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<h1 class="title toc-ignore">Example with a TCGA dataset</h1>
<h4 class="date">8 September 2014</h4>
<div id="TOC">
<ul>
<li><a href="#dendsort" id="toc-dendsort">dendsort</a></li>
<li><a href="#dendrograms" id="toc-dendrograms">dendrograms</a></li>
</ul>
</div>
<p>This is an R Markdown document, which demonstrates the use of
<strong>gapmap</strong> and <strong>dendsort</strong> packages to
generate a gapped cluster heatmap visualization.</p>
<p>Let’s start by loading the data file from the package, and creating
two color palettes.</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>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;sample_tcga&quot;</span>)</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="fu">library</span>(RColorBrewer)</span></code></pre></div>
<pre><code>## Warning: package &#39;RColorBrewer&#39; was built under R version 4.1.2</code></pre>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a>RdBu <span class="ot">=</span> <span class="fu">rev</span>(<span class="fu">brewer.pal</span>(<span class="dv">11</span>, <span class="at">name=</span><span class="st">&quot;RdBu&quot;</span>))</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a>RdYlBu <span class="ot">=</span> <span class="fu">rev</span>(<span class="fu">brewer.pal</span>(<span class="dv">11</span>, <span class="at">name=</span><span class="st">&quot;RdYlBu&quot;</span>))</span></code></pre></div>
<p>Now you have the data matrix loaded, let’s calculate
correlation-based distance and perform hierarchical clustering. In this
example, we use the correlation-based dissimilarity (Pearson
Correlation) and the complete linkage for hierarchical clustering.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="co">#transpose</span></span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a>dataTable <span class="ot">&lt;-</span> <span class="fu">t</span>(sample_tcga)</span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="co">#calculate the correlation based distance</span></span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a>row_dist <span class="ot">&lt;-</span> <span class="fu">as.dist</span>(<span class="dv">1</span><span class="sc">-</span><span class="fu">cor</span>(<span class="fu">t</span>(dataTable), <span class="at">method =</span> <span class="st">&quot;pearson&quot;</span>))</span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a>col_dist <span class="ot">&lt;-</span> <span class="fu">as.dist</span>(<span class="dv">1</span><span class="sc">-</span><span class="fu">cor</span>(dataTable, <span class="at">method =</span> <span class="st">&quot;pearson&quot;</span>))</span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a><span class="co">#hierarchical clustering</span></span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a>col_hc <span class="ot">&lt;-</span> <span class="fu">hclust</span>(col_dist, <span class="at">method =</span> <span class="st">&quot;complete&quot;</span>)</span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a>row_hc <span class="ot">&lt;-</span> <span class="fu">hclust</span>(row_dist, <span class="at">method =</span> <span class="st">&quot;complete&quot;</span>)</span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a>col_d <span class="ot">&lt;-</span> <span class="fu">as.dendrogram</span>(col_hc)</span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a>row_d <span class="ot">&lt;-</span> <span class="fu">as.dendrogram</span>(row_hc)</span></code></pre></div>
<p>Now you are ready to plot the data. First, we will plot a cluster
heatmap without any 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>(dataTable), <span class="at">d_row =</span> <span class="fu">rev</span>(row_d), <span class="at">d_col =</span> col_d, <span class="at">ratio =</span> <span class="dv">0</span>, <span class="at">verbose=</span><span class="cn">FALSE</span>, <span class="at">col=</span>RdBu,</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a> <span class="at">label_size=</span><span class="dv">2</span>, <span class="at">v_ratio=</span> <span class="fu">c</span>(<span class="fl">0.1</span>,<span class="fl">0.8</span>,<span class="fl">0.1</span>), <span class="at">h_ratio=</span><span class="fu">c</span>(<span class="fl">0.1</span>,<span class="fl">0.8</span>,<span class="fl">0.1</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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
<p>This <code>gapmap</code> package was designed to encode the
similarity between two nodes by adjusting the position of each leaves.
In the traditional representation, all the information about the
similarity two adjacent nodes is in the height of the branch in a
dendrogram. By positioning leaves based on the similarity, we introduce
gaps in both dendrograms and heat map visualization. In the figure
below, we exponentially map a distance (dissimilarity) of two nodes to a
scale of gap size.</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>(dataTable), <span class="at">d_row =</span> <span class="fu">rev</span>(row_d), <span class="at">d_col =</span> col_d, <span class="at">mode =</span> <span class="st">&quot;quantitative&quot;</span>, <span class="at">mapping=</span><span class="st">&quot;exponential&quot;</span>, <span class="at">col=</span>RdBu,</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a> <span class="at">ratio =</span> <span class="fl">0.3</span>, <span class="at">verbose=</span><span class="cn">FALSE</span>, <span class="at">scale =</span> <span class="fl">0.5</span>, <span class="at">label_size=</span><span class="dv">2</span>, <span class="at">v_ratio=</span> <span class="fu">c</span>(<span class="fl">0.1</span>,<span class="fl">0.8</span>,<span class="fl">0.1</span>), <span class="at">h_ratio=</span><span class="fu">c</span>(<span class="fl">0.1</span>,<span class="fl">0.8</span>,<span class="fl">0.1</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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
<p>Since the background is white, we can use another color scale where
the value 0 is encoded in yellow.</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">gapmap</span>(<span class="at">m =</span> <span class="fu">as.matrix</span>(dataTable), <span class="at">d_row =</span> <span class="fu">rev</span>(row_d), <span class="at">d_col =</span> col_d, <span class="at">mode =</span> <span class="st">&quot;quantitative&quot;</span>, <span class="at">mapping=</span><span class="st">&quot;exponential&quot;</span>, <span class="at">col=</span>RdYlBu,</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a> <span class="at">ratio =</span> <span class="fl">0.3</span>, <span class="at">verbose=</span><span class="cn">FALSE</span>, <span class="at">scale =</span> <span class="fl">0.5</span>, <span class="at">label_size=</span><span class="dv">2</span>, <span class="at">v_ratio=</span> <span class="fu">c</span>(<span class="fl">0.1</span>,<span class="fl">0.8</span>,<span class="fl">0.1</span>), <span class="at">h_ratio=</span><span class="fu">c</span>(<span class="fl">0.1</span>,<span class="fl">0.8</span>,<span class="fl">0.1</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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
<div id="dendsort" class="section level2">
<h2>dendsort</h2>
<p>This package works well with the <strong>dendsort</strong> package to
reorder the structure of dendrograms. For further information for the
<strong>dendsort</strong>, please see the <a href="https://f1000research.com/articles/3-177/v1">paper</a>.</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">library</span>(dendsort)</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="fu">gapmap</span>(<span class="at">m =</span> <span class="fu">as.matrix</span>(dataTable), <span class="at">d_row =</span> <span class="fu">rev</span>(<span class="fu">dendsort</span>(row_d, <span class="at">type =</span> <span class="st">&quot;average&quot;</span>)), <span class="at">d_col =</span> <span class="fu">dendsort</span>(col_d, <span class="at">type =</span> <span class="st">&quot;average&quot;</span>), </span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a> <span class="at">mode =</span> <span class="st">&quot;quantitative&quot;</span>, <span class="at">mapping=</span><span class="st">&quot;exponential&quot;</span>, <span class="at">ratio =</span> <span class="fl">0.3</span>, <span class="at">verbose=</span><span class="cn">FALSE</span>, <span class="at">scale =</span> <span class="fl">0.5</span>, <span class="at">v_ratio=</span> <span class="fu">c</span>(<span class="fl">0.1</span>,<span class="fl">0.8</span>,<span class="fl">0.1</span>), </span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a> <span class="at">h_ratio=</span><span class="fu">c</span>(<span class="fl">0.1</span>,<span class="fl">0.8</span>,<span class="fl">0.1</span>), <span class="at">label_size=</span><span class="dv">2</span>, <span class="at">show_legend=</span><span class="cn">TRUE</span>, <span class="at">col=</span>RdBu)</span></code></pre></div>
<p><img src="data:image/png;base64,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
</div>
<div id="dendrograms" class="section level2">
<h2>dendrograms</h2>
<p>You can also plot gapped dendrogram. First you need to create a
<code>gapdata</code> class object by calling <code>gap_data()</code>. To
bring the text labels closer to the dendrogram, we set a negative value
to <code>axis.tick.margin</code>. This value should be adjusted
depending on your plot size. If anyone has a better solution to adjust
the position of the axis labels, please let me know.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>row_data <span class="ot">&lt;-</span> <span class="fu">gap_data</span>(<span class="at">d=</span> row_d, <span class="at">mode =</span> <span class="st">&quot;quantitative&quot;</span>, <span class="at">mapping=</span><span class="st">&quot;exponential&quot;</span>, <span class="at">ratio=</span><span class="fl">0.3</span>, <span class="at">scale=</span> <span class="fl">0.5</span>)</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>dend <span class="ot">&lt;-</span> <span class="fu">gap_dendrogram</span>(<span class="at">data =</span> row_data, <span class="at">leaf_labels =</span> <span class="cn">TRUE</span>, <span class="at">rotate_label =</span> <span class="cn">TRUE</span>)</span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>dend <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.ticks.length=</span> grid<span class="sc">::</span><span class="fu">unit</span>(<span class="dv">0</span>,<span class="st">&quot;lines&quot;</span>) )<span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.ticks.margin =</span> grid<span class="sc">::</span><span class="fu">unit</span>(<span class="sc">-</span><span class="fl">0.8</span>, <span class="st">&quot;lines&quot;</span>))</span></code></pre></div>
<pre><code>## Warning: The `axis.ticks.margin` argument of `theme()` is deprecated as of ggplot2
## 2.0.0.
## ℹ Please set `margin` property of `axis.text` instead
## 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,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0HmBzF8fYbEBlEENkgkgAhMkZkEQQWmJyDhTEYDBhEtgEBRiIKySaDSTYgMgiQMAabjIgiR5ODSAKJDBLReL/61Z/ab25u725Pt3u3e/fW82yYmZ4Ob1dXV1dXd09TMEoVoFNOOSUNHz48ffHFFxWITVEIASEgBISAEBACQkAIdFYEpu2sBVO5hIAQEAJCQAgIASEgBGoTASmgtVkvypUQEAJCQAgIASEgBDotAlJAO23VqmBCQAgIASEgBISAEKhNBKSA1ma9KFdCQAgIASEgBISAEOi0CBQV0Oeeey7dcMMNacqUKU0W9ttvv0233HJLuvXWW9P333/fZDg9EAJCQAgIASEgBISAEBACTSHgCugmm2ySBg8enJ544om00korpTvuuKNReBTTFVdc0ZXUYcOGpc033zxVaAF9o7R0QwgIASEgBISAEBACQqDzItDtkUceSe+//3564YUXvJSrrbZaOu2009KAAQMalPr0009Pm266aTr77LP9/lprrZVuv/12v9cgoC6EgBAQAkJACAgBISAEhEAzCHRbY4010qOPPloM8vHHH5echn/22WfTwIEDi+E23HDDNG7cOCmgRUT0RwhUF4H//e9/6YQTTqhuIoq9rhGYbbbZ0uTJk+u6DMp8dRFYe+21GxmYqpuiYhcCpRHoNu2006ZZZ53Vn7733nuJDeUvuuiiRqHHjx+fevToUbw/99xzp9dff714rT9CQAhUF4FBgwalyy67rNheq5uaYq9HBJZZZpn0yiuv1GPWled2QODHH39MF198cRozZkzq27dvO6SoJIRA0wh0i0cvvvhi2mKLLdKQIUNKWjWnn3769N///jeCpx9++CEx2hYJASHQPgi89tpr6Re/+EW6+eab2ydBpSIEhECnQuDll19Oyy67bPryyy87VblUmPpEwBXQxx9/PG2zzTbpnHPOSdttt13Jkiy00ELpww8/LD7j/9JLL1281h8hIASEgBAQAkJACAgBIVAOAtNOmDDBLZ9XX311I+WTZx988IHHg4I6cuTI9PXXXyfusxUTfqAiISAEhIAQEAJCQAgIASHQGgSmPfPMM9OkSZNS//7903TTTecfrJ3Q8ccfn9hyCdpll11S9+7d3erJCvgDDzww9enTx5/pSwgIASEgBISAEBACQkAIlItAtxEjRiQ+pejCCy8s3sYHdNSoUenzzz93389u3Yruo8Uw+iMEhIAQEAJCQAgIASEgBFpCoNVa5JxzztlSnHouBISAEBACQkAICAEhIASaRKB4FGeTIfRACAgBISAEhIAQEAJCQAhUEAEpoBUEU1EJASEgBISAEBACQkAItIyAFNCWMVIIISAEhIAQEAJCQAgIgQoiIAW0gmAqKiEgBISAEBACQkAICIGWEZAC2jJGCiEEhIAQEAJCQAgIASFQQQSkgFYQTEUlBISAEBACQkAICAEh0DICUkBbxkghhIAQEAJCQAgIASEgBCqIgBTQCoKpqISAEBACQkAICAEhIARaRkAKaMsYKYQQEAJCQAgIASEgBIRABRGQAlpBMBWVEBACQkAICAEhIASEQMsISAFtGSOFEAJCQAgIASEgBISAEKggAlJAKwimohICQkAICAEhIASEgBBoGQEpoC1jpBBCQAgIASEgBISAEBACFURACmgFwVRUQkAICAEhIASEgBAQAi0jIAW0ZYwUQggIASEgBISAEBACQqCCCEgBrSCYikoICAEhIASEgBAQAkKgZQSkgLaMkUIIASEgBISAEBACQkAIVBABKaAVBFNRCQEhIASEgBAQAkJACLSMQLeWgyiEEBACQkAICIHaQ+D7779PN9xwQ+1lrEZzNGHCBM/Z3XffnSZOnFijuay9bG2++eZpjjnmqL2M1XmOpIDWeQUq+0JACAiBrorAcccdl4YPH95Viz9V5e7du3caNmzYVL3bVV8aOHBguvLKK7tq8atWbimgVYNWEQsBISAEhEA1EbjvvvvSDjvskC688MJqJtOp4p5mmmlSoVDoVGWqZmGGDh2aRo8eXc0kumzcUkC7bNWr4EJACAiB+kYAZWrGGWdMc889d30XRLmvWQRmnnnmBJ+JKo+AFiFVHlPFKASEgBAQAkJACAgBIdAMAlJAmwFHj4SAEBACQkAICAEhIAQqj4AU0MpjqhiFgBAQAkJACAgBISAEmkFACmgz4OiREBACQkAICAEhIASEQOURkAJaeUwVoxAQAkJACAgBISAEhEAzCEgBbQYcPRICQkAICAEhIASEgBCoPAJSQCuPqWIUAkJACAgBISAEhIAQaAYBKaDNgKNHQkAICAEhIASEgBAQApVHQApo5TFVjEJACAgBISAEhIAQEALNICAFtBlw9EgICAEhIASEgBAQAkKg8ghIAa08popRCAgBISAEhIAQEAJCoBkEpIA2A44eCQEhIASEgBAQAkJACFQeASmglcdUMQoBISAEhIAQEAJCQAg0g0C37LMnnngizTrrrGnZZZfN3i7+Hzt2bPrmm2+K
<p>Here, we can also apply <code>dendsort</code>.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>row_data <span class="ot">&lt;-</span> <span class="fu">gap_data</span>(<span class="at">d=</span> <span class="fu">dendsort</span>(row_d, <span class="at">type =</span> <span class="st">&quot;average&quot;</span>), <span class="at">mode =</span> <span class="st">&quot;quantitative&quot;</span>, <span class="at">mapping=</span><span class="st">&quot;exponential&quot;</span>, <span class="at">ratio=</span><span class="fl">0.3</span>, <span class="at">scale=</span> <span class="fl">0.5</span>)</span>
<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a>dend <span class="ot">&lt;-</span> <span class="fu">gap_dendrogram</span>(<span class="at">data =</span> row_data, <span class="at">leaf_labels =</span> <span class="cn">TRUE</span>, <span class="at">rotate_label =</span> <span class="cn">TRUE</span>)</span>
<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a>dend <span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.ticks.length=</span> grid<span class="sc">::</span><span class="fu">unit</span>(<span class="dv">0</span>,<span class="st">&quot;lines&quot;</span>) )<span class="sc">+</span> <span class="fu">theme</span>(<span class="at">axis.ticks.margin =</span> grid<span class="sc">::</span><span class="fu">unit</span>(<span class="sc">-</span><span class="fl">0.8</span>, <span class="st">&quot;lines&quot;</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7J0FnBxF9seLEDQkQJDg7u4QPMgR3OV/4dDD7YAj+KEHlxx2yOESJBAcQnDXAMHtDgIEDW7BCcz/fd/dm+vtnd2dyc7uzuz+3uezO9M93dVV36p69arqVfVEBZNUBTnppJPSoEGD0tdff12F0BSECIiACIiACIiACIhAZyXQrbMmTOkSAREQAREQAREQARGoTQIyQGszXxQrERABERABERABEei0BGSAdtqsVcJEQAREQAREQAREoDYJyACtzXxRrERABERABERABESg0xIoGqAvvvhiuv7669N3333XZGJ//PHHNHz48DRixIj0888/N3mdfhABERABERABERABERCBpgi4Abreeuulww8/PI0aNSotueSS6e677250PYbpEkss4UbqySefnDbccMNUpQX0jZ6lEyIgAiIgAiIgAiIgAp2XQPcnnngiffDBB+nll1/2VC633HLp1FNPTb/73e8apPq0005L/fv3T2eeeaaf79u3b7rrrrv8XIMLdSACIiACIiACIiACIiACzRDovuKKK6Ynn3yyeMlnn31Wchr+hRdeSAMGDChe169fvzRy5EgZoEUi+iICIgAB9gI+/fTTBUMEnED37t3T+PHjRUMEnMDEE0+cjj76aNEQgdS9W7duqUePHo7i/fffT2wof8EFFzRCM2bMmDTddNMVz/fu3TuNHj26eKwvIiACIgCBHXbYId17771pyimnFBARSD179kzjxo0TCRFwAt9//713Uk855RQR6eIEukf6X3311bTRRhulY445puSo5iSTTNKgF/vLL7+kqaaaKm7XpwiIgAg4gTfffNON0HPPPVdEREAERKABAdz73njjjQbndNA1CbgB+vTTT6fNNtssnXXWWWmLLbYoSWKWWWZJH330UfE3vi+wwALFY30RAREQAREQAREQAREQgXIIdPvwww995HPo0KGNjE9+Gzt2rIeDgTpkyJDE8Dnn2YoJP1CJCIiACIiACIiACIiACFRCoNsZZ5yRPvnkk7TWWmslnIP5Y7QTOe644xJbLiHbbbdd6tWrl496sgJ+v/32S4sssoj/pn8iIAIiIAIiIAIiIAIiUC6B7oMHD078lZLzzz+/eBof0Ouuuy599dVX7vvJykaJCIiACIiACIiACIiACFRKoGIrcppppqn0GbpeBERABERABERABERABIoEiq/iLJ7RFxEQAREQAREQAREQARFoQwIyQNsQroIWAREQAREQAREQARFoTEAGaGMmOiMCIiACIiACIiACItCGBGSAtiFcBS0CIiACIiACIiACItCYgAzQxkx0RgREQAREQAREQAREoA0JyABtQ7gKWgREQAREQAREQAREoDEBGaCNmeiMCIiACIiACIiACIhAGxKQAdqGcBW0CIiACIiACIiACIhAYwIyQBsz0RkREAEREAEREAEREIE2JCADtA3hKmgREAEREAEREAEREIHGBGSANmaiMyIgAiIgAiIgAiIgAm1IQAZoG8JV0CIgAiIgAiIgAiIgAo0JyABtzERnREAEREAEREAEREAE2pCADNA2hKugRUAEREAEREAEREAEGhOQAdqYic6IgAiIgAiIgAiIgAi0IQEZoG0IV0GLgAiIgAiIgAiIgAg0JiADtDETnREBERABERABERABEWhDAjJA2xCughYBERABERABERABEWhMoHvjUzojAiIgAiIgAiJQiwReffXV9Pzzz9di1MqK09ixY9Okk06ahg4dWtb1tXjRsssumxZccMFajFpdxUkGaF1llyIrAiIgAiLQVQm89957acCAAXVtgE400USpUCh4Ouo1HzFAR4wYkfr06VOvSaiJeMsArYlsUCREQAREQAREoHkCX375pRuft912W+rbt2/zF9for2GA1mj0WozW/fffn7beeuv0zTffyABtkVbzF8gAbZ6PfhUBERABERCBmiLQq1ev1Lt375qKU1eJDOwl1SGgRUjV4ahQREAEREAEREAEREAEyiQgA7RMULpMBERABERABERABESgOgRkgFaHo0IRAREQAREQAREQAREok4AM0DJB6TIREAEREAEREAEREIHqEJABWh2OCkUEREAEREAEREAERKBMAjJAywSly0RABERABERABERABKpDQAZodTgqFBEQAREQAREQAREQgTIJyAAtE5QuEwEREAEREAEREAERqA4BGaDV4ahQREAEREAEREAEREAEyiQgA7RMULpMBERABERABERABESgOgRkgFaHo0IRAREQAREQAREQAREok4AM0DJB6TIREAEREAEREAEREIHqEJABWh2OCkUEREAEREAEREAERKBMAt2z140aNSr16NEjLbzwwtnTxe8PPfRQ+uGHH4rHSy+9dOrTp0/xWF9EQAREQAREQAREQAREoCUCRQP01VdfTf3790+nnnpqSQP0l19+SRtssEFaa621imEefvjhMkCLNPRFBERABERABERABESgHAJugJ5//vnp+OOPT9NNN12T92Cgzj///Gn48OFN
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