P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-09-07 02:09:37 +02:00
Code Releases Activity

(v1.1.0.9021) 1st isolates update

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2020-05-28 10:51:56 +02:00
parent 86d44054f0
commit d9a4b0bcaf
51 changed files with 483 additions and 1106 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
docs/articles/PCA.html
View File

@@ -39,7 +39,7 @@
</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">1.1.0.9019</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9021</span>
</span>
</div>
@@ -186,7 +186,7 @@
<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">25 May 2020</h4>
<h4 class="date">28 May 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/PCA.Rmd"><code>vignettes/PCA.Rmd</code></a></small>
<div class="hidden name"><code>PCA.Rmd</code></div>
@@ -269,14 +269,14 @@
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="no">resistance_data</span>)
<span class="co"># # A tibble: 6 x 10</span>
<span class="co"># # Groups: order [2]</span>
<span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span>
<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 class="co"># 1 (unknown orde… Micrococcoides NA NA NA NA NA NA NA NA</span>
<span class="co"># 2 Actinomycetal… Actinomyces NA NA NA NA NA NA NA NA</span>
<span class="co"># 3 Actinomycetal… Corynebacterium NA NA NA NA NA NA NA NA</span>
<span class="co"># 4 Actinomycetal… Dermabacter NA NA NA NA NA NA NA NA</span>
<span class="co"># 5 Actinomycetal… Micrococcus NA NA NA NA NA NA NA NA</span>
<span class="co"># 6 Actinomycetal… Propionibacter… NA NA NA NA NA NA NA NA</span></pre></body></html></div>
<span class="co"># order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span>
<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 class="co"># 1 (unknown order) (unknown genu… NA NA NA NA NA NA NA NA</span>
<span class="co"># 2 Actinomycetales Corynebacteri… NA NA NA NA NA NA NA NA</span>
<span class="co"># 3 Actinomycetales Cutibacterium NA NA NA NA NA NA NA NA</span>
<span class="co"># 4 Actinomycetales Dermabacter NA NA NA NA NA NA NA NA</span>
<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></body></html></div>
</div>
<div id="perform-principal-component-analysis" class="section level1">
<h1 class="hasAnchor">
@@ -288,11 +288,11 @@
<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"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">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 2.1580 1.6783 0.61282 0.33017 0.20150 0.03190 2.123e-16</span>
<span class="co"># Proportion of Variance 0.5821 0.3521 0.04694 0.01363 0.00508 0.00013 0.000e+00</span>
<span class="co"># Cumulative Proportion 0.5821 0.9342 0.98117 0.99480 0.99987 1.00000 1.000e+00</span></pre></body></html></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>
<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></body></html></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">