\section*{\texorpdfstring{Some \LaTeX{}
Examples}{Some  Examples}}\label{some-examples}
\addcontentsline{toc}{section}{Some \LaTeX{} Examples}

Use section and subsection commands to organize your document. \LaTeX{}
handles all the formatting and numbering automatically. Use ref and
label commands for cross-references.

\subsection*{Figures and Tables}\label{figures-and-tables}
\addcontentsline{toc}{subsection}{Figures and Tables}

Use the table and tabular commands for basic tables --- see Table
@ref(tab:widgets), for example. You can upload a figure (JPEG, PNG or
PDF) using the project menu. To include it in your document, use the
includegraphics command as in the code for Figure @ref(fig:view) below.

Standard \LaTeX references will work as well (e.g.~Fig. \ref{fig:view}).

\begin{figure}
\includegraphics[width=1\linewidth]{view} \caption{An example image.}\label{fig:view}
\end{figure}

\begin{longtable}[]{@{}lr@{}}
\caption{(\#tab:widgets) An Example Table.}\tabularnewline
\toprule
Item & Quantity\tabularnewline
\midrule
\endfirsthead
\toprule
Item & Quantity\tabularnewline
\midrule
\endhead
Widgets & 42\tabularnewline
Gadgets & 13\tabularnewline
\bottomrule
\end{longtable}

\subsection*{Citations}\label{citations}
\addcontentsline{toc}{subsection}{Citations}

LaTeX formats citations and references automatically using the
bibliography records in your .bib file, which you can edit via the
project menu. Use the cite command for an inline citation, like
Figueredo and Wolf (2009), and the citep command for a citation in
parentheses (Figueredo and Wolf 2009).

\subsection*{Mathematics}\label{mathematics}
\addcontentsline{toc}{subsection}{Mathematics}

\LaTeX{} is great at typesetting mathematics. Let
\(X_1, X_2, \ldots, X_n\) be a sequence of independent and identically
distributed random variables with \(\text{E}[X_i] = \mu\) and
\(\text{Var}[X_i] = \sigma^2 < \infty\), and let
\[S_n = \frac{X_1 + X_2 + \cdots + X_n}{n}
      = \frac{1}{n}\sum_{i}^{n} X_i\] denote their mean. Then as \(n\)
approaches infinity, the random variables \(\sqrt{n}(S_n - \mu)\)
converge in distribution to a normal \(\mathcal{N}(0, \sigma^2)\).

\subsection*{Lists}\label{lists}
\addcontentsline{toc}{subsection}{Lists}

You can make lists with automatic numbering \dots

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Like this,
\item
  and like this.
\end{enumerate}

or bullet points\ldots{}

\begin{itemize}
\tightlist
\item
  Like this,
\item
  and like this.
\end{itemize}

or with descriptions\ldots{}

\begin{itemize}
\tightlist
\item
  \textbf{Word} Definition
\item
  \textbf{Concept} Explanation
\item
  \textbf{Idea} Text
\end{itemize}

We hope you find write\LaTeX~useful for your PeerJ submission, and
please let us know if you have any feedback. Further examples with dummy
text are included in the following pages.

\section*{Methods}\label{methods}
\addcontentsline{toc}{section}{Methods}

\lipsum[4] 

\begin{equation}
\cos^3 \theta =\frac{1}{4}\cos\theta+\frac{3}{4}\cos 3\theta
\label{eq:refname2}
\end{equation}

\lipsum[5] 

\subsection*{Subsection}\label{subsection}
\addcontentsline{toc}{subsection}{Subsection}

\lipsum[6] 

\paragraph{Paragraph}

\lipsum[7]  \paragraph{Paragraph} \lipsum[8] 

\subsection*{Subsection}\label{subsection-1}
\addcontentsline{toc}{subsection}{Subsection}

\lipsum[9] 

\begin{figure}
\includegraphics[width=1\linewidth]{ProcMod_files/figure-latex/results-1} \caption{In-text Picture}\label{fig:results}
\end{figure}

Reference to Figure @ref(fig:results).

\section*{Results and Discussion}\label{results-and-discussion}
\addcontentsline{toc}{section}{Results and Discussion}

\lipsum[10] 

\subsection*{Subsection}\label{subsection-2}
\addcontentsline{toc}{subsection}{Subsection}

\lipsum[11] 

\subsubsection*{Subsubsection}\label{subsubsection}
\addcontentsline{toc}{subsubsection}{Subsubsection}

\lipsum[12] 

\subsubsection*{Subsubsection}\label{subsubsection-1}
\addcontentsline{toc}{subsubsection}{Subsubsection}

\lipsum[14] 

\subsection*{Subsection}\label{subsection-3}
\addcontentsline{toc}{subsection}{Subsection}

\lipsum[15-20] 

\section*{Acknowledgments}\label{acknowledgments}
\addcontentsline{toc}{section}{Acknowledgments}

So long and thanks for all the fish.

\section*{References}\label{references}
\addcontentsline{toc}{section}{References}

\hypertarget{refs}{}
\hypertarget{ref-Figueredo:2009dg}{}
Figueredo, Aurelio José, and Pedro S. A. Wolf. 2009. ``Assortative
Pairing and Life History Strategy.'' \emph{Human Nature} 20 (3).
Springer Nature: 317--30.
doi:\href{https://doi.org/10.1007/s12110-009-9068-2}{10.1007/s12110-009-9068-2}.



\end{document}
