Deploying to gh-pages from @ fbc209a 🚀

docs/devel/index.html

@@ -554,7 +554,7 @@ <h1 class="title">Orchestrating Microbiome Analysis</h1>
 <div class="quarto-layout-panel" data-layout-ncol="2">
 <div class="quarto-layout-row">
 <div class="quarto-layout-cell" style="flex-basis: 50.0%;justify-content: flex-start;">
-<p><strong>Package:</strong> OMA<br><strong>Authors:<br></strong> - Leo Lahti [aut]<br> - Tuomas Borman [aut, cre]<br> - Felix GM Ernst [aut]<br> - and others (see the full list of contributors) [ctb]<br><strong>Compiled:</strong> 2024-08-10<br><strong>Package version:</strong> 0.98.25<br><strong>R version:</strong> R version 4.4.1 (2024-06-14)<br><strong>BioC version:</strong> 3.20<br><strong>License:</strong> CC BY-NC-SA 4.0<br></p>
+<p><strong>Package:</strong> OMA<br><strong>Authors:<br></strong> - Leo Lahti [aut]<br> - Tuomas Borman [aut, cre]<br> - Felix GM Ernst [aut]<br> - and others (see the full list of contributors) [ctb]<br><strong>Compiled:</strong> 2024-08-30<br><strong>Package version:</strong> 0.98.25<br><strong>R version:</strong> R version 4.4.1 (2024-06-14)<br><strong>BioC version:</strong> 3.20<br><strong>License:</strong> CC BY-NC-SA 4.0<br></p>
 </div>
 <div class="quarto-layout-cell" style="flex-basis: 50.0%;justify-content: flex-start;">
 <p><img src="assets/cover.png" align="middle" style="max-width:100%;

docs/devel/pages/acknowledgments.html

@@ -632,6 +632,15 @@ <h1 class="title">
 <li><p><em>Jacques Serizay</em> converted the <em>OMA</em> book to the <code>BiocBook</code> format. This allows the <em>OMA</em> book to be built and distributed by Bioconductor.</p></li>
 <li><p><em>Himel Mallick, PhD, FASA</em>, principal investigator and tenure-track faculty at Cornell University’s Department of Population Health Sciences and an adjunct faculty of Statistics and Data Science at Bowers College of Computing and Information Science. He contributed to the chapters on meta-analyses, microbe set enrichment analysis (MSEA) and multi-omics prediction and classification.</p></li>
 <li><p><em>Yihan Liu</em>, assisted Dr.&nbsp;Mallick in writing the chapters on meta-anlayses, MSEA and multi-omics prediction and classification.</p></li>
+<li><p><em>Jennifer Wokaty</em></p></li>
+<li><p><em>Danielle Callan</em></p></li>
+<li><p><em>Katariina Pärnänen</em></p></li>
+<li><p><em>Yu Gao 高煜</em></p></li>
+<li><p><em>Ben Allen</em></p></li>
+<li><p><em>Teo Dallier</em></p></li>
+<li><p><em>Elliot Gaudron-Parry</em></p></li>
+<li><p><em>Inès Benseddik</em></p></li>
+<li><p><em>Jesse Pasanen</em></p></li>
 </ul></section><section id="acknowledgments" class="level3 unnumbered page-columns page-full"><h3 class="unnumbered anchored" data-anchor-id="acknowledgments">Acknowledgments</h3>
 <p>This work would not have been possible without the countless contributions and interactions with other researchers, developers, and users. We express our gratitude to the entire Bioconductor community for developing this high-quality open research software repository for life science analytics, continuously pushing the limits in emerging fields <span class="citation" data-cites="Gentleman2004">(<a href="#ref-Gentleman2004" role="doc-biblioref">Gentleman et al. 2004</a>)</span>, <span class="citation" data-cites="Huber2015">(<a href="#ref-Huber2015" role="doc-biblioref">Huber et al. 2015</a>)</span>.</p>
 <div class="no-row-height column-margin column-container"><div id="ref-Gentleman2004" class="csl-entry" role="listitem">

docs/devel/pages/alpha_diversity.html

@@ -643,11 +643,11 @@ <h1 class="title"><span id="sec-community-diversity" class="quarto-section-ident
 <span class="header-section-number">12.1</span> Alpha diversity estimation in practice</h2>
 <section id="sec-estimate-diversity" class="level3 page-columns page-full" data-number="12.1.1"><h3 data-number="12.1.1" class="anchored" data-anchor-id="sec-estimate-diversity">
 <span class="header-section-number">12.1.1</span> Calculate diversity measures</h3>
-<p>Alpha diversity can be estimated with <code><a href="https://rdrr.io/pkg/mia/man/addAlpha.html">addAlpha()</a></code> wrapper function that interact with other packages implementing the calculation, such as <em><code>vegan</code></em> <span class="citation" data-cites="R_vegan">(<a href="#ref-R_vegan" role="doc-biblioref">Oksanen et al. 2020</a>)</span>.</p>
+<p>Alpha diversity can be estimated with <code><a href="https://rdrr.io/pkg/mia/man/addAlpha.html">addAlpha()</a></code> wrapper function that interact with other packages implementing the calculation, such as <code>vegan</code> <span class="citation" data-cites="R_vegan">(<a href="#ref-R_vegan" role="doc-biblioref">Oksanen et al. 2020</a>)</span>.</p>
 <div class="no-row-height column-margin column-container"><div id="ref-R_vegan" class="csl-entry" role="listitem">
 Oksanen, Jari, F. Guillaume Blanchet, Michael Friendly, Roeland Kindt, Pierre Legendre, Dan McGlinn, Peter R. Minchin, et al. 2020. <em>Vegan: Community Ecology Package</em>. <a href="https://CRAN.R-project.org/package=vegan">https://CRAN.R-project.org/package=vegan</a>.
 </div></div><p>These functions calculate the given indices, and add them to the <code>colData</code> slot of the <code>SummarizedExperiment</code> object with the given <code>name</code>.</p>
-<p>The estimated values can then be retrieved and analyzed directly from the <code>colData</code>, for example, by plotting them using <code><a href="https://rdrr.io/pkg/scater/man/plotColData.html">plotColData()</a></code> from the <em><code>scater</code></em> package <span class="citation" data-cites="R_scater">(<a href="#ref-R_scater" role="doc-biblioref">McCarthy et al. 2020</a>)</span>. Here, we use the <code>observed</code> species as a measure of richness.</p>
+<p>The estimated values can then be retrieved and analyzed directly from the <code>colData</code>, for example, by plotting them using <code><a href="https://rdrr.io/pkg/scater/man/plotColData.html">plotColData()</a></code> from the <code>scater</code> package <span class="citation" data-cites="R_scater">(<a href="#ref-R_scater" role="doc-biblioref">McCarthy et al. 2020</a>)</span>. Here, we use the <code>observed</code> species as a measure of richness.</p>
 <div class="no-row-height column-margin column-container"><div id="ref-R_scater" class="csl-entry" role="listitem">
 McCarthy, Davis, Kieran Campbell, Aaron Lun, and Quin Wills. 2020. <em>Scater: Single-Cell Analysis Toolkit for Gene Expression Data in r</em>. <a href="http://bioconductor.org/packages/scater/">http://bioconductor.org/packages/scater/</a>.
 </div></div><p>Certain indices have additional options, here observed has <code>detection</code> parameter that control the detection threshold. Species over this threshold is considered as detected. See full list of options from from <code><a href="https://rdrr.io/pkg/mia/man/addAlpha.html">help(addAlpha)</a></code>.</p>

docs/devel/pages/beta_diversity.html

@@ -773,23 +773,23 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Run NMDS on relabundance assay with Bray-Curtis distances</span></span>
 <span><span class="va">tse</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mia/man/runNMDS.html">runNMDS</a></span><span class="op">(</span></span>
 <span>    <span class="va">tse</span>,</span>
-<span>    FUN <span class="op">=</span> <span class="fu">vegan</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/vegan/man/vegdist.html">vegdist</a></span>,</span>
+<span>    FUN <span class="op">=</span> <span class="va">getDissimilarity</span>,</span>
 <span>    method <span class="op">=</span> <span class="st">"bray"</span>,</span>
 <span>    assay.type <span class="op">=</span> <span class="st">"relabundance"</span>,</span>
 <span>    name <span class="op">=</span> <span class="st">"NMDS_bray"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Run MDS on clr assay with Aitchison distances</span></span>
 <span><span class="va">tse</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/scater/man/runMDS.html">runMDS</a></span><span class="op">(</span></span>
 <span>    <span class="va">tse</span>,</span>
-<span>    FUN <span class="op">=</span> <span class="fu">vegan</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/vegan/man/vegdist.html">vegdist</a></span>,</span>
+<span>    FUN <span class="op">=</span> <span class="va">getDissimilarity</span>,</span>
 <span>    method <span class="op">=</span> <span class="st">"euclidean"</span>,</span>
 <span>    assay.type <span class="op">=</span> <span class="st">"clr"</span>,</span>
 <span>    name <span class="op">=</span> <span class="st">"MDS_aitchison"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Run NMDS on clr assay with Euclidean distances</span></span>
 <span><span class="va">tse</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mia/man/runNMDS.html">runNMDS</a></span><span class="op">(</span></span>
 <span>    <span class="va">tse</span>,</span>
-<span>    FUN <span class="op">=</span> <span class="fu">vegan</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/vegan/man/vegdist.html">vegdist</a></span>,</span>
+<span>    FUN <span class="op">=</span> <span class="va">getDissimilarity</span>,</span>
 <span>    method <span class="op">=</span> <span class="st">"euclidean"</span>,</span>
 <span>    assay.type <span class="op">=</span> <span class="st">"clr"</span>,</span>
 <span>    name <span class="op">=</span> <span class="st">"NMDS_aitchison"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -816,11 +816,12 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 </div>
 </div>
 </div>
-<p>The <em>Unifrac</em> method is a special case, as it requires data on the relationship of features in the form of a <code>phylo</code> tree. <code><a href="https://rdrr.io/pkg/mia/man/calculateUnifrac.html">calculateUnifrac()</a></code> performs the calculation to return a <code>dist</code> object, which can again be used within <code><a href="https://rdrr.io/pkg/scater/man/runMDS.html">runMDS()</a></code>.</p>
+<p>The <em>Unifrac</em> method is a special case, as it requires data on the relationship of features in the form of a <code>phylo</code> tree. <code><a href="https://rdrr.io/pkg/mia/man/getDissimilarity.html">getDissimilarity()</a></code> performs the calculation to return a <code>dist</code> object, which can again be used within <code><a href="https://rdrr.io/pkg/scater/man/runMDS.html">runMDS()</a></code>.</p>
 <div class="cell" data-layout-align="center">
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">tse</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/scater/man/runMDS.html">runMDS</a></span><span class="op">(</span></span>
 <span>    <span class="va">tse</span>,</span>
-<span>    FUN <span class="op">=</span> <span class="fu">mia</span><span class="fu">::</span><span class="va"><a href="https://rdrr.io/pkg/mia/man/calculateUnifrac.html">calculateUnifrac</a></span>,</span>
+<span>    FUN <span class="op">=</span> <span class="va">getDissimilarity</span>,</span>
+<span>    method <span class="op">=</span> <span class="st">"unifrac"</span>,</span>
 <span>    name <span class="op">=</span> <span class="st">"Unifrac"</span>,</span>
 <span>    tree <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/TreeSummarizedExperiment/man/TreeSummarizedExperiment-accessor.html">rowTree</a></span><span class="op">(</span><span class="va">tse</span><span class="op">)</span>,</span>
 <span>    ntop <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/nrow.html">nrow</a></span><span class="op">(</span><span class="va">tse</span><span class="op">)</span>,</span>
@@ -1129,7 +1130,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">6</td>
 <td style="text-align: right;">1.1157</td>
 <td style="text-align: right;">1.940</td>
-<td style="text-align: right;">0.046</td>
+<td style="text-align: right;">0.032</td>
 <td style="text-align: right;">3.991</td>
 <td style="text-align: right;">0.2795</td>
 </tr>
@@ -1138,7 +1139,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">4</td>
 <td style="text-align: right;">0.5837</td>
 <td style="text-align: right;">1.522</td>
-<td style="text-align: right;">0.136</td>
+<td style="text-align: right;">0.131</td>
 <td style="text-align: right;">3.991</td>
 <td style="text-align: right;">0.1463</td>
 </tr>
@@ -1147,7 +1148,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">1</td>
 <td style="text-align: right;">0.1679</td>
 <td style="text-align: right;">1.751</td>
-<td style="text-align: right;">0.106</td>
+<td style="text-align: right;">0.098</td>
 <td style="text-align: right;">3.991</td>
 <td style="text-align: right;">0.0421</td>
 </tr>
@@ -1276,7 +1277,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">0.0628</td>
 <td style="text-align: right;">2.7440</td>
 <td style="text-align: right;">999</td>
-<td style="text-align: right;">0.108</td>
+<td style="text-align: right;">0.107</td>
 <td style="text-align: right;">1.0288</td>
 <td style="text-align: right;">0.2440</td>
 </tr>
@@ -1287,7 +1288,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">0.0103</td>
 <td style="text-align: right;">0.4158</td>
 <td style="text-align: right;">999</td>
-<td style="text-align: right;">0.543</td>
+<td style="text-align: right;">0.490</td>
 <td style="text-align: right;">0.9283</td>
 <td style="text-align: right;">0.0111</td>
 </tr>
@@ -1298,7 +1299,7 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <td style="text-align: right;">0.0113</td>
 <td style="text-align: right;">17.0255</td>
 <td style="text-align: right;">999</td>
-<td style="text-align: right;">0.409</td>
+<td style="text-align: right;">0.404</td>
 <td style="text-align: right;">0.3319</td>
 <td style="text-align: right;">0.9860</td>
 </tr>
@@ -1458,13 +1459,13 @@ <h1 class="title"><span id="sec-community-similarity" class="quarto-section-iden
 <li>dbRDA: <code><a href="https://rdrr.io/pkg/mia/man/runCCA.html">getRDA()</a></code> and <code>runRDA(0)</code>; our recommended default method to assess differences in community composition (beta diversity)</li>
 <li>Double Principal Coordinate Analysis (DPCoA): <code><a href="https://rdrr.io/pkg/mia/man/runDPCoA.html">getDPCoA()</a></code> and <code><a href="https://rdrr.io/pkg/mia/man/runDPCoA.html">runDPCoA()</a></code>
 </li>
-<li>Jensen-Shannon Divergence (JSD): <code><a href="https://rdrr.io/pkg/mia/man/calculateJSD.html">calculateJSD()</a></code> and <code><a href="https://rdrr.io/pkg/mia/man/calculateJSD.html">runJSD()</a></code>
+<li>Jensen-Shannon Divergence (JSD): <code><a href="https://rdrr.io/pkg/mia/man/deprecate.html">calculateJSD()</a></code> and <code><a href="https://rdrr.io/pkg/mia/man/deprecate.html">runJSD()</a></code>
 </li>
 <li>MDS: <code><a href="https://rdrr.io/pkg/scater/man/runMDS.html">calculateMDS()</a></code> and <code><a href="https://rdrr.io/pkg/scater/man/runMDS.html">runMDS()</a></code>
 </li>
 <li>NMDS: <code><a href="https://rdrr.io/pkg/mia/man/runNMDS.html">getNMDS()</a></code> and <code><a href="https://rdrr.io/pkg/mia/man/runNMDS.html">runNMDS()</a></code>
 </li>
-<li>Overlap: <code><a href="https://rdrr.io/pkg/mia/man/calculateOverlap.html">calculateOverlap()</a></code> and <code><a href="https://rdrr.io/pkg/mia/man/calculateOverlap.html">runOverlap()</a></code>
+<li>Overlap: <code><a href="https://rdrr.io/pkg/mia/man/deprecate.html">calculateOverlap()</a></code> and <code><a href="https://rdrr.io/pkg/mia/man/deprecate.html">runOverlap()</a></code>
 </li>
 <li>PERMANOVA: (e.g.&nbsp;from <code><a href="https://rdrr.io/pkg/vegan/man/adonis.html">vegan::adonis2()</a></code>) can be used to assess significance when comparing community composition between groups. Retrieving the loadings and components is more tricky, however.</li>
 <li>t-distributed Stochastic Neighbor Embedding (t-SNE): <code><a href="https://rdrr.io/pkg/scater/man/runTSNE.html">calculateTSNE()</a></code> and <code><a href="https://rdrr.io/pkg/scater/man/runTSNE.html">runTSNE()</a></code>

docs/devel/pages/clustering.html

@@ -609,7 +609,7 @@ <h1 class="title"><span id="sec-clustering" class="quarto-section-identifier"><s
 <section id="clustering" class="level2" data-number="14.1"><h2 data-number="14.1" class="anchored" data-anchor-id="clustering">
 <span class="header-section-number">14.1</span> Clustering</h2>
 <p>Clustering techniques aim to find groups, called clusters, that share a pattern in the data. In the microbiome context, clustering techniques are included in microbiome community typing methods. For example, clustering allow samples to be distinguished from each other based on their microbiome community composition. Clustering scheme consists of two steps, the first is to compute the sample dissimilarities with a given distance metrics, and the second is to form the clusters based on the dissimilarity matrix. The data can be clustered either based on features or samples. The examples below are focused on sample clustering.</p>
-<p>There are multiple clustering algorithms available. <code>*bluster*</code> is a Bioconductor package providing tools for clustering data in the <code>SummarizedExperiment</code> container. It offers multiple algorithms such as hierarchical clustering, DBSCAN, and K-means.</p>
+<p>There are multiple clustering algorithms available. <code>bluster</code> is a Bioconductor package providing tools for clustering data in the <code>SummarizedExperiment</code> container. It offers multiple algorithms such as hierarchical clustering, DBSCAN, and K-means.</p>
 <div class="cell" data-layout-align="center">
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Load dependencies</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">bluster</span><span class="op">)</span></span>