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<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
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<p><strong>NOTE: This page will be updated soon, as the pca() function is currently being developed.</strong></p>
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<div id="introduction" class="section level1">
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<h1 class="hasAnchor">
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<a href="#introduction" class="anchor" aria-hidden="true"></a>Introduction</h1>
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<div id="transforming" class="section level1">
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<h1 class="hasAnchor">
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<a href="#transforming" class="anchor" aria-hidden="true"></a>Transforming</h1>
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<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>
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<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
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<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>
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<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>
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<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>
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<span class="co"># Rows: 2,000</span>
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<span class="co"># Columns: 49</span>
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<span class="co"># $ date <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…</span>
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<span class="co"># $ hospital_id <fct> D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B, …</span>
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<span class="co"># $ ward_icu <lgl> FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TR…</span>
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<span class="co"># $ ward_clinical <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FA…</span>
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<span class="co"># $ ward_outpatient <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…</span>
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<span class="co"># $ age <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71…</span>
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<span class="co"># $ gender <chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M",…</span>
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<span class="co"># $ patient_id <chr> "A77334", "A77334", "067927", "067927", "067927", "067…</span>
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<span class="co"># $ mo <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPH…</span>
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<span class="co"># $ PEN <rsi> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, …</span>
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<span class="co"># $ OXA <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ FLC <rsi> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, …</span>
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<span class="co"># $ AMX <rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, …</span>
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<span class="co"># $ AMC <rsi> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, …</span>
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<span class="co"># $ AMP <rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, …</span>
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<span class="co"># $ TZP <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ CZO <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ FEP <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ CXM <rsi> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R,…</span>
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<span class="co"># $ FOX <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ CTX <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
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<span class="co"># $ CAZ <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S…</span>
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<span class="co"># $ CRO <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
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<span class="co"># $ GEN <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ TOB <rsi> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA…</span>
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<span class="co"># $ AMK <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ KAN <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ TMP <rsi> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R…</span>
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<span class="co"># $ SXT <rsi> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, …</span>
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<span class="co"># $ NIT <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ FOS <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ LNZ <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span>
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<span class="co"># $ CIP <rsi> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, …</span>
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<span class="co"># $ MFX <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ VAN <rsi> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S…</span>
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<span class="co"># $ TEC <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span>
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<span class="co"># $ TCY <rsi> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S…</span>
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<span class="co"># $ TGC <rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, …</span>
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<span class="co"># $ DOX <rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, …</span>
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<span class="co"># $ ERY <rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, …</span>
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<span class="co"># $ CLI <rsi> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, …</span>
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<span class="co"># $ AZM <rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, …</span>
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<span class="co"># $ IPM <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …</span>
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<span class="co"># $ MEM <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ MTR <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ CHL <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ COL <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R…</span>
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<span class="co"># $ MUP <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ RIF <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…</span></code></pre></div>
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<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
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<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span class="va">resistance_data</span> <span class="op"><-</span> <span class="va">example_isolates</span> <span class="op">%>%</span>
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<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>
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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">%>%</span> <span class="co"># and genus as we do here</span>
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<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">%>%</span> <span class="co"># then get resistance of all drugs</span>
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<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>,
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<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>
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<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>
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<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>
|
||
<span class="co"># <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl></span>
|
||
<span class="co"># 1 (unknown order) (unknown gen… NA NA NA NA NA NA NA NA</span>
|
||
<span class="co"># 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA</span>
|
||
<span class="co"># 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA</span>
|
||
<span class="co"># 4 Campylobacteral… Campylobacter NA NA NA NA NA NA NA NA</span>
|
||
<span class="co"># 5 Caryophanales Gemella NA NA NA NA NA NA NA NA</span>
|
||
<span class="co"># 6 Caryophanales Listeria NA NA NA NA NA NA NA NA</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" 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"><-</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>
|
||
<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>
|
||
<span class="co"># [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span>
|
||
<span class="co"># Importance of components:</span>
|
||
<span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span>
|
||
<span class="co"># Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 5.121e-17</span>
|
||
<span class="co"># Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span>
|
||
<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" 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" 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" 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">
|
||
<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>
|
||
<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" 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>
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