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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/master/vignettes/PCA.Rmd"><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"></a>Introduction</h1>
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</div>
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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"></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">
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<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://msberends.github.io/AMR">AMR</a></span>)
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<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://dplyr.tidyverse.org">dplyr</a></span>)
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<span class="fu"><a href="https://tibble.tidyverse.org/reference/glimpse.html">glimpse</a></span>(<span class="kw">example_isolates</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, T…</span>
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<span class="co"># $ ward_clinical <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, F…</span>
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<span class="co"># $ ward_outpatient <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…</span>
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<span class="co"># $ age <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 7…</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", "06…</span>
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<span class="co"># $ mo <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STP…</span>
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<span class="co"># $ PEN <ord> 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 <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ FLC <ord> 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 <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ AMC <ord> 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 <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ TZP <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ CZO <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ FEP <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ CXM <ord> 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 <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ CTX <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
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<span class="co"># $ CAZ <ord> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …</span>
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<span class="co"># $ CRO <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
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<span class="co"># $ GEN <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ TOB <ord> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…</span>
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<span class="co"># $ AMK <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ KAN <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ TMP <ord> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …</span>
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<span class="co"># $ SXT <ord> 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 <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ FOS <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ LNZ <ord> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
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<span class="co"># $ CIP <ord> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…</span>
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<span class="co"># $ MFX <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ VAN <ord> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …</span>
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<span class="co"># $ TEC <ord> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
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<span class="co"># $ TCY <ord> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …</span>
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||
<span class="co"># $ TGC <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ DOX <ord> 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 <ord> 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 <ord> NA, NA, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, NA, NA…</span>
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<span class="co"># $ AZM <ord> 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 <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
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<span class="co"># $ MEM <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ MTR <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ CHL <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ COL <ord> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span>
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<span class="co"># $ MUP <ord> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
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<span class="co"># $ RIF <ord> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
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</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">
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<span class="kw">resistance_data</span> <span class="op"><-</span> <span class="kw">example_isolates</span> <span class="op">%>%</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(order = <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span>(<span class="kw">mo</span>), <span class="co"># group on anything, like order</span>
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genus = <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="kw">mo</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">summarise_if</a></span>(<span class="kw">is.rsi</span>, <span class="kw">resistance</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">select</a></span>(<span class="kw">order</span>, <span class="kw">genus</span>, <span class="kw">AMC</span>, <span class="kw">CXM</span>, <span class="kw">CTX</span>,
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<span class="kw">CAZ</span>, <span class="kw">GEN</span>, <span class="kw">TOB</span>, <span class="kw">TMP</span>, <span class="kw">SXT</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">head</a></span>(<span class="kw">resistance_data</span>)
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<span class="co"># # A tibble: 6 x 10</span>
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<span class="co"># # Groups: order [2]</span>
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<span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span>
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<span class="co"># <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl></span>
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<span class="co"># 1 (unknown order) (unknown genu… NA NA NA NA NA NA NA NA</span>
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<span class="co"># 2 Actinomycetales Corynebacteri… NA NA NA NA NA NA NA NA</span>
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<span class="co"># 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA</span>
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<span class="co"># 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA</span>
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<span class="co"># 5 Actinomycetales Micrococcus NA NA NA NA NA NA NA NA</span>
|
||
<span class="co"># 6 Actinomycetales Rothia NA NA NA NA NA NA NA NA</span>
|
||
</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="downlit">
|
||
<span class="kw">pca_result</span> <span class="op"><-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span>(<span class="kw">resistance_data</span>)
|
||
<span class="co"># NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT.</span>
|
||
<span class="co"># Total observations available: 7.</span>
|
||
</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="downlit">
|
||
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="kw">pca_result</span>)
|
||
<span class="co"># Importance of components:</span>
|
||
<span class="co"># PC1 PC2 PC3 PC4 PC5 PC6 PC7</span>
|
||
<span class="co"># Standard deviation 2.154 1.6809 0.61305 0.33882 0.20755 0.03137 1.602e-16</span>
|
||
<span class="co"># Proportion of Variance 0.580 0.3532 0.04698 0.01435 0.00538 0.00012 0.000e+00</span>
|
||
<span class="co"># Cumulative Proportion 0.580 0.9332 0.98014 0.99449 0.99988 1.00000 1.000e+00</span>
|
||
</pre></div>
|
||
<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>
|
||
</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="downlit">
|
||
<span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span>(<span class="kw">pca_result</span>)
|
||
</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="cb6"><pre class="downlit">
|
||
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="kw">pca_result</span>)
|
||
</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="downlit">
|
||
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="kw">pca_result</span>, ellipse = <span class="fl">TRUE</span>) <span class="op">+</span>
|
||
<span class="kw">ggplot2</span>::<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span>(title = <span class="st">"An AMR/PCA biplot!"</span>)
|
||
</pre></div>
|
||
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
|
||
</div>
|
||
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|
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