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<![endif]-->
</head>
<body data-spy="scroll" data-target="#toc">
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</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.7.1.9022</span>
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<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fas fa-directions"></span>
<span class="fa fa-directions"></span>
Conduct AMR analysis
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<li>
<a href="../articles/resistance_predict.html">
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Predict antimicrobial resistance
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Conduct principal component analysis for AMR
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Determine multi-drug resistance (MDR)
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@ -172,7 +174,7 @@
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@ -187,13 +189,13 @@
</header><script src="PCA_files/header-attrs-2.8/header-attrs.js"></script><div class="row">
</header><script src="PCA_files/header-attrs-2.9/header-attrs.js"></script><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/PCA.Rmd"><code>vignettes/PCA.Rmd</code></a></small>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
<div class="hidden name"><code>PCA.Rmd</code></div>
</div>
@ -203,16 +205,16 @@
<p><strong>NOTE: This page will be updated soon, as the pca() function is currently being developed.</strong></p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
<a href="#introduction" class="anchor" aria-hidden="true"></a>Introduction</h1>
</div>
<div id="transforming" class="section level1">
<h1 class="hasAnchor">
<a href="#transforming" class="anchor"></a>Transforming</h1>
<a href="#transforming" class="anchor" aria-hidden="true"></a>Transforming</h1>
<p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
<span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/msberends/AMR" class="external-link">AMR</a></span><span class="op">)</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span>
<span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
<span class="co"># Rows: 2,000</span>
<span class="co"># Columns: 49</span>
<span class="co"># $ date &lt;date&gt; 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…</span>
@ -267,13 +269,13 @@
<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span>order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span>
genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="co"># and genus as we do here</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="co"># then get resistance of all drugs</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.rsi</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="co"># then get resistance of all drugs</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,
<span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span><span class="op">)</span> <span class="co"># and select only relevant columns</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<span class="co"># # A tibble: 6 x 10</span>
<span class="co"># # Groups: order [5]</span>
<span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span>
@ -287,15 +289,15 @@
</div>
<div id="perform-principal-component-analysis" class="section level1">
<h1 class="hasAnchor">
<a href="#perform-principal-component-analysis" class="anchor"></a>Perform principal component analysis</h1>
<a href="#perform-principal-component-analysis" class="anchor" aria-hidden="true"></a>Perform principal component analysis</h1>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span>
<span class="co"># ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP"</span>
<span class="co"># and "TOB". Total observations available: 7.</span></code></pre></div>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> function:</p>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code> function:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span>
<span class="co"># Groups (n=4, named as 'order'):</span>
<span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span>
<span class="co"># Importance of components:</span>
@ -305,13 +307,13 @@
<span class="co"># Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></code></pre></div>
<pre><code># Groups (n=4, named as 'order'):
# [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</code></pre>
<p>Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the <em>Proportion of Variance</em>. We can create a so-called biplot with the base R <code><a href="https://rdrr.io/r/stats/biplot.html">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
<p>Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the <em>Proportion of Variance</em>. We can create a so-called biplot with the base R <code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
</div>
<div id="plotting-the-results" class="section level1">
<h1 class="hasAnchor">
<a href="#plotting-the-results" class="anchor"></a>Plotting the results</h1>
<a href="#plotting-the-results" class="anchor" aria-hidden="true"></a>Plotting the results</h1>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
<p>But we can’t see the explanation of the points. Perhaps this works better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that automatically adds the right labels and even groups:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
@ -320,7 +322,7 @@
<p>You can also print an ellipse per group, and edit the appearance:</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span>
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></code></pre></div>
<span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
</div>
</div>
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<footer><div class="copyright">
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
<p></p>
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/" class="external-link external-link">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/" class="external-link external-link">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/" class="external-link external-link">Alexander W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/" class="external-link external-link">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/" class="external-link external-link">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/" class="external-link external-link">Corinna Glasner</a>.</p>
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@ -349,5 +353,7 @@
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