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TuomasBorman committed Nov 13, 2024
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2 changes: 1 addition & 1 deletion docs/devel/index.html
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Expand Up @@ -560,7 +560,7 @@ <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> 2024-11-13<br><strong>Package version:</strong> 0.98.30<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> - Tuomas Borman [aut, cre]<br> - Leo Lahti [aut]<br> - Felix GM Ernst [aut]<br> - and others (see the full list of contributors) [ctb]<br><strong>Compiled:</strong> 2024-11-13<br><strong>Package version:</strong> 0.98.30<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>
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<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%;
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12 changes: 6 additions & 6 deletions docs/devel/pages/beta_diversity.html
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Expand Up @@ -1127,7 +1127,7 @@ <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;">0.028</td>
<td style="text-align: right;">0.035</td>
<td style="text-align: right;">3.991</td>
<td style="text-align: right;">0.2795</td>
</tr>
Expand All @@ -1136,7 +1136,7 @@ <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;">0.126</td>
<td style="text-align: right;">0.156</td>
<td style="text-align: right;">3.991</td>
<td style="text-align: right;">0.1463</td>
</tr>
Expand All @@ -1145,7 +1145,7 @@ <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;">0.117</td>
<td style="text-align: right;">0.100</td>
<td style="text-align: right;">3.991</td>
<td style="text-align: right;">0.0421</td>
</tr>
Expand Down Expand Up @@ -1254,7 +1254,7 @@ <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;">0.125</td>
<td style="text-align: right;">0.140</td>
<td style="text-align: right;">1.0288</td>
<td style="text-align: right;">0.2440</td>
</tr>
Expand All @@ -1265,7 +1265,7 @@ <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;">0.518</td>
<td style="text-align: right;">0.513</td>
<td style="text-align: right;">0.9283</td>
<td style="text-align: right;">0.0111</td>
</tr>
Expand All @@ -1276,7 +1276,7 @@ <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;">0.419</td>
<td style="text-align: right;">0.406</td>
<td style="text-align: right;">0.3319</td>
<td style="text-align: right;">0.9860</td>
</tr>
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34 changes: 14 additions & 20 deletions docs/devel/pages/extra_material.html
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Expand Up @@ -1296,7 +1296,7 @@ <h1 class="title"><span id="sec-extras" class="quarto-section-identifier">Append
<p>Printing a summary about the posterior:</p>
<div class="cell" data-layout-align="center">
<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://jsilve24.github.io/fido/reference/ppc_summary.html">ppc_summary</a></span><span class="op">(</span><span class="va">posterior</span><span class="op">)</span></span>
<span><span class="co">## Proportions of Observations within 95% Credible Interval: 0.9966644</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<span><span class="co">## Proportions of Observations within 95% Credible Interval: 0.9967794</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Plotting the summary of the posterior distributions of the regression parameters:</p>
<div class="cell" data-layout-align="center">
Expand Down Expand Up @@ -1487,12 +1487,12 @@ <h1 class="title"><span id="sec-extras" class="quarto-section-identifier">Append
<span><span class="co">## call:</span></span>
<span><span class="co">## biclust(x = corr, method = BCPlaid(), verbose = FALSE)</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Number of Clusters found: 5 </span></span>
<span><span class="co">## Number of Clusters found: 4 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## First 5 Cluster sizes:</span></span>
<span><span class="co">## BC 1 BC 2 BC 3 BC 4 BC 5</span></span>
<span><span class="co">## Number of Rows: 16 14 18 4 4</span></span>
<span><span class="co">## Number of Columns: 13 14 9 11 8</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<span><span class="co">## First 4 Cluster sizes:</span></span>
<span><span class="co">## BC 1 BC 2 BC 3 BC 4</span></span>
<span><span class="co">## Number of Rows: 15 16 17 2</span></span>
<span><span class="co">## Number of Columns: 13 14 10 9</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>The object includes cluster information. However compared to <code>cobiclust</code>, <code>biclust</code> object includes only information about clusters that were found, not general cluster.</p>
<p>Meaning that if one cluster size of 5 features was found out of 20 features, those 15 features do not belong to any cluster. That is why we have to create an additional cluster for features/samples that are not assigned into any cluster.</p>
Expand Down Expand Up @@ -1557,13 +1557,13 @@ <h1 class="title"><span id="sec-extras" class="quarto-section-identifier">Append
<span></span>
<span><span class="co"># Print biclusters for rows</span></span>
<span><span class="va">bicluster_rows</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co">## cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 cluster_6</span></span>
<span><span class="co">## Ambiguous_taxa_1 FALSE FALSE FALSE FALSE FALSE TRUE</span></span>
<span><span class="co">## Ambiguous_taxa_3 TRUE FALSE TRUE FALSE FALSE FALSE</span></span>
<span><span class="co">## Ambiguous_taxa_4 FALSE FALSE FALSE FALSE FALSE TRUE</span></span>
<span><span class="co">## Ambiguous_taxa_5 FALSE FALSE FALSE FALSE FALSE TRUE</span></span>
<span><span class="co">## Ambiguous_taxa_7 FALSE FALSE FALSE FALSE FALSE TRUE</span></span>
<span><span class="co">## Ambiguous_taxa_9 FALSE FALSE FALSE FALSE FALSE TRUE</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<span><span class="co">## cluster_1 cluster_2 cluster_3 cluster_4 cluster_5</span></span>
<span><span class="co">## Ambiguous_taxa_1 FALSE FALSE FALSE FALSE TRUE</span></span>
<span><span class="co">## Ambiguous_taxa_3 TRUE FALSE TRUE FALSE FALSE</span></span>
<span><span class="co">## Ambiguous_taxa_4 FALSE FALSE FALSE FALSE TRUE</span></span>
<span><span class="co">## Ambiguous_taxa_5 FALSE FALSE FALSE FALSE TRUE</span></span>
<span><span class="co">## Ambiguous_taxa_7 FALSE FALSE FALSE FALSE TRUE</span></span>
<span><span class="co">## Ambiguous_taxa_9 FALSE FALSE FALSE FALSE TRUE</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Let’s collect information for the scatter plot.</p>
<div class="cell" data-layout-align="center">
Expand Down Expand Up @@ -1641,16 +1641,10 @@ <h1 class="title"><span id="sec-extras" class="quarto-section-identifier">Append
</figure>
</div>
</div>
<div class="cell-output-display">
<div class="quarto-figure quarto-figure-center">
<figure class="figure"><p><img src="extra_material_files/figure-html/biclust_7-6.png" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:33.0%"></p>
</figure>
</div>
</div>
<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">pics</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span> <span class="op">+</span> <span class="va">pics</span><span class="op">[[</span><span class="fl">2</span><span class="op">]</span><span class="op">]</span> <span class="op">+</span> <span class="va">pics</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div class="quarto-figure quarto-figure-center">
<figure class="figure"><p><img src="extra_material_files/figure-html/biclust_7-7.png" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:33.0%"></p>
<figure class="figure"><p><img src="extra_material_files/figure-html/biclust_7-6.png" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:33.0%"></p>
</figure>
</div>
</div>
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2 changes: 1 addition & 1 deletion docs/devel/pages/integrated_learner.html
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Expand Up @@ -751,7 +751,7 @@ <h1 class="title"><span id="sec-multi-omics-integration" class="quarto-section-i
<span><span class="co">## Running base model for layer 2 ... </span></span>
<span><span class="co">## Running stacked model...</span></span>
<span><span class="co">## Running concatenated model...</span></span>
<span><span class="co">## Time for model fit : 1.623 minutes </span></span>
<span><span class="co">## Time for model fit : 1.611 minutes </span></span>
<span><span class="co">## ========================================</span></span>
<span><span class="co">## Model fit for individual layers: SL.randomForest </span></span>
<span><span class="co">## Model fit for stacked layer: SL.nnls.auc </span></span>
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26 changes: 13 additions & 13 deletions docs/devel/pages/introductory_workflow_dutch_version.html
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44 changes: 22 additions & 22 deletions docs/devel/pages/machine_learning.html
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Expand Up @@ -708,28 +708,28 @@ <h1 class="title"><span id="sec-machine_learning" class="quarto-section-identifi
<span><span class="co">## </span></span>
<span><span class="co">## Reference</span></span>
<span><span class="co">## Prediction T2D healthy</span></span>
<span><span class="co">## T2D 87 51</span></span>
<span><span class="co">## healthy 49 104</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Accuracy : 0.656 </span></span>
<span><span class="co">## 95% CI : (0.599, 0.711)</span></span>
<span><span class="co">## No Information Rate : 0.533 </span></span>
<span><span class="co">## P-Value [Acc &gt; NIR] : 1.26e-05 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Kappa : 0.31 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Mcnemar's Test P-Value : 0.92 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Sensitivity : 0.640 </span></span>
<span><span class="co">## Specificity : 0.671 </span></span>
<span><span class="co">## Pos Pred Value : 0.630 </span></span>
<span><span class="co">## Neg Pred Value : 0.680 </span></span>
<span><span class="co">## Prevalence : 0.467 </span></span>
<span><span class="co">## Detection Rate : 0.299 </span></span>
<span><span class="co">## Detection Prevalence : 0.474 </span></span>
<span><span class="co">## Balanced Accuracy : 0.655 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## 'Positive' Class : T2D </span></span>
<span><span class="co">## T2D 81 39</span></span>
<span><span class="co">## healthy 55 116</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Accuracy : 0.677 </span></span>
<span><span class="co">## 95% CI : (0.62, 0.73)</span></span>
<span><span class="co">## No Information Rate : 0.533 </span></span>
<span><span class="co">## P-Value [Acc &gt; NIR] : 3.91e-07 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Kappa : 0.346 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Mcnemar's Test P-Value : 0.122 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Sensitivity : 0.596 </span></span>
<span><span class="co">## Specificity : 0.748 </span></span>
<span><span class="co">## Pos Pred Value : 0.675 </span></span>
<span><span class="co">## Neg Pred Value : 0.678 </span></span>
<span><span class="co">## Prevalence : 0.467 </span></span>
<span><span class="co">## Detection Rate : 0.278 </span></span>
<span><span class="co">## Detection Prevalence : 0.412 </span></span>
<span><span class="co">## Balanced Accuracy : 0.672 </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## 'Positive' Class : T2D </span></span>
<span><span class="co">## </span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>The model performs relatively well considering the data type as predicting outcomes based on complex microbiome data is typically challenging.</p>
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