Working on preface chapter

docs/index.html

@@ -273,7 +273,6 @@ <h1 class="quarto-secondary-nav-title">The moiraine R package user manual</h1>
   <ul class="collapse">
 <li><a href="#the-moiraine-package" id="toc-the-moiraine-package" class="nav-link" data-scroll-target="#the-moiraine-package">The moiraine package</a></li>
   <li><a href="#about-this-manual" id="toc-about-this-manual" class="nav-link" data-scroll-target="#about-this-manual">About this manual</a></li>
-  <li><a href="#section" id="toc-section" class="nav-link" data-scroll-target="#section"></a></li>
   </ul>
 </li>
   </ul></nav>
@@ -300,18 +299,15 @@ <h1 class="title d-none d-lg-block">The moiraine R package user manual</h1>
 
 
 </header><section id="preface" class="level1 unnumbered"><h1 class="unnumbered">Preface</h1>
-<p>Quick blurb around multi-omics integration. There are many tools available to perform multi-omics integration, and a lot are implemented as R packages. These tools differ conceptually (in terms of required data input, assumptions, questions they answer) but also at a practical level in terms of input data format, parameters, etc and output format. That makes it time-consuming to apply different tools to a same multi-omics dataset, and to compare the results.</p>
 <section id="the-moiraine-package" class="level2"><h2 class="anchored" data-anchor-id="the-moiraine-package">The moiraine package</h2>
-<p>The moiraine package aims at alleviating this by providing a framework to easily and consistently apply different integration tools to a same dataset. It also facilitates the comparison of results with consistent formatting of integration output and visualisations.</p>
-<p>In addition, in an effort to make these computations reproducible, moiraine heavily relies on targets for the creating of reproducible pipelines.</p>
+<p>Omics datasets provide an overview of the content of cells for a specific molecular layer (e.g.&nbsp;transcriptome, proteome, metabolome). By integrating different omics datasets obtained on the same biological samples, we can gain a deeper understanding of the interactions between these molecular layers, and shed light on the regulations occurring both within and between layers. A number of statistical methods have been developed to extract such information from multi-omics datasets, and many have been implemented in software such as R packages. However, these tools differ conceptually, in terms of the input data they require, the assumptions they make, the statistical approaches they use or even the questions they answers. They also differ at a practical level in terms of the format required for data input, the parameters to tune or select, and the format in which the results are returned. These differences render the <strong>application of several integration tools to a multi-omics dataset</strong> and the <strong>comparison of their results</strong> complex and time-consuming.</p>
+<p>The <code>moiraine</code> package aims at alleviating these issues, by providing a framework to easily and consistently apply different integration tools to a same multi-omics dataset. It implements numerous visualisation and reporting functions to facilitate the interpretation of the integration results, and facilitates the comparison of these results across integration methods. In addition, in an effort to make these computations reproducible, <code>moiraine</code> heavily relies on the <a href="https://books.ropensci.org/targets/"><code>targets</code> package</a> for the construction of reproducible analysis pipelines.</p>
 </section><section id="about-this-manual" class="level2"><h2 class="anchored" data-anchor-id="about-this-manual">About this manual</h2>
-<p>In this manual, we are showcasing the functionalities of the moiraine package by presenting an in-depth walk-through example of multi-omics integration analysis. This will not only talk about the how in terms of R functions etc, but also talk about the integration methods and how to use them.</p>
+<p>In this manual, we are showcasing the functionalities of the <code>moiraine</code> package by presenting an in-depth walk-through example of a multi-omics integration analysis. This will not only talk about the how in terms of R functions etc, but also talk about the integration methods and how to use them.</p>
 <p>Here, say that we heavily recommend to be familiar with targets (teaching targets it out of the scope of this manual, and we refer to the excellent targets manual).</p>
 <div class="cell">
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://docs.ropensci.org/targets/">targets</a></span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<p>This is a Quarto book.</p>
-<p>To learn more about Quarto books visit <a href="https://quarto.org/docs/books" class="uri">https://quarto.org/docs/books</a>.</p>
 <div class="cell">
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fl">1</span> <span class="op">+</span> <span class="fl">1</span></span>
 <span><span class="co">#&gt; [1] 2</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -325,7 +321,6 @@ <h1 class="title d-none d-lg-block">The moiraine R package user manual</h1>
 <pre class="file file-chunk cell-code"><code>"something"</code></pre>
 </div>
 </div>
-</section><section id="section" class="level2"><h2 class="anchored" data-anchor-id="section"></h2>
 
 
 </section></section></main><!-- /main --><script id="quarto-html-after-body" type="application/javascript">

docs/search.json

@@ -4,21 +4,21 @@
     "href": "index.html",
     "title": "The moiraine R package user manual",
     "section": "",
-    "text": "Preface\nQuick blurb around multi-omics integration. There are many tools available to perform multi-omics integration, and a lot are implemented as R packages. These tools differ conceptually (in terms of required data input, assumptions, questions they answer) but also at a practical level in terms of input data format, parameters, etc and output format. That makes it time-consuming to apply different tools to a same multi-omics dataset, and to compare the results."
+    "text": "Preface"
   },
   {
     "objectID": "index.html#the-moiraine-package",
     "href": "index.html#the-moiraine-package",
     "title": "The moiraine R package user manual",
     "section": "The moiraine package",
-    "text": "The moiraine package\nThe moiraine package aims at alleviating this by providing a framework to easily and consistently apply different integration tools to a same dataset. It also facilitates the comparison of results with consistent formatting of integration output and visualisations.\nIn addition, in an effort to make these computations reproducible, moiraine heavily relies on targets for the creating of reproducible pipelines."
+    "text": "The moiraine package\nOmics datasets provide an overview of the content of cells for a specific molecular layer (e.g. transcriptome, proteome, metabolome). By integrating different omics datasets obtained on the same biological samples, we can gain a deeper understanding of the interactions between these molecular layers, and shed light on the regulations occurring both within and between layers. A number of statistical methods have been developed to extract such information from multi-omics datasets, and many have been implemented in software such as R packages. However, these tools differ conceptually, in terms of the input data they require, the assumptions they make, the statistical approaches they use or even the questions they answers. They also differ at a practical level in terms of the format required for data input, the parameters to tune or select, and the format in which the results are returned. These differences render the application of several integration tools to a multi-omics dataset and the comparison of their results complex and time-consuming.\nThe moiraine package aims at alleviating these issues, by providing a framework to easily and consistently apply different integration tools to a same multi-omics dataset. It implements numerous visualisation and reporting functions to facilitate the interpretation of the integration results, and facilitates the comparison of these results across integration methods. In addition, in an effort to make these computations reproducible, moiraine heavily relies on the targets package for the construction of reproducible analysis pipelines."
   },
   {
     "objectID": "index.html#about-this-manual",
     "href": "index.html#about-this-manual",
     "title": "The moiraine R package user manual",
     "section": "About this manual",
-    "text": "About this manual\nIn this manual, we are showcasing the functionalities of the moiraine package by presenting an in-depth walk-through example of multi-omics integration analysis. This will not only talk about the how in terms of R functions etc, but also talk about the integration methods and how to use them.\nHere, say that we heavily recommend to be familiar with targets (teaching targets it out of the scope of this manual, and we refer to the excellent targets manual).\n\nlibrary(targets)\n\nThis is a Quarto book.\nTo learn more about Quarto books visit https://quarto.org/docs/books.\n\n1 + 1\n#> [1] 2\n\nA targets chunck:\n\ntar_target(a, 2+2)\n\n\n\n\"something\""
+    "text": "About this manual\nIn this manual, we are showcasing the functionalities of the moiraine package by presenting an in-depth walk-through example of a multi-omics integration analysis. This will not only talk about the how in terms of R functions etc, but also talk about the integration methods and how to use them.\nHere, say that we heavily recommend to be familiar with targets (teaching targets it out of the scope of this manual, and we refer to the excellent targets manual).\n\nlibrary(targets)\n\n\n1 + 1\n#> [1] 2\n\nA targets chunck:\n\ntar_target(a, 2+2)\n\n\n\n\"something\""
   },
   {
     "objectID": "intro.html",

index.qmd

@@ -1,16 +1,15 @@
 # Preface {.unnumbered}
 
-Quick blurb around multi-omics integration. There are many tools available to perform multi-omics integration, and a lot are implemented as R packages. These tools differ conceptually (in terms of required data input, assumptions, questions they answer) but also at a practical level in terms of input data format, parameters, etc and output format. That makes it time-consuming to apply different tools to a same multi-omics dataset, and to compare the results.
 
 ## The moiraine package
 
-The moiraine package aims at alleviating this by providing a framework to easily and consistently apply different integration tools to a same dataset. It also facilitates the comparison of results with consistent formatting of integration output and visualisations.
+Omics datasets provide an overview of the content of cells for a specific molecular layer (e.g. transcriptome, proteome, metabolome). By integrating different omics datasets obtained on the same biological samples, we can gain a deeper understanding of the interactions between these molecular layers, and shed light on the regulations occurring both within and between layers. A number of statistical methods have been developed to extract such information from multi-omics datasets, and many have been implemented in software such as R packages. However, these tools differ conceptually, in terms of the input data they require, the assumptions they make, the statistical approaches they use or even the questions they answers. They also differ at a practical level in terms of the format required for data input, the parameters to tune or select, and the format in which the results are returned. These differences render the **application of several integration tools to a multi-omics dataset** and the **comparison of their results** complex and time-consuming.
 
-In addition, in an effort to make these computations reproducible, moiraine heavily relies on targets for the creating of reproducible pipelines.
+The `moiraine` package aims at alleviating these issues, by providing **a framework to easily and consistently apply different integration tools to a same multi-omics dataset**. It implements numerous visualisation and reporting functions to **facilitate the interpretation of the integration results** as well as the comparison of these results across integration methods. In addition, in an effort to make these computations reproducible, `moiraine` heavily relies on the [`targets` package](https://books.ropensci.org/targets/) for the **construction of reproducible analysis pipelines**.
 
 ## About this manual
 
-In this manual, we are showcasing the functionalities of the moiraine package by presenting an in-depth walk-through example of multi-omics integration analysis. This will not only talk about the how in terms of R functions etc, but also talk about the integration methods and how to use them.
+In this manual, we are showcasing the functionalities of the `moiraine` package by presenting an in-depth walk-through example of a multi-omics integration analysis. This will not only talk about the how in terms of R functions etc, but also talk about the integration methods and how to use them.
 
 Here, say that we heavily recommend to be familiar with targets (teaching targets it out of the scope of this manual, and we refer to the excellent targets manual).
 
@@ -22,10 +21,6 @@ Here, say that we heavily recommend to be familiar with targets (teaching target
 library(targets)
 ```
 
-This is a Quarto book.
-
-To learn more about Quarto books visit <https://quarto.org/docs/books>.
-
 ```{r}
 1 + 1
 ```
@@ -40,4 +35,4 @@ tar_target(a, 2+2)
 "something"
 ```
 
-## 
+