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<title>An introduction to the gapmap package</title>
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<h1 class="title toc-ignore">An introduction to the gapmap package</h1>
<h4 class="date">2 September 2014</h4>
<p>This document explains basic functions of the <code>gapmap</code>
package to draw a gapped cluster heatmap. The plot is generated using
the <code>ggplot2</code> package. Let’s load the library first.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(gapmap)</span></code></pre></div>
<p>We will simulate a simple dataset.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1234</span>)</span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">rnorm</span>(<span class="dv">10</span>, <span class="at">mean=</span><span class="fu">rep</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>, <span class="at">each=</span><span class="dv">2</span>), <span class="at">sd=</span><span class="fl">0.4</span>)</span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a>y <span class="ot">&lt;-</span> <span class="fu">rnorm</span>(<span class="dv">10</span>, <span class="at">mean=</span><span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>), <span class="at">each=</span><span class="dv">5</span>), <span class="at">sd=</span><span class="fl">0.4</span>)</span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a>dataFrame <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">x=</span>x, <span class="at">y=</span>y, <span class="at">row.names=</span><span class="fu">c</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>))</span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a><span class="co">#calculate distance matrix. default is Euclidean distance</span></span>
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a>distxy <span class="ot">&lt;-</span> <span class="fu">dist</span>(dataFrame)</span>
<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a><span class="co">#perform hierarchical clustering. default is complete linkage.</span></span>
<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a>hc <span class="ot">&lt;-</span> <span class="fu">hclust</span>(distxy)</span>
<span id="cb2-9"><a href="#cb2-9" tabindex="-1"></a>dend <span class="ot">&lt;-</span> <span class="fu">as.dendrogram</span>(hc)</span></code></pre></div>
<p>To make a gapped cluster heatmap, you need to pass a
<code>matrix</code> object for heatmap, and <code>dendrogram</code>
class objects for drawing dendrograms and ordering.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>grey_scale <span class="ot">=</span><span class="fu">c</span>(<span class="st">&quot;#333333&quot;</span>, <span class="st">&quot;#5C5C5C&quot;</span>, <span class="st">&quot;#757575&quot;</span>, <span class="st">&quot;#8A8A8A&quot;</span>, <span class="st">&quot;#9B9B9B&quot;</span>, <span class="st">&quot;#AAAAAA&quot;</span>, <span class="st">&quot;#B8B8B8&quot;</span>, <span class="st">&quot;#C5C5C5&quot;</span>, <span class="st">&quot;#D0D0D0&quot;</span>, <span class="st">&quot;#DBDBDB&quot;</span>, <span class="st">&quot;#E6E6E6&quot;</span>)</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">gapmap</span>(<span class="at">m =</span> <span class="fu">as.matrix</span>(distxy), <span class="at">d_row=</span> <span class="fu">rev</span>(dend), <span class="at">d_col=</span>dend, <span class="at">col =</span> grey_scale)</span></code></pre></div>
<pre><code>## Warning: The `panel.margin` argument of `theme()` is deprecated as of ggplot2 2.2.0.
## ℹ Please use the `panel.spacing` argument instead.
## ℹ The deprecated feature was likely used in the gapmap package.
## Please report the issue at
## &lt;https://github.com/evanbiederstedt/gapmap/issues&gt;.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.</code></pre>
<p><img src="data:image/png;base64,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
<p>The default of <code>gapmap</code> function is in the
<strong>quantitative</strong> mode and uses <strong>exponential</strong>
mapping. First, you can choose either modes:
<strong>quantitative</strong> or <strong>threshold</strong>.</p>
<ul>
<li><strong>quantitative</strong>: the size of gap is quantitatively
mapped to the distance/height at which two nodes merges in hierarchical
clustering. For mapping the distance, you have 2 options:
<strong>linear</strong> or <strong>exponential</strong> mapping.
<ul>
<li><strong>linear</strong>: The distance and the size of a gap is
linearly mapped.</li>
<li><strong>exponential</strong>: The distance and the size of a gap is
linearly mapped. The scale log base can be set with
<strong>scale</strong> attribute. The default scale is set to 0.5.</li>
</ul></li>
<li><strong>threshold</strong>: the gaps are introduced based on the
“cutting the tree” method. By setting <strong>threshold</strong>
attributes, or alternatively <strong>row_threshold</strong> and
<strong>col_threshold</strong>, you can divide into clusters and
introduce the gaps between clusters.</li>
</ul>
<p>The following example uses the linear mapping. This mapping generate
more gaps, whereas the previous example of exponential mapping emphasize
on the large gaps.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">gapmap</span>(<span class="at">m =</span> <span class="fu">as.matrix</span>(distxy), <span class="at">d_row=</span> <span class="fu">rev</span>(dend), <span class="at">d_col=</span>dend, <span class="at">mode =</span> <span class="st">&quot;quantitative&quot;</span>, <span class="at">mapping=</span><span class="st">&quot;linear&quot;</span>, <span class="at">col =</span> grey_scale)</span></code></pre></div>
<p><img src="data:image/png;base64,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
<p>The following example illustrate the difference of two mapping
schemes. For the exponential mapping, the scale log base is set to 0.5.
<img src="data:image/png;base64,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
<p>The variation of scale log base settings is illustrated in the
following plot. The value of scale is annotated on the plot. <img src="data:image/png;base64,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
<p>Besides the <strong>quantitative</strong> mode, there is
<strong>linear</strong> mode to introduce gap by a threshold. In the
following example, the dendrograms for rows and columns are cut at the
threshold distance of 2 and gaps of the same size are introduced between
clusters.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">gapmap</span>(<span class="at">m =</span> <span class="fu">as.matrix</span>(distxy), <span class="at">d_row=</span> <span class="fu">rev</span>(dend), <span class="at">d_col=</span>dend, <span class="at">mode =</span> <span class="st">&quot;threshold&quot;</span>, <span class="at">row_threshold =</span> <span class="dv">2</span>, <span class="at">col_threshold =</span> <span class="dv">2</span>, <span class="at">col =</span> grey_scale)</span></code></pre></div>
<p><img src="data:image/png;base64,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
<p>In addition, this package works well with our dendrogram sorting
package, called <code>dendsort</code>. For the details on dendsort,
please check our <a href="https://f1000research.com/articles/3-177/v1">paper</a>.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="fu">library</span>(dendsort);</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="fu">gapmap</span>(<span class="at">m =</span> <span class="fu">as.matrix</span>(distxy), <span class="at">d_row=</span> <span class="fu">rev</span>(<span class="fu">dendsort</span>(dend)), <span class="at">d_col=</span><span class="fu">dendsort</span>(dend), <span class="at">mode =</span> <span class="st">&quot;quantitative&quot;</span>, <span class="at">col =</span> grey_scale)</span></code></pre></div>
<p><img src="data:image/png;base64,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