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Expand Down Expand Up @@ -355,7 +355,16 @@ <h1 class="title">Orchestrating Microbiome Analysis</h1>


</header><p><img src="assets/cover.png" title="Orchestrating Microbiome Analysis" class="quarto-cover-image img-fluid"></p>
<p><strong>Package:</strong> OMA<br><strong>Authors:</strong> Leo Lahti [aut], Tuomas Borman [aut, cre], Felix GM Ernst [aut], and others (see the full list of contributors) [ctb]<br><strong>Compiled:</strong> 2023-11-17<br><strong>Package version:</strong> 0.98.16<br><strong>R version:</strong> <b style="color:red;">R Under development (unstable) (2023-11-08 r85496)</b><br><strong>BioC version:</strong> <b style="color:red;">3.19</b><br><strong>License:</strong> CC BY-NC-SA 4.0<br></p>
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<p>{strong(‘Package’)}: {pkg} <strong>Authors:</strong> &lt;br/&gt; {format(authors, include = c(‘given’, ‘family’, ‘role’)) |&gt; lapply(tags<span class="math inline">\(li) |&amp;gt; tags\)</span>ul()}&lt;br/&gt;</p>
<p><strong>Compiled:</strong> {as.character(Sys.Date())}&lt;br/&gt;</p>
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<p><strong>BioC version:</strong> &lt;b style=‘color:red;’&gt;{BiocManager::version()}&lt;/b&gt;&lt;br/&gt;</p>
<strong>License:</strong> {license}&lt;br/&gt;
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<p>`{=html}</p>
<section id="welcome" class="level1 unnumbered page-columns page-full"><h1 class="unnumbered">Welcome</h1>
<p>You are reading the online book, <a href="https://microbiome.github.io/OMA/"><strong>Orchestrating Microbiome Analysis with Bioconductor</strong></a> <span class="citation" data-cites="OMA">(<a href="#ref-OMA" role="doc-biblioref">Lahti et al. 2021</a>)</span>, where we walk through common strategies and workflows in microbiome data science.</p>
<div class="no-row-height column-margin column-container"><div id="ref-OMA" class="csl-entry" role="listitem">
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<span><span class="co">## Hmisc 5.1-1 2023-09-12 [2] CRAN (R 4.4.0)</span></span>
<span><span class="co">## hms 1.1.3 2023-03-21 [2] CRAN (R 4.4.0)</span></span>
<span><span class="co">## htmlTable 2.4.2 2023-10-29 [2] CRAN (R 4.4.0)</span></span>
<span><span class="co">## htmltools 0.5.7 2023-11-03 [2] CRAN (R 4.4.0)</span></span>
<span><span class="co">## htmltools * 0.5.7 2023-11-03 [2] CRAN (R 4.4.0)</span></span>
<span><span class="co">## htmlwidgets 1.6.2 2023-03-17 [2] CRAN (R 4.4.0)</span></span>
<span><span class="co">## httpuv 1.6.12 2023-10-23 [2] CRAN (R 4.4.0)</span></span>
<span><span class="co">## httr 1.4.7 2023-08-15 [2] CRAN (R 4.4.0)</span></span>
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<span><span class="co">## zlibbioc 1.49.0 2023-10-24 [2] Bioconductor</span></span>
<span><span class="co">## zoo 1.8-12 2023-04-13 [2] CRAN (R 4.4.0)</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## [1] /tmp/RtmplXrl9c/Rinstb1214fca7</span></span>
<span><span class="co">## [1] /tmp/RtmpQaUrm0/Rinstb12a9a32a</span></span>
<span><span class="co">## [2] /usr/local/lib/R/site-library</span></span>
<span><span class="co">## [3] /usr/local/lib/R/library</span></span>
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</ul>
</li>
<li>
<a href="#sec-loading-experimental-microbiome-data" id="toc-sec-loading-experimental-microbiome-data" class="nav-link" data-scroll-target="#sec-loading-experimental-microbiome-data"><span class="header-section-number">2.4</span> Loading experimental microbiome data</a>
<a href="#loading-experimental-microbiome-data" id="toc-loading-experimental-microbiome-data" class="nav-link" data-scroll-target="#loading-experimental-microbiome-data"><span class="header-section-number">2.4</span> Loading experimental microbiome data</a>
<ul class="collapse">
<li><a href="#s-workflow" id="toc-s-workflow" class="nav-link" data-scroll-target="#s-workflow"><span class="header-section-number">2.4.1</span> 16S workflow</a></li>
<li><a href="#sec-import-from-file" id="toc-sec-import-from-file" class="nav-link" data-scroll-target="#sec-import-from-file"><span class="header-section-number">2.4.2</span> Import from external files</a></li>
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</div></div><p>[<code>TreeSummarizedExperiment</code>] (<code>TreeSE</code>) <span class="citation" data-cites="R_TreeSummarizedExperiment">(<a href="#ref-R_TreeSummarizedExperiment" role="doc-biblioref">Huang 2020</a>)</span> was developed as an extension to incorporate hierarchical information (such as phylogenetic trees and sample hierarchies) and reference sequences.</p>
<p>[<code>MultiAssayExperiment</code>] (<code>MAE</code>) <span class="citation" data-cites="Ramos2017">(<a href="#ref-Ramos2017" role="doc-biblioref">Ramos et al. 2017</a>)</span> provides an organized way to bind several different data containers together in a single object. For example, we can bind microbiome data (in <code>TreeSE</code> container) with metabolomic profiling data (in <code>SE</code>) container, with (partially) shared sample metadata. This is convenient and robust for instance in subsetting and other data manipulation tasks. Microbiome data can be part of multiomics experiments and analysis strategies. We highlight how the methods used througout in this book relate to this data framework by using the <code>TreeSummarizedExperiment</code>, <code>MultiAssayExperiment</code>, and classes beyond.</p>
<div class="no-row-height column-margin column-container"></div><p>This section provides an introductions to these data containers. In microbiome data science, these containers link taxonomic abundance tables with rich side information on the features and samples. Taxonomic abundance data can be obtained by 16S rRNA amplicon or metagenomic sequencing, phylogenetic microarrays, or by other means. Many microbiome experiments include multiple versions and types of data generated independently or derived from each other through transformation or agglomeration. We start by providing recommendations on how to represent different varieties of multi-table data within the <code>TreeSummarizedExperiment</code> class.</p>
<p>The options and recommendations are summarized in <a href="#tbl-options">Table&nbsp;<span>2.1</span></a>.</p>
<p>The options and recommendations are summarized in Table <a href="#tbl-options">Table&nbsp;<span>2.1</span></a>.</p>
<section id="sec-assay-slot" class="level3" data-number="2.2.1"><h3 data-number="2.2.1" class="anchored" data-anchor-id="sec-assay-slot">
<span class="header-section-number">2.2.1</span> Assay data</h3>
<p>The original count-based taxonomic abundance tables may have different transformations, such as logarithmic, Centered Log-Ratio (CLR), or relative abundance. These are typically stored in <em><strong>assays</strong></em>.</p>
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<span><span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span><span class="op">(</span><span class="va">HintikkaXOData</span>, package <span class="op">=</span> <span class="st">"mia"</span><span class="op">)</span></span>
<span><span class="va">mae</span> <span class="op">&lt;-</span> <span class="va">HintikkaXOData</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Data is available in <em><a href="https://bioconductor.org/packages/3.19/SummarizedExperiment">SummarizedExperiment</a></em>, <code>r Biocpkg("TreeSummarizedExperiment")</code> and <code>r Biocpkg("MultiAssayExperiment")</code> data containers; see the separate page on <a href="../pages/23_multi-assay_analyses.html">alternative containers</a> for more details.</p>
<p>Data is available in <em><a href="https://bioconductor.org/packages/3.19/SummarizedExperiment">SummarizedExperiment</a></em>, <code>r Biocpkg("TreeSummarizedExperiment")</code> and <code>r Biocpkg("MultiAssayExperiment")</code> data containers; see the separate page on <a href="https://microbiome.github.io/OMA/multitable.html">alternative containers</a> for more details.</p>
</section><section id="curated-metagenomic-data" class="level3 page-columns page-full" data-number="2.3.3"><h3 data-number="2.3.3" class="anchored" data-anchor-id="curated-metagenomic-data">
<span class="header-section-number">2.3.3</span> Curated metagenomic data</h3>
<p><a href="https://bioconductor.org/packages/release/data/experiment/html/curatedMetagenomicData.html">curatedMetagenomicData</a> is a large collection of curated human microbiome datasets, provided as <code>(Tree)SummarizedExperiment</code> objects <span class="citation" data-cites="Pasolli2017">(<a href="#ref-Pasolli2017" role="doc-biblioref">Pasolli et al. 2017</a>)</span>. The resource provides curated human microbiome data including gene families, marker abundance, marker presence, pathway abundance, pathway coverage, and relative abundance for samples from different body sites. See the package homepage for more details on data availability and access.</p>
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<li>
<a href="https://github.com/cran/qiitr">qiitr</a> provides access to <a href="https://qiita.com/about">QIITA</a>
</li>
</ul></section></section><section id="sec-loading-experimental-microbiome-data" class="level2" data-number="2.4"><h2 data-number="2.4" class="anchored" data-anchor-id="sec-loading-experimental-microbiome-data">
</ul></section></section><section id="loading-experimental-microbiome-data" class="level2" data-number="2.4"><h2 data-number="2.4" class="anchored" data-anchor-id="loading-experimental-microbiome-data">
<span class="header-section-number">2.4</span> Loading experimental microbiome data</h2>
<section id="s-workflow" class="level3" data-number="2.4.1"><h3 data-number="2.4.1" class="anchored" data-anchor-id="s-workflow">
<span class="header-section-number">2.4.1</span> 16S workflow</h3>
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<span></span>
<span><span class="co"># view new variable</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">tse</span><span class="op">$</span><span class="va">NewVariable</span><span class="op">)</span></span>
<span><span class="co">## [1] 0.6530 0.6679 0.3921 0.3830 0.3275 0.6138</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<span><span class="co">## [1] 0.9387 0.7439 0.9393 0.9141 0.4957 0.6280</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section><section id="merge-data" class="level2" data-number="3.3"><h2 data-number="3.3" class="anchored" data-anchor-id="merge-data">
<span class="header-section-number">3.3</span> Merge data</h2>
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<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;">0.027</td>
<td style="text-align: right;">0.041</td>
<td style="text-align: right;">3.991</td>
<td style="text-align: right;">0.2795</td>
</tr>
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<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;">0.127</td>
<td style="text-align: right;">0.130</td>
<td style="text-align: right;">3.991</td>
<td style="text-align: right;">0.1463</td>
</tr>
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<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;">0.094</td>
<td style="text-align: right;">0.131</td>
<td style="text-align: right;">3.991</td>
<td style="text-align: right;">0.0421</td>
</tr>
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<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;">0.107</td>
<td style="text-align: right;">0.120</td>
<td style="text-align: right;">1.0288</td>
<td style="text-align: right;">0.2440</td>
</tr>
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<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;">0.528</td>
<td style="text-align: right;">0.534</td>
<td style="text-align: right;">0.9283</td>
<td style="text-align: right;">0.0111</td>
</tr>
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<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;">0.417</td>
<td style="text-align: right;">0.430</td>
<td style="text-align: right;">0.3319</td>
<td style="text-align: right;">0.9860</td>
</tr>
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<p>As an example, we use a dataset from the following publication: <span class="citation" data-cites="Hintikka2021">(<a href="#ref-Hintikka2021" role="doc-biblioref">2021</a>)</span> Xylo-oligosaccharides in prevention of hepatic steatosis and adipose tissue inflammation: associating taxonomic and metabolomic patterns in fecal microbiota with biclustering. In this study, mice were fed either with a high-fat or a low-fat diet, and with or without prebiotics, for the purpose studying whether prebiotics attenuate the negative impact of a high-fat diet on health.</p>
<div class="no-row-height column-margin column-container"><div id="ref-Hintikka2021" class="csl-entry" role="listitem">
Hintikka, Jukka, Sanna Lensu, Elina Mäkinen, Sira Karvinen, Marjaana Honkanen, Jere Lindén, Tim Garrels, Satu Pekkala, and Leo Lahti. 2021. <span>“Xylo-Oligosaccharides in Prevention of Hepatic Steatosis and Adipose Tissue Inflammation: Associating Taxonomic and Metabolomic Patterns in Fecal Microbiomes with Biclustering.”</span> <em>International Journal of Environmental Research and Public Health</em> 18 (8): 4049.
</div></div><p>This example data can be loaded from microbiomeDataSets. The data is already in MAE format. It includes three different experiments: microbial abundance data, metabolite concentrations, and data about different biomarkers. If you like to construct the same data object from the original files instead, <a href="[@sec-loading-experimental-microbiome-data]">here</a> you can find help for importing data into an SE object.</p>
</div></div><p>This example data can be loaded from microbiomeDataSets. The data is already in MAE format. It includes three different experiments: microbial abundance data, metabolite concentrations, and data about different biomarkers. If you like to construct the same data object from the original files instead, <a href="https://microbiome.github.io/OMA/containers.html#loading-experimental-microbiome-data">here</a> you can find help for importing data into an SE object.</p>
<div class="cell" data-layout-align="center" data-hash="23_multi-assay_analyses_cache/html/cross-correlation1_b64126bd8e2965a8bb30b11edf2b0028">
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># Load the data</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span><span class="op">(</span><span class="va">HintikkaXOData</span>, package <span class="op">=</span> <span class="st">"mia"</span><span class="op">)</span></span>
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