Commit 2bb2cc51 authored by Jakob Wirbel's avatar Jakob Wirbel

udate page to include matomo tracking.

parent 3aa92c9d
......@@ -5,18 +5,42 @@
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<title>Holdout Testing with SIAMCAT • SIAMCAT</title>
<title>Statistical Inference of Associations between Microbial
Communities And host phenoTypes • SIAMCAT</title>
<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.3.7/cosmo/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
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<script src="pkgdown.js"></script><meta property="og:title" content="Statistical Inference of Associations between Microbial
Communities And host phenoTypes">
<meta property="og:description" content="Pipeline for Statistical Inference of Associations between
Microbial Communities And host phenoTypes (SIAMCAT). A primary goal
of analyzing microbiome data is to determine changes in community
composition that are associated with environmental factors. In particular,
linking human microbiome composition to host phenotypes such as diseases
has become an area of intense research. For this, robust statistical
modeling and biomarker extraction toolkits are crucially needed. SIAMCAT
provides a full pipeline supporting data preprocessing, statistical
association testing, statistical modeling (LASSO logistic regression)
including tools for evaluation and interpretation of these models (such as
cross validation, parameter selection, ROC analysis and diagnostic
model plots).">
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......@@ -145,13 +169,13 @@ siamcat.fr &lt;-<span class="st"> </span><span class="kw"><a href="../reference/
## CRC
## Label used as control:
## CTR</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.015 s</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.016 s</code></pre>
<pre><code>## + starting validate.data</code></pre>
<pre><code>## +++ checking overlap between labels and features</code></pre>
<pre><code>## + Keeping labels of 141 sample(s).</code></pre>
<pre><code>## +++ checking sample number per class</code></pre>
<pre><code>## +++ checking overlap between samples and metadata</code></pre>
<pre><code>## + finished validate.data in 0.041 s</code></pre>
<pre><code>## + finished validate.data in 0.04 s</code></pre>
<p>We can load the data from the Chinese study in a similar way and also create a <code>SIAMCAT</code> object for the holdout dataset.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># features</span>
feat.cn &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/read.table">read.table</a></span>(fn.feat.cn, <span class="dt">sep=</span><span class="st">'</span><span class="ch">\t</span><span class="st">'</span>, <span class="dt">quote=</span><span class="st">""</span>,
......@@ -176,7 +200,7 @@ siamcat.cn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/
<pre><code>## + Keeping labels of 128 sample(s).</code></pre>
<pre><code>## +++ checking sample number per class</code></pre>
<pre><code>## +++ checking overlap between samples and metadata</code></pre>
<pre><code>## + finished validate.data in 0.032 s</code></pre>
<pre><code>## + finished validate.data in 0.04 s</code></pre>
</div>
<div id="model-building-on-the-french-dataset" class="section level1">
<h1 class="hasAnchor">
......@@ -196,7 +220,7 @@ siamcat.cn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/
<pre><code>## +++ before filtering, the data have 1754 features</code></pre>
<pre><code>## +++ removed 1 features corresponding to UNMAPPED reads</code></pre>
<pre><code>## +++ removed 1538 features whose values did not exceed 0.001 in any sample (retaining 215)</code></pre>
<pre><code>## + finished filter.features in 0.003 s</code></pre>
<pre><code>## + finished filter.features in 0.007 s</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">siamcat.fr &lt;-<span class="st"> </span><span class="kw"><a href="../reference/normalize.features.html">normalize.features</a></span>(
siamcat.fr,
<span class="dt">norm.method =</span> <span class="st">"log.std"</span>,
......@@ -207,7 +231,7 @@ siamcat.cn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/
<pre><code>## +++ performing de novo normalization using the log.std method</code></pre>
<pre><code>## + feature sparsity before normalization: 46.05%</code></pre>
<pre><code>## +++ feature sparsity after normalization: 0 %</code></pre>
<pre><code>## + finished normalize.features in 0.004 s</code></pre>
<pre><code>## + finished normalize.features in 0.006 s</code></pre>
</div>
<div id="model-training" class="section level2">
<h2 class="hasAnchor">
......@@ -251,7 +275,7 @@ siamcat.cn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/
<pre><code>## + performing frozen log.std normalization using the supplied parameters</code></pre>
<pre><code>## + feature sparsity before normalization: 49.77%</code></pre>
<pre><code>## + feature sparsity after normalization: 0%</code></pre>
<pre><code>## + finished normalize.features in 0.005 s</code></pre>
<pre><code>## + finished normalize.features in 0.004 s</code></pre>
</div>
<div id="holdout-predictions" class="section level2">
<h2 class="hasAnchor">
......
......@@ -5,18 +5,42 @@
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>SIAMCAT input files formats • SIAMCAT</title>
<title>Statistical Inference of Associations between Microbial
Communities And host phenoTypes • SIAMCAT</title>
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Communities And host phenoTypes">
<meta property="og:description" content="Pipeline for Statistical Inference of Associations between
Microbial Communities And host phenoTypes (SIAMCAT). A primary goal
of analyzing microbiome data is to determine changes in community
composition that are associated with environmental factors. In particular,
linking human microbiome composition to host phenotypes such as diseases
has become an area of intense research. For this, robust statistical
modeling and biomarker extraction toolkits are crucially needed. SIAMCAT
provides a full pipeline supporting data preprocessing, statistical
association testing, statistical modeling (LASSO logistic regression)
including tools for evaluation and interpretation of these models (such as
cross validation, parameter selection, ROC analysis and diagnostic
model plots).">
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......@@ -412,7 +436,7 @@ feat &lt;-<span class="st"> </span>meta.and.features<span class="op">$</span>fea
## cancer
## Label used as control:
## healthy</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.015 s</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.016 s</code></pre>
</div>
<div id="metagenomeseq-format-files" class="section level2">
<h2 class="hasAnchor">
......@@ -454,7 +478,7 @@ label &lt;-<span class="st"> </span><span class="kw"><a href="../reference/creat
## Freshwater,Freshwater (creek),Ocean
## Label used as control:
## rest</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.003 s</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.004 s</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># run the constructor function</span>
siamcat &lt;-<span class="st"> </span><span class="kw"><a href="../reference/siamcat.html">siamcat</a></span>(<span class="dt">phyloseq=</span>GlobalPatterns, <span class="dt">label=</span>label)</code></pre></div>
<pre><code>## + starting validate.data</code></pre>
......@@ -464,7 +488,7 @@ siamcat &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sia
<pre><code>## Data set has only 8 training examples of class Case .
## Note that a dataset this small/skewed is not necessarily suitable for analysis in this pipeline.</code></pre>
<pre><code>## +++ checking overlap between samples and metadata</code></pre>
<pre><code>## + finished validate.data in 0.439 s</code></pre>
<pre><code>## + finished validate.data in 0.443 s</code></pre>
</div>
</div>
<div id="creating-a-siamcat-class-object" class="section level1">
......
......@@ -5,18 +5,42 @@
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<title>SIAMCAT: Statistical Inference of Associations between Microbial Communities And host phenoTypes • SIAMCAT</title>
<title>Statistical Inference of Associations between Microbial
Communities And host phenoTypes • SIAMCAT</title>
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Communities And host phenoTypes">
<meta property="og:description" content="Pipeline for Statistical Inference of Associations between
Microbial Communities And host phenoTypes (SIAMCAT). A primary goal
of analyzing microbiome data is to determine changes in community
composition that are associated with environmental factors. In particular,
linking human microbiome composition to host phenotypes such as diseases
has become an area of intense research. For this, robust statistical
modeling and biomarker extraction toolkits are crucially needed. SIAMCAT
provides a full pipeline supporting data preprocessing, statistical
association testing, statistical modeling (LASSO logistic regression)
including tools for evaluation and interpretation of these models (such as
cross validation, parameter selection, ROC analysis and diagnostic
model plots).">
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......@@ -162,7 +186,7 @@
<pre><code>## + Keeping labels of 141 sample(s).</code></pre>
<pre><code>## +++ checking sample number per class</code></pre>
<pre><code>## +++ checking overlap between samples and metadata</code></pre>
<pre><code>## + finished validate.data in 0.058 s</code></pre>
<pre><code>## + finished validate.data in 0.057 s</code></pre>
<p>A few information about the <code>siamcat</code> object can be accessed with the <code>show</code> function from <code>phyloseq</code> (<code>SIAMCAT</code> builds on the <code>phyloseq</code> data structure):</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">show</span>(siamcat)</code></pre></div>
<pre><code>## siamcat-class object
......@@ -248,12 +272,12 @@ models[[<span class="dv">1</span>]]</code></pre></div>
pred_matrix &lt;-<span class="st"> </span><span class="kw"><a href="../reference/pred_matrix-methods.html">pred_matrix</a></span>(siamcat)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/head">head</a></span>(pred_matrix)</code></pre></div>
<pre><code>## CV_rep1 CV_rep2
## CCIS27304052ST-3-0 0.19880497 0.09535739
## CCIS15794887ST-4-0 0.25670422 0.16473066
## CCIS74726977ST-3-0 0.59187395 0.63774235
## CCIS16561622ST-4-0 0.54217795 0.09439314
## CCIS79210440ST-3-0 0.28578571 0.23673264
## CCIS82507866ST-3-0 0.08502265 0.07268986</code></pre>
## CCIS27304052ST-3-0 0.05713775 0.06163008
## CCIS15794887ST-4-0 0.09270902 0.10763851
## CCIS74726977ST-3-0 0.22063337 0.38061033
## CCIS16561622ST-4-0 0.44445670 0.46337167
## CCIS79210440ST-3-0 0.34595497 0.08786233
## CCIS82507866ST-3-0 0.06638877 0.06432296</code></pre>
</div>
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<div id="model-evaluation-and-interpretation" class="section level1">
......
......@@ -2,51 +2,49 @@
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has become an area of intense research. For this, robust statistical
modeling and biomarker extraction toolkits are crucially needed. SIAMCAT
provides a full pipeline supporting data preprocessing, statistical
association testing, statistical modeling (LASSO logistic regression)
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......@@ -214,6 +225,13 @@ eLife, (<strong>2019</strong>) 8:e42693</p>
<p>See Figure 3 - figure supplement 1</p>
</blockquote>
</li>
<li>
<p><strong><a href="https://www.nature.com/articles/s41591-019-0406-6">Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer</a></strong><br><em>Wirbel J, Pyl PT, Kartal E, Zych K, Kashani A, Milanese A, Fleck JS, Voigt AY, Palleja A, Ponnudurai R, Sunagawa S, Coelho LP, Schrotz-King P, Vogtmann E, Habermann N, Niméus E, Thomas AM, Manghi P, Gandini S, Serrano D, Mizutani S, Shiroma H, Shiba S, Shibata T, Yachida S, Yamada T, Waldron L, Naccarati A, Segata N, Sinha R, Ulrich CM, Brenner H, Arumugam M, Bork P, Zeller G</em><br>
Nature Medicine, (<strong>2019</strong>) [Epub ahead of print]</p>
<blockquote>
<p>In this publication, <code>SIAMCAT</code> is used extensively for holdout testing</p>
</blockquote>
</li>
</ul>
<p>If you used <code>SIAMCAT</code> in your publication, <a href="mailto:zeller@embl.de">let us know</a>!</p>
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Communities And host phenoTypes">
<meta property="og:description" content="Pipeline for Statistical Inference of Associations between
Microbial Communities And host phenoTypes (SIAMCAT). A primary goal
of analyzing microbiome data is to determine changes in community
composition that are associated with environmental factors. In particular,
linking human microbiome composition to host phenotypes such as diseases
has become an area of intense research. For this, robust statistical
modeling and biomarker extraction toolkits are crucially needed. SIAMCAT
provides a full pipeline supporting data preprocessing, statistical
association testing, statistical modeling (LASSO logistic regression)
including tools for evaluation and interpretation of these models (such as
cross validation, parameter selection, ROC analysis and diagnostic
model plots).">
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