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(v1.0.1.9000) first PCA implementation
2020-03-07 21:48:21 +01:00
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<h1>How to conduct principal component analysis (PCA) for AMR</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">07 March 2020</h4>
<div class="hidden name"><code>PCA.Rmd</code></div>
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<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>
</div>
<div id="transforming" class="section level1">
<h1 class="hasAnchor">
<a href="#transforming" class="anchor"></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="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(AMR)</span>
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(dplyr)</span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw"><a href="https://dplyr.tidyverse.org/reference/reexports.html">glimpse</a></span>(example_isolates)</span>
<span id="cb1-4"><a href="#cb1-4"></a><span class="co"># Observations: 2,000</span></span>
<span id="cb1-5"><a href="#cb1-5"></a><span class="co"># Variables: 49</span></span>
<span id="cb1-6"><a href="#cb1-6"></a><span class="co"># $ date &lt;date&gt; 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002…</span></span>
<span id="cb1-7"><a href="#cb1-7"></a><span class="co"># $ hospital_id &lt;fct&gt; D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B,…</span></span>
<span id="cb1-8"><a href="#cb1-8"></a><span class="co"># $ ward_icu &lt;lgl&gt; FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, T…</span></span>
<span id="cb1-9"><a href="#cb1-9"></a><span class="co"># $ ward_clinical &lt;lgl&gt; TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, F…</span></span>
<span id="cb1-10"><a href="#cb1-10"></a><span class="co"># $ ward_outpatient &lt;lgl&gt; FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…</span></span>
<span id="cb1-11"><a href="#cb1-11"></a><span class="co"># $ age &lt;dbl&gt; 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 7…</span></span>
<span id="cb1-12"><a href="#cb1-12"></a><span class="co"># $ gender &lt;chr&gt; "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M"…</span></span>
<span id="cb1-13"><a href="#cb1-13"></a><span class="co"># $ patient_id &lt;chr&gt; "A77334", "A77334", "067927", "067927", "067927", "06…</span></span>
<span id="cb1-14"><a href="#cb1-14"></a><span class="co"># $ mo &lt;mo&gt; B_ESCHR_COLI, B_ESCHR_COLI, B_STPHY_EPDR, B_STPHY_EPDR…</span></span>
<span id="cb1-15"><a href="#cb1-15"></a><span class="co"># $ PEN &lt;rsi&gt; R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R,…</span></span>
<span id="cb1-16"><a href="#cb1-16"></a><span class="co"># $ OXA &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-17"><a href="#cb1-17"></a><span class="co"># $ FLC &lt;rsi&gt; NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA,…</span></span>
<span id="cb1-18"><a href="#cb1-18"></a><span class="co"># $ AMX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-19"><a href="#cb1-19"></a><span class="co"># $ AMC &lt;rsi&gt; I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I,…</span></span>
<span id="cb1-20"><a href="#cb1-20"></a><span class="co"># $ AMP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-21"><a href="#cb1-21"></a><span class="co"># $ TZP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-22"><a href="#cb1-22"></a><span class="co"># $ CZO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-23"><a href="#cb1-23"></a><span class="co"># $ FEP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-24"><a href="#cb1-24"></a><span class="co"># $ CXM &lt;rsi&gt; I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R…</span></span>
<span id="cb1-25"><a href="#cb1-25"></a><span class="co"># $ FOX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-26"><a href="#cb1-26"></a><span class="co"># $ CTX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span></span>
<span id="cb1-27"><a href="#cb1-27"></a><span class="co"># $ CAZ &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …</span></span>
<span id="cb1-28"><a href="#cb1-28"></a><span class="co"># $ CRO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span></span>
<span id="cb1-29"><a href="#cb1-29"></a><span class="co"># $ GEN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-30"><a href="#cb1-30"></a><span class="co"># $ TOB &lt;rsi&gt; NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…</span></span>
<span id="cb1-31"><a href="#cb1-31"></a><span class="co"># $ AMK &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-32"><a href="#cb1-32"></a><span class="co"># $ KAN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-33"><a href="#cb1-33"></a><span class="co"># $ TMP &lt;rsi&gt; R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …</span></span>
<span id="cb1-34"><a href="#cb1-34"></a><span class="co"># $ SXT &lt;rsi&gt; R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S,…</span></span>
<span id="cb1-35"><a href="#cb1-35"></a><span class="co"># $ NIT &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-36"><a href="#cb1-36"></a><span class="co"># $ FOS &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-37"><a href="#cb1-37"></a><span class="co"># $ LNZ &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></span>
<span id="cb1-38"><a href="#cb1-38"></a><span class="co"># $ CIP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…</span></span>
<span id="cb1-39"><a href="#cb1-39"></a><span class="co"># $ MFX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-40"><a href="#cb1-40"></a><span class="co"># $ VAN &lt;rsi&gt; R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …</span></span>
<span id="cb1-41"><a href="#cb1-41"></a><span class="co"># $ TEC &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></span>
<span id="cb1-42"><a href="#cb1-42"></a><span class="co"># $ TCY &lt;rsi&gt; R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …</span></span>
<span id="cb1-43"><a href="#cb1-43"></a><span class="co"># $ TGC &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-44"><a href="#cb1-44"></a><span class="co"># $ DOX &lt;rsi&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…</span></span>
<span id="cb1-45"><a href="#cb1-45"></a><span class="co"># $ ERY &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span></span>
<span id="cb1-46"><a href="#cb1-46"></a><span class="co"># $ CLI &lt;rsi&gt; NA, NA, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, NA, NA…</span></span>
<span id="cb1-47"><a href="#cb1-47"></a><span class="co"># $ AZM &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span></span>
<span id="cb1-48"><a href="#cb1-48"></a><span class="co"># $ IPM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span></span>
<span id="cb1-49"><a href="#cb1-49"></a><span class="co"># $ MEM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-50"><a href="#cb1-50"></a><span class="co"># $ MTR &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-51"><a href="#cb1-51"></a><span class="co"># $ CHL &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-52"><a href="#cb1-52"></a><span class="co"># $ COL &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span></span>
<span id="cb1-53"><a href="#cb1-53"></a><span class="co"># $ MUP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span></span>
<span id="cb1-54"><a href="#cb1-54"></a><span class="co"># $ RIF &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></span></code></pre></div>
<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="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>resistance_data &lt;-<span class="st"> </span>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></span>
<span id="cb2-2"><a href="#cb2-2"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(<span class="dt">order =</span> <span class="kw"><a href="../reference/mo_property.html">mo_order</a></span>(mo), <span class="co"># group on anything, like order</span></span>
<span id="cb2-3"><a href="#cb2-3"></a> <span class="dt">genus =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(mo)) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># and genus as we do here</span></span>
<span id="cb2-4"><a href="#cb2-4"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html">summarise_if</a></span>(is.rsi, resistance) <span class="op">%&gt;%</span><span class="st"> </span><span class="co"># then get resistance of all drugs</span></span>
<span id="cb2-5"><a href="#cb2-5"></a><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(order, genus, AMC, CXM, CTX, </span>
<span id="cb2-6"><a href="#cb2-6"></a> CAZ, GEN, TOB, TMP, SXT) <span class="co"># and select only relevant columns</span></span>
<span id="cb2-7"><a href="#cb2-7"></a></span>
<span id="cb2-8"><a href="#cb2-8"></a><span class="kw"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(resistance_data)</span>
<span id="cb2-9"><a href="#cb2-9"></a><span class="co"># # A tibble: 6 x 10</span></span>
<span id="cb2-10"><a href="#cb2-10"></a><span class="co"># # Groups: order [2]</span></span>
<span id="cb2-11"><a href="#cb2-11"></a><span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span></span>
<span id="cb2-12"><a href="#cb2-12"></a><span class="co"># &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;</span></span>
<span id="cb2-13"><a href="#cb2-13"></a><span class="co"># 1 (unknown orde… Micrococcoides NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-14"><a href="#cb2-14"></a><span class="co"># 2 Actinomycetal… Actinomyces NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-15"><a href="#cb2-15"></a><span class="co"># 3 Actinomycetal… Corynebacterium NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-16"><a href="#cb2-16"></a><span class="co"># 4 Actinomycetal… Dermabacter NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-17"><a href="#cb2-17"></a><span class="co"># 5 Actinomycetal… Micrococcus NA NA NA NA NA NA NA NA</span></span>
<span id="cb2-18"><a href="#cb2-18"></a><span class="co"># 6 Actinomycetal… Propionibacter… NA NA NA NA NA NA NA NA</span></span></code></pre></div>
</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>
<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="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>pca_result &lt;-<span class="st"> </span><span class="kw"><a href="../reference/pca.html">pca</a></span>(resistance_data)</span>
<span id="cb3-2"><a href="#cb3-2"></a><span class="co"># </span><span class="al">NOTE</span><span class="co">: Columns selected for PCA: AMC/CXM/CTX/CAZ/GEN/TOB/TMP/SXT.</span></span>
<span id="cb3-3"><a href="#cb3-3"></a><span class="co"># Total observations available: 7.</span></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>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a><span class="kw"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(pca_result)</span>
<span id="cb4-2"><a href="#cb4-2"></a><span class="co"># Importance of components:</span></span>
<span id="cb4-3"><a href="#cb4-3"></a><span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span></span>
<span id="cb4-4"><a href="#cb4-4"></a><span class="co"># Standard deviation 2.1580 1.6783 0.61282 0.33017 0.20150 0.03190 2.123e-16</span></span>
<span id="cb4-5"><a href="#cb4-5"></a><span class="co"># Proportion of Variance 0.5821 0.3521 0.04694 0.01363 0.00508 0.00013 0.000e+00</span></span>
<span id="cb4-6"><a href="#cb4-6"></a><span class="co"># Cumulative Proportion 0.5821 0.9342 0.98117 0.99480 0.99987 1.00000 1.000e+00</span></span></code></pre></div>
<p>Good news. The first two components explain a total of 93.4% 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>
</div>
<div id="plotting-the-results" class="section level1">
<h1 class="hasAnchor">
<a href="#plotting-the-results" class="anchor"></a>Plotting the results</h1>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span>(pca_result)</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 the 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="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(pca_result)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
<p>You can also print an ellipse per group, and edit the appearance:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="kw"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(pca_result, <span class="dt">ellipse =</span> <span class="ot">TRUE</span>) <span class="op">+</span></span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="st"> </span>ggplot2<span class="op">::</span><span class="kw"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span>(<span class="dt">title =</span> <span class="st">"An AMR/PCA biplot!"</span>)</span></code></pre></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
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<li><a href="#introduction">Introduction</a></li>
<li><a href="#transforming">Transforming</a></li>
<li><a href="#perform-principal-component-analysis">Perform principal component analysis</a></li>
<li><a href="#plotting-the-results">Plotting the results</a></li>
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