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@ -185,11 +185,10 @@
</header><div class="row">
</header><script src="PCA_files/accessible-code-block-0.0.1/empty-anchor.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>
<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/HEAD/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
@ -199,8 +198,7 @@
<p><strong>NOTE: This page will be updated soon, as the pca() function
is currently being developed.</strong></p>
<p><strong>NOTE: This page will be updated soon, as the pca() function is currently being developed.</strong></p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@ -208,12 +206,11 @@ is currently being developed.</strong></p>
<div class="section level2">
<h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
</h2>
<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>
<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" class="external-link">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" 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="fu"><a href="https://dplyr.tidyverse.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 <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…</span>
@ -265,8 +262,7 @@ is currently being developed.</strong></p>
<span class="co"># $ COL <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R…</span>
<span class="co"># $ MUP <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ RIF <span style="color: #949494; font-style: italic;">&lt;rsi&gt;</span> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span></code></pre></div>
<p>Now to transform this to a data set with only resistance percentages
per taxonomic order and genus:</p>
<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"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></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>
@ -290,15 +286,12 @@ per taxonomic order and genus:</p>
<div class="section level2">
<h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
</h2>
<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>
<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" class="external-link">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" 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>
@ -310,11 +303,7 @@ function:</p>
<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><span class="co"># Groups (n=4, named as 'order'):</span>
<span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></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" class="external-link">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 class="section level2">
<h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
@ -322,9 +311,7 @@ per drug explain the difference per microorganism.</p>
<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" 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>
<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">
<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><span class="op">)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
@ -348,14 +335,12 @@ automatically adds the right labels and even groups:</p>
<footer><div class="copyright">
<p></p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
Erwin E. A. Hassing.</p>
<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein, Erwin E. A. Hassing.</p>
</div>
<div class="pkgdown">
<p></p>
<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
2.0.2.</p>
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