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docs/devel/pages/acknowledgments.html

@@ -585,15 +585,15 @@ <h1 class="title">
 <section id="core-team" class="level3 unnumbered"><h3 class="unnumbered anchored" data-anchor-id="core-team">Core team</h3>
 <p>Contributions to this Gitbook from the various developers are coordinated by:</p>
 <ul>
-<li><p><em>Leo Lahti, DSc</em>, professor in Data Science in the <a href="https://datascience.utu.fi/">Department of Computing, University of Turku, Finland</a>, with a focus on computational microbiome analysis. Lahti obtained a doctoral degree (DSc) from Aalto University in Finland (2010), developing probabilistic machine learning with applications to high-throughput life science data integration. Since then he has focused on microbiome research and developed, for instance, the <em>phyloseq</em>-based <a href="https://bioconductor.org/packages/release/bioc/html/microbiome.html">microbiome R package</a> before starting to develop the <em>TreeSummarizedExperiment</em> / <em>MultiAssayExperiment</em> framework and the mia family of Bioconductor packages for microbiome data science introduced in this Gitbook. Lahti led the development of <a href="https://avointiede.fi/en/policies-materials/policies-open-science-and-research-finland/policy-open-research-data-and-methods">national policy on open access to research methods in Finland</a>. He is a current member in the <a href="https://bioconductor.org/about/community-advisory-board/">Bioconductor Community Advisory Board</a> and runs regular training workshops in microbiome data science.</p></li>
+<li><p><em>Leo Lahti, DSc</em>, professor in Data Science in the <a href="https://datascience.utu.fi/">Department of Computing, University of Turku, Finland</a>, with a focus on computational microbiome analysis. Lahti obtained a doctoral degree (DSc) from Aalto University in Finland (2010), developing probabilistic machine learning with applications to high-throughput life science data integration. Since then, he has focused on microbiome research and developed, for instance, the <em>phyloseq</em>-based <a href="https://bioconductor.org/packages/release/bioc/html/microbiome.html">microbiome R package</a> before starting to develop the <em>TreeSummarizedExperiment</em> / <em>MultiAssayExperiment</em> framework and the mia family of Bioconductor packages for microbiome data science introduced in this Gitbook. Lahti led the development of <a href="https://avointiede.fi/en/policies-materials/policies-open-science-and-research-finland/policy-open-research-data-and-methods">national policy on open access to research methods in Finland</a>. He is a current member in the [Bioconductor Community Advisory] (https://bioconductor.org/about/community-advisory-board/) and runs regular training workshops in microbiome data science.</p></li>
 <li><p><em>Tuomas Borman</em>, PhD researcher and the lead developer of OMA/mia at the Department of Computing, University of Turku.</p></li>
 </ul></section><section id="contributors" class="level3 unnumbered"><h3 class="unnumbered anchored" data-anchor-id="contributors">Contributors</h3>
 <p>This work is a remarkably collaborative effort. The full list of contributors is available via <a href="https://github.com/microbiome/OMA/graphs/contributors">Github</a>. Some key authors/contributors include:</p>
 <ul>
 <li><p><em>Felix Ernst, PhD</em>, among the first developers of R/Bioc methods for microbiome research based on the <em>SummarizedExperiment</em> class and its derivatives.</p></li>
-<li><p><em>Giulio Benedetti</em>, scientific programmer at the Department of Computing, University of Turku. His research interest is mostly related to Data Science. He has also helped to expand the SummarizedExperiment-based microbiome analysis framework to the Julia language, implementing <a href="https://github.com/JuliaTurkuDataScience/MicrobiomeAnalysis.jl">MicrobiomeAnalysis.jl</a>.</p></li>
+<li><p><em>Giulio Benedetti</em>, scientific programmer at the Department of Computing, University of Turku. His research interest is mostly related to Data Science. He has also helped to expand the SummarizedExperiment-based microbiome analysis framework to the Julia language, implementing Board](https://bioconductor.org/about/community-advisory-board/) <a href="https://github.com/JuliaTurkuDataScience/MicrobiomeAnalysis.jl">MicrobiomeAnalysis.jl</a>.</p></li>
 <li><p><em>Sudarshan Shetty, PhD</em> has supported the establishment of the framework and associated tools. He also maintains a list of <a href="https://microsud.github.io/Tools-Microbiome-Analysis/">microbiome R packages</a>.</p></li>
-<li><p><em>Henrik Eckermann</em>, in particular to the development of differential abundance analyses</p></li>
+<li><p><em>Henrik Eckermann</em> contributed in particular to the development of differential abundance analyses</p></li>
 <li><p><em>Chouaib Benchraka</em> provided various contributions to the package ecosystem and the OMA book</p></li>
 <li><p><em>Yağmur Şimşek</em> converted the miaSim R package to support the Bioconductor framework</p></li>
 <li><p><em>Basil Courbayre</em> provided various contributions to the package ecosystem and the OMA book, in particular on unsupervised machine learning</p></li>

docs/devel/pages/agglomeration.html

@@ -606,7 +606,7 @@ <h1 class="title"><span id="sec-agglomeration" class="quarto-section-identifier"
 <p>In this chapter, we discuss agglomeration, which involves summing data within specific groups. For example, we can agglomerate data to the phylum taxonomy level. This process begins by identifying which phyla are present in the data. Subsequently, we group the data according to these phyla and aggregate the counts. The resulting dataset will have features corresponding to each phylum, with counts aggregated from the lower-level taxa associated with them.</p>
 <section id="sec-data-agglomeration" class="level2" data-number="9.1"><h2 data-number="9.1" class="anchored" data-anchor-id="sec-data-agglomeration">
 <span class="header-section-number">9.1</span> Agglomerate data to certain rank</h2>
-<p>One of the main applications of taxonomic information in regards to count data is to agglomerate count data on taxonomic levels and track the influence of changing conditions through these levels. For this <code>mia</code> contains the <code>agglomerateByRank</code> function. The ideal location to store the agglomerated data is as an alternative experiment.</p>
+<p>One of the main applications of taxonomic information in regards to count data is to agglomerate count data on taxonomic levels and track the influence of changing conditions through these levels. For this <code>mia</code> contains the <code><a href="https://rdrr.io/pkg/mia/man/agglomerate-methods.html">agglomerateByRank()</a></code> function. The ideal location to store the agglomerated data is as an alternative experiment.</p>
 <div class="cell" data-layout-align="center">
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Tranform data</span></span>
 <span><span class="va">tse</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mia/man/transformAssay.html">transformAssay</a></span><span class="op">(</span><span class="va">tse</span>, assay.type <span class="op">=</span> <span class="st">"counts"</span>, method <span class="op">=</span> <span class="st">"relabundance"</span><span class="op">)</span></span>
@@ -662,7 +662,7 @@ <h1 class="title"><span id="sec-agglomeration" class="quarto-section-identifier"
 <span><span class="co">##  ACK-M1   51  64 150      64      97      31     145</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p><code>altExpNames</code> now consists of <code>Family</code> level data. This can be extended to use any taxonomic level listed in <code>taxonomyRanks(tse)</code>.</p>
-<p>We can also aggregate the data across all available ranks in one step using <code>agglomerateByRanks</code>. The function returns <code>TreeSE</code> including agglomerated objects in <code>altExp</code> slot.</p>
+<p>We can also aggregate the data across all available ranks in one step using <code><a href="https://rdrr.io/pkg/mia/man/agglomerate-methods.html">agglomerateByRanks()</a></code>. The function returns <code>TreeSE</code> including agglomerated objects in <code>altExp</code> slot.</p>
 <div class="cell" data-layout-align="center">
 <div class="sourceCode" id="cb6"><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/mia/man/agglomerate-methods.html">agglomerateByRanks</a></span><span class="op">(</span><span class="va">tse</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/SingleCellExperiment/man/altExps.html">altExpNames</a></span><span class="op">(</span><span class="va">tse</span><span class="op">)</span></span>
@@ -709,7 +709,7 @@ <h1 class="title"><span id="sec-agglomeration" class="quarto-section-identifier"
 </div>
 </section></section><section id="agglomerate-based-on-prevalence" class="level2" data-number="9.2"><h2 data-number="9.2" class="anchored" data-anchor-id="agglomerate-based-on-prevalence">
 <span class="header-section-number">9.2</span> Agglomerate based on prevalence</h2>
-<p>Rare taxa can also be aggregated into a single group “Other” instead of filtering them out. A suitable function for this is <code>agglomerateByPrevalence</code>. The number of rare taxa is higher on the species level, which causes the need for data agglomeration by prevalence.</p>
+<p>Rare taxa can also be aggregated into a single group “Other” instead of filtering them out. A suitable function for this is <code><a href="https://rdrr.io/pkg/mia/man/agglomerateByPrevalence.html">agglomerateByPrevalence()</a></code>. The number of rare taxa is higher on the species level, which causes the need for data agglomeration by prevalence.</p>
 <div class="cell" data-layout-align="center">
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/SingleCellExperiment/man/altExps.html">altExp</a></span><span class="op">(</span><span class="va">tse</span>, <span class="st">"Species_byPrevalence"</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mia/man/agglomerateByPrevalence.html">agglomerateByPrevalence</a></span><span class="op">(</span></span>
 <span>    <span class="va">tse</span>,</span>
@@ -746,7 +746,7 @@ <h1 class="title"><span id="sec-agglomeration" class="quarto-section-identifier"
 </div>
 </section><section id="aggregate-data-based-on-variable" class="level2" data-number="9.3"><h2 data-number="9.3" class="anchored" data-anchor-id="aggregate-data-based-on-variable">
 <span class="header-section-number">9.3</span> Aggregate data based on variable</h2>
-<p><code>agglomerateByRank</code> aggregates the data taking into account the taxonomy information. For more flexible aggregations, there is available method <code>agglomerateByVariable</code>. For instance, we can aggregate the data by sample types.</p>
+<p><code><a href="https://rdrr.io/pkg/mia/man/agglomerate-methods.html">agglomerateByRank()</a></code> aggregates the data taking into account the taxonomy information. For more flexible aggregations, there is available method <code><a href="https://rdrr.io/pkg/mia/man/agglomerate-methods.html">agglomerateByVariable()</a></code>. For instance, we can aggregate the data by sample types.</p>
 <div class="cell" data-layout-align="center">
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Agglomerate samples based on type</span></span>
 <span><span class="va">tse_sub</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mia/man/agglomerate-methods.html">agglomerateByVariable</a></span><span class="op">(</span><span class="va">tse</span>, by <span class="op">=</span> <span class="st">"cols"</span>, f <span class="op">=</span> <span class="st">"SampleType"</span><span class="op">)</span></span>
@@ -770,7 +770,7 @@ <h1 class="title"><span id="sec-agglomeration" class="quarto-section-identifier"
 <p><a href="clustering.html#sec-taxa-clustering" class="quarto-xref"><span>Section 14.1.2</span></a> introduces how cluster information can be utilized to agglomerate data.</p>
 </section><section id="subset-based-on-prevalence" class="level2" data-number="9.4"><h2 data-number="9.4" class="anchored" data-anchor-id="subset-based-on-prevalence">
 <span class="header-section-number">9.4</span> Subset based on prevalence</h2>
-<p>In addition to agglomeration, we can subset the data based on prevalence. Using <code>subsetByPrevalent</code>, we can filter for taxa that exceed a specified prevalence threshold. Alternatively, <code>subsetByRare</code> allows us to filter for taxa that do not exceed the threshold.</p>
+<p>In addition to agglomeration, we can subset the data based on prevalence. Using <code><a href="https://rdrr.io/pkg/mia/man/getPrevalence.html">subsetByPrevalent()</a></code>, we can filter for taxa that exceed a specified prevalence threshold. Alternatively, <code><a href="https://rdrr.io/pkg/mia/man/getPrevalence.html">subsetByRare()</a></code> allows us to filter for taxa that do not exceed the threshold.</p>
 <div class="cell" data-layout-align="center">
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">tse_sub</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/mia/man/getPrevalence.html">subsetByRare</a></span><span class="op">(</span><span class="va">tse</span>, rank <span class="op">=</span> <span class="st">"Genus"</span>, detection <span class="op">=</span> <span class="fl">0.01</span>, prevalence <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span>
 <span><span class="va">tse_sub</span></span>