Commit 60d61b9d authored by Jakob Wirbel's avatar Jakob Wirbel

modify header so that the website is not broken anymore.

parent 2bb2cc51
......@@ -5,25 +5,12 @@
<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>Statistical Inference of Associations between Microbial
Communities And host phenoTypes • SIAMCAT</title>
<title>Holdout Testing with SIAMCAT • 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).">
<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script><!-- sticky kit --><script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
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<meta property="og:description" content="">
<meta property="og:image" content="/logo.png">
<meta name="twitter:card" content="summary">
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......@@ -169,13 +156,13 @@ 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 0.016 s</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.015 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 0.04 s</code></pre>
<pre><code>## + finished validate.data in 0.039 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>,
......@@ -200,7 +187,7 @@ 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 0.04 s</code></pre>
<pre><code>## + finished validate.data in 0.031 s</code></pre>
</div>
<div id="model-building-on-the-french-dataset" class="section level1">
<h1 class="hasAnchor">
......@@ -220,7 +207,7 @@ 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 0.007 s</code></pre>
<pre><code>## + finished filter.features in 0.003 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>,
......@@ -231,7 +218,7 @@ 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 0.006 s</code></pre>
<pre><code>## + finished normalize.features in 0.004 s</code></pre>
</div>
<div id="model-training" class="section level2">
<h2 class="hasAnchor">
......@@ -275,7 +262,7 @@ 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 0.004 s</code></pre>
<pre><code>## + finished normalize.features in 0.005 s</code></pre>
</div>
<div id="holdout-predictions" class="section level2">
<h2 class="hasAnchor">
......
......@@ -5,25 +5,12 @@
<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>Statistical Inference of Associations between Microbial
Communities And host phenoTypes • SIAMCAT</title>
<title>SIAMCAT input files formats • 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).">
<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script><!-- sticky kit --><script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
<script src="../pkgdown.js"></script><meta property="og:title" content="SIAMCAT input files formats">
<meta property="og:description" content="">
<meta property="og:image" content="/logo.png">
<meta name="twitter:card" content="summary">
<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
......@@ -478,7 +465,7 @@ 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 0.004 s</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.003 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>
......@@ -488,7 +475,7 @@ 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 0.443 s</code></pre>
<pre><code>## + finished validate.data in 0.433 s</code></pre>
</div>
</div>
<div id="creating-a-siamcat-class-object" class="section level1">
......
......@@ -5,25 +5,12 @@
<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>Statistical Inference of Associations between Microbial
Communities And host phenoTypes • SIAMCAT</title>
<title>SIAMCAT: 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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<meta name="twitter:card" content="summary">
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......@@ -176,7 +163,7 @@
## CRC
## Label used as control:
## CTR</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.002 s</code></pre>
<pre><code>## + finished create.label.from.metadata in 0.001 s</code></pre>
<p>Now we have all the ingredients to create a <code>SIAMCAT</code> object. Please have a look at the <a href="SIAMCAT_read-in.html">vignette about input formats</a> for more information about supported formats and other ways to create a <code>SIAMCAT</code> object.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">siamcat &lt;-<span class="st"> </span><span class="kw"><a href="../reference/siamcat.html">siamcat</a></span>(<span class="dt">feat=</span>feat.crc.zeller,
<span class="dt">label=</span>label.crc.zeller,
......@@ -271,13 +258,13 @@ models[[<span class="dv">1</span>]]</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">siamcat &lt;-<span class="st"> </span><span class="kw"><a href="../reference/make.predictions.html">make.predictions</a></span>(siamcat)
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 0.05713775 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>
<pre><code>## CV_rep1 CV_rep2
## CCIS27304052ST-3-0 0.05627524 0.1227390
## CCIS15794887ST-4-0 0.06400252 0.0941945
## CCIS74726977ST-3-0 0.41996493 0.2777645
## CCIS16561622ST-4-0 0.34675575 0.2208130
## CCIS79210440ST-3-0 0.44411220 0.1147398
## CCIS82507866ST-3-0 0.07560513 0.0656949</code></pre>
</div>
</div>
<div id="model-evaluation-and-interpretation" class="section level1">
......
......@@ -2,32 +2,27 @@
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<html lang="en">
<head>
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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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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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......@@ -44,7 +39,6 @@
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Communities And host phenoTypes — SIAMCAT-package" />
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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,
......@@ -25,9 +32,13 @@
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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model plots)." />
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Communities And host phenoTypes • SIAMCAT</title>
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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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<title>Universal slot accessor function for siamcat-class. — accessSlot • SIAMCAT</title>
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<meta property="og:title" content="Universal slot accessor function for siamcat-class. — accessSlot" />
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......@@ -44,7 +41,6 @@
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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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<meta name="twitter:card" content="summary">
<title>Add metadata as predictors — add.meta.pred • SIAMCAT</title>
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<meta property="og:title" content="Add metadata as predictors — add.meta.pred" />
<meta property="og:description" content="This function adds metadata to the feature matrix to be later
used as predictors" />
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<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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......@@ -44,7 +42,6 @@
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......
......@@ -2,32 +2,29 @@
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Communities And host phenoTypes • SIAMCAT</title>
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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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<title>Assign a new assocications object to <code>x</code> — associations&lt;- • 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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<title>Assign a new list containing a cross-validation split to a
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