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<title>Intertwining phylogenetic trees and networks: R Example Script</title>



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<h1 class="title toc-ignore">Intertwining phylogenetic trees and networks: R Example Script</h1>
<h4 class="author"><em>Klaus Schliep, Alastair Potts, David Morrison and Guido Grimm</em></h4>
<h4 class="date"><em>February 14, 2018</em></h4>



<p><em>Description:</em> This script provides examples of the new functions available in the phangorn library to ‘intertwine’ trees and networks, i.e. compare trees and networks and data transferrance. It also provides a step-by-step guide for users new to R.</p>
<p><em>Methodological advancement:</em> The major advancement in this phangorn update is the introduction of a generic network object with a wide range of related transfer and analysis functions. These new functions provide the first means to directly transfer information amongst a wide range of phylogenetic trees and networks, as well as means to visualise and further analyse this information. This should provide a platform for individuals to easily conduct tree-network comparisons and stimulate further function development by the community.</p>
<p><em>What next?:</em> By implementing full network handling compatibility in R, and providing exemplar scripts (such as this) and <a href="https://github.com/KlausVigo/phangorn">support</a>, the scientific community now has an easy means to analyse and compare the results of phylogenetic trees vs. network approaches. We hope this will open a largely unexplored world to the general phylogenetic audience.</p>
<div id="installing-r" class="section level3">
<h3>Installing R</h3>
<ol style="list-style-type: decimal">
<li>Download R<br />
Select the nearest mirror to your location at <a href="https://cran.r-project.org/mirrors.html" class="uri">https://cran.r-project.org/mirrors.html</a><br />
</li>
<li>Select your operating system and download the relevant installation file.<br />
</li>
<li>Install R following the instructions.</li>
</ol>
</div>
<div id="installing-the-phangorn-library" class="section level3">
<h3>Installing the phangorn library</h3>
<p>Open R and run the two lines of code below in the command line.<br />
(You will need to select a region from which to download the library)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">install.packages</span>(<span class="st">&quot;phangorn&quot;</span>, <span class="dt">dependencies=</span><span class="ot">TRUE</span>)
<span class="co"># install latest development version needs devtools</span>
<span class="kw">install.packages</span>(<span class="st">&quot;devtools&quot;</span>, <span class="dt">dependencies=</span><span class="ot">TRUE</span>)
<span class="kw">library</span>(devtools)
<span class="kw">install_github</span>(<span class="st">&quot;KlausVigo/phangorn&quot;</span>)</code></pre></div>
</div>
<div id="getting-started" class="section level3">
<h3>Getting started</h3>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(phangorn)    <span class="co"># load the phangorn library</span>
<span class="kw">library</span>(magrittr)  </code></pre></div>
</div>
<div id="set-the-working-directory" class="section level3">
<h3>Set the working directory</h3>
<p>This is often a major stumbling block for new R users. You need to specify where in your folder structure you wish to work. i.e, where are the files stored that you wish to input?</p>
<p>This is done using the <code>setwd()</code> function, e.g. <code>setwd(&quot;C:/TreesNetworks/Example Files&quot;)</code>.</p>
<p>We now set it to the folder of the phangorn package, which contains the files we want to load for this example.</p>
</div>
<div id="read-in-the-example-file-datasets" class="section level3">
<h3>Read in the example file datasets:</h3>
<p>This example files are based on the woodmouse dataset available in the ape library. Ultimately, this dataset is from this study: Michaux, J. R., Magnanou, E., Paradis, E., Nieberding, C. and Libois, R. (2003) Mitochondrial phylogeography of the Woodmouse (<em>Apodemus sylvaticus</em>) in the Western Palearctic region. Molecular Ecology, 12, 685-697.)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## automatically set the correct working directory for the examples below
<span class="co"># setwd(system.file(&quot;extdata/trees&quot;, package = &quot;phangorn&quot;))</span>
<span class="co"># for this vignette we create a path to the files we want to load </span>
fdir &lt;-<span class="st"> </span><span class="kw">system.file</span>(<span class="st">&quot;extdata/trees&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;phangorn&quot;</span>)
## in your case it may look something like this...
<span class="co"># setwd(&quot;C:/TreesNetworks/Example Files&quot;)</span>

##DNA Matrix, maybe not needed 
woodmouse &lt;-<span class="st"> </span><span class="kw">read.phyDat</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;woodmouse.fasta&quot;</span>),<span class="dt">format=</span><span class="st">&quot;fasta&quot;</span>) 
## RAxML best-known tree with bipartition support (from previous analysis)
raxml.tree &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;RAxML_bipartitions.woodmouse&quot;</span>))
## RAxML bootstrap trees (from previous analysis)
raxml.bootstrap &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;RAxML_bootstrap.woodmouse&quot;</span>))
## MrBayes consensus tree (50% majority rule) (from previous analysis)
mrbayes.tree &lt;-<span class="st"> </span><span class="kw">read.nexus</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;woodmouse.mrbayes.nex.con&quot;</span>))
## MrBayes sample runs 1 and 2 (from previous analysis)
run1 &lt;-<span class="st"> </span><span class="kw">read.nexus</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;woodmouse.mrbayes.nex.run1.t&quot;</span>))
run2 &lt;-<span class="st"> </span><span class="kw">read.nexus</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;woodmouse.mrbayes.nex.run2.t&quot;</span>))
## How many trees are in the MrBayes tree sample?
run1</code></pre></div>
<pre><code>## 1001 phylogenetic trees</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">run2</code></pre></div>
<pre><code>## 1001 phylogenetic trees</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Combining the two runs and removing 25% burn-in
mrbayes.trees &lt;-<span class="st"> </span><span class="kw">c</span>(run1[<span class="dv">251</span><span class="op">:</span><span class="dv">1001</span>],run2[<span class="dv">251</span><span class="op">:</span><span class="dv">1001</span>])
## NeigbourNet Nexus file generated by SplitsTree (from previous analysis)
Nnet &lt;-<span class="st"> </span><span class="kw">read.nexus.networx</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;woodmouse.nxs&quot;</span>))</code></pre></div>
<p>All example files read into R.</p>
</div>
<div id="viewing-the-data" class="section level3">
<h3>Viewing the data</h3>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mfrow=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">2</span>), <span class="dt">mar=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>)) <span class="co"># Setting plot parameters</span>
### Plotting trees with support values:
##  RAxML
<span class="kw">plot</span>(raxml.tree)
<span class="kw">nodelabels</span>(raxml.tree<span class="op">$</span>node.label, <span class="dt">adj =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">0</span>), <span class="dt">frame =</span> <span class="st">&quot;none&quot;</span>)
##  MrBayes
<span class="kw">plot</span>(mrbayes.tree)
<span class="kw">nodelabels</span>(mrbayes.tree<span class="op">$</span>node.label, <span class="dt">adj =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">0</span>), <span class="dt">frame =</span> <span class="st">&quot;none&quot;</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mfrow=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>)) <span class="co"># Setting plot parameters</span>
<span class="co"># NeighbourNet</span>
<span class="kw">plot</span>(Nnet,<span class="st">&quot;2D&quot;</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## alternatively,
<span class="co"># plot(Nnet,&quot;2D&quot;)</span></code></pre></div>
</div>
<div id="interwining-trees-and-network-functions" class="section level3">
<h3>Interwining trees and network functions</h3>
</div>
<div id="a" class="section level3">
<h3>1A:</h3>
<p>Identification of edge bundles (in black) in a neighbour-net (NN) network that correspond to branches (labelled 1-12) in a tree (a maximum likelihood tree, in this case).</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># create a vector of labels for the network corresponding to edges in the tree</span>
edge.lab &lt;-<span class="st"> </span><span class="kw">createLabel</span>(Nnet, raxml.tree, raxml.tree<span class="op">$</span>edge[,<span class="dv">2</span>], <span class="st">&quot;edge&quot;</span>)
<span class="co"># could be also 1:27 instead of raxml.tree$edge[,2]</span>

<span class="co"># Show the correspondingly labelled tree and network in R</span>
<span class="kw">par</span>(<span class="dt">mfrow=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">2</span>))  
<span class="kw">plot</span>(raxml.tree, <span class="st">&quot;u&quot;</span>, <span class="dt">rotate.tree =</span> <span class="dv">180</span>, <span class="dt">cex=</span>.<span class="dv">7</span>) 
<span class="kw">edgelabels</span>(raxml.tree<span class="op">$</span>edge[,<span class="dv">2</span>],<span class="dt">col=</span><span class="st">&quot;blue&quot;</span>, <span class="dt">frame=</span><span class="st">&quot;none&quot;</span>, <span class="dt">cex=</span>.<span class="dv">7</span>)

<span class="co"># find edges that are in the network but not in the tree</span>
edge.col &lt;-<span class="st"> </span><span class="kw">rep</span>(<span class="st">&quot;black&quot;</span>, <span class="kw">nrow</span>(Nnet<span class="op">$</span>edge))
edge.col[ <span class="kw">is.na</span>(edge.lab) ] &lt;-<span class="st"> &quot;red&quot;</span>
<span class="co"># or a simpler alternative...</span>
edge.col &lt;-<span class="st"> </span><span class="kw">createLabel</span>(Nnet, raxml.tree, <span class="st">&quot;black&quot;</span>, <span class="dt">nomatch=</span><span class="st">&quot;red&quot;</span>)

x &lt;-<span class="st"> </span><span class="kw">plot</span>(Nnet, <span class="dt">edge.label =</span> edge.lab, <span class="dt">show.edge.label =</span> T, <span class="st">&quot;2D&quot;</span>, <span class="dt">edge.color =</span> edge.col,
                  <span class="dt">col.edge.label =</span> <span class="st">&quot;blue&quot;</span>, <span class="dt">cex=</span>.<span class="dv">7</span>)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># the above plot function returns an invisible networx object and this object also  </span>
<span class="co"># contains the colors for the edges.</span></code></pre></div>
</div>
<div id="b" class="section level3">
<h3>1B:</h3>
<p>Bootstrap support for all branches (branch labels) in the ML tree mapped on the corresponding edge bundles of the NN network. The edges in the network which are not found as ML tree branches are highlighted in red.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># the scaler argument multiplies the confidence values. This is useful to switch</span>
<span class="co"># confidences values between total, percentage or ratios.   </span>
x &lt;-<span class="st"> </span><span class="kw">addConfidences</span>(Nnet,raxml.tree, <span class="dt">scaler =</span> .<span class="dv">01</span>)
<span class="co"># find splits that are in the network but not in the tree</span>
split.col &lt;-<span class="st"> </span><span class="kw">rep</span>(<span class="st">&quot;black&quot;</span>, <span class="kw">length</span>(x<span class="op">$</span>splits))
split.col[ <span class="op">!</span><span class="kw">matchSplits</span>(<span class="kw">as.splits</span>(x), <span class="kw">as.splits</span>(raxml.tree)) ] &lt;-<span class="st"> &quot;red&quot;</span>

<span class="co"># simpler alternative...</span>
split.col2 &lt;-<span class="st"> </span><span class="kw">createLabel</span>(x, raxml.tree, <span class="dt">label=</span><span class="st">&quot;black&quot;</span>, <span class="st">&quot;split&quot;</span>, <span class="dt">nomatch=</span><span class="st">&quot;red&quot;</span>)

<span class="co"># Plotting in R</span>
out.x &lt;-<span class="st"> </span><span class="kw">plot</span>(x,<span class="st">&quot;2D&quot;</span>,<span class="dt">show.edge.label=</span><span class="ot">TRUE</span>, <span class="dt">split.color=</span>split.col, <span class="dt">col.edge.label =</span> <span class="st">&quot;blue&quot;</span>)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Again we can write to SplitsTree for viewing…</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># write.nexus.networx(out.x,&quot;woodmouse.tree.support.nxs&quot;)</span>
## or we can also export the splits alone (for usage in software other than SplitsTree)
<span class="co"># write.nexus.splits(as.splits(out.x),&quot;woodmouse.splits.support.nxs&quot;)</span></code></pre></div>
</div>
<div id="c" class="section level3">
<h3>1C:</h3>
<p>Frequencies of bipartitions found in the bootstrap pseudoreplicates mapped on the corresponding edge bundles of the NN network using a threshold of 10% (i.e. any edge is labelled that occurs in at least 100 of the 1000 ML-BS pseudoreplicates). Edge bundles not found in the ML tree are labelled using grey edges.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">y &lt;-<span class="st"> </span><span class="kw">addConfidences</span>(Nnet, <span class="kw">as.splits</span>(raxml.bootstrap))
edge.col &lt;-<span class="st"> </span><span class="kw">createLabel</span>(y, raxml.tree, <span class="dt">label=</span><span class="st">&quot;black&quot;</span>, <span class="st">&quot;edge&quot;</span>, <span class="dt">nomatch=</span><span class="st">&quot;grey&quot;</span>)

y &lt;-<span class="st"> </span><span class="kw">plot</span>(y,<span class="st">&quot;2D&quot;</span>,<span class="dt">show.edge.label=</span><span class="ot">TRUE</span>, <span class="dt">edge.color=</span>edge.col)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Write to SplitsTree for viewing
<span class="co"># write.nexus.networx(y,&quot;NN.with.bs.support.nxs&quot;)</span></code></pre></div>
</div>
<div id="extras" class="section level3">
<h3>Extras…</h3>
<p>We can also compare the neighborNet with a consensusNet (Holland BR, Huber KT, Moulton V, Lockhart PJ,2004, Using consensus networks to visualize contradictory evidence for species phylogeny. Molecular Biology and Evolution, 21, 1459-1461). Furthermore, we can extract the support values from the consensus network, and place these on to the NeighbourNet (this is similar to the process explained in 1C above).</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cnet &lt;-<span class="st"> </span><span class="kw">consensusNet</span>(raxml.bootstrap,<span class="dt">prob=</span><span class="fl">0.10</span>)
edge.col &lt;-<span class="st"> </span><span class="kw">createLabel</span>(cnet, Nnet, <span class="dt">label=</span><span class="st">&quot;black&quot;</span>, <span class="st">&quot;edge&quot;</span>, <span class="dt">nomatch=</span><span class="st">&quot;grey&quot;</span>)
cnet &lt;-<span class="st"> </span><span class="kw">plot</span>(cnet, <span class="st">&quot;2D&quot;</span>, <span class="dt">show.edge.label =</span> <span class="ot">TRUE</span>, <span class="dt">edge.color=</span>edge.col)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">edge.col &lt;-<span class="st"> </span><span class="kw">createLabel</span>(Nnet, cnet, <span class="dt">label=</span><span class="st">&quot;black&quot;</span>, <span class="st">&quot;edge&quot;</span>, <span class="dt">nomatch=</span><span class="st">&quot;grey&quot;</span>)
z &lt;-<span class="st"> </span><span class="kw">plot</span>(Nnet, <span class="st">&quot;2D&quot;</span>, <span class="dt">show.edge.label =</span> <span class="ot">TRUE</span>, <span class="dt">edge.color=</span>edge.col)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">obj &lt;-<span class="st"> </span><span class="kw">addConfidences</span>(Nnet,cnet)
<span class="kw">plot</span>(obj,<span class="st">&quot;2D&quot;</span>,<span class="dt">show.edge.label=</span>T, <span class="dt">edge.color=</span>edge.col, <span class="dt">col.edge.label =</span> <span class="st">&quot;blue&quot;</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Write to SplitsTree for viewing
<span class="co"># write.nexus.networx(obj,&quot;Nnet.with.ML.Cnet.Bootstrap.nxs&quot;)</span></code></pre></div>
</div>
<div id="section" class="section level3">
<h3></h3>
<p>There are four possible data patterns in phylogenetic reconstruction: (1) patterns that are well supported in the network and appear in the bootstapped trees; (2) patterns that are well supported by (part of) the data/network but do not appear in the optimised trees, i.e. they are incompatible with the tree; (3) patterns that are weakly supported by the network but appear in the optimised trees anyway, i.e. they are compatible with the tree.</p>
<p>Here we demonstrate these patterns by showing the relationships between splits weights, from a NeighborNet splits graph, bootstrap bipartitions support and bootstrap percentages plotted on the optimised tree for a dataset of <span class="citation">Wang, Braun, and Kimball (2012)</span>.</p>
<p>(An advanced user figure…)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">Nnet &lt;-<span class="st"> </span><span class="kw">read.nexus.networx</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;RAxML_distances.Wang.nxs&quot;</span>))
raxml.tree &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;RAxML_bestTree.Wang.out&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span>unroot
raxml.bootstrap &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir,<span class="st">&quot;RAxML_bootstrap.Wang.out&quot;</span>))

bs_splits &lt;-<span class="st"> </span><span class="kw">as.splits</span>(raxml.bootstrap)
tree_splits &lt;-<span class="st"> </span><span class="kw">as.splits</span>(raxml.tree) <span class="op">%&gt;%</span><span class="st"> </span>unique <span class="op">%&gt;%</span><span class="st"> </span>removeTrivialSplits
<span class="co"># we overwrite bootstrap values and set the weights </span>
<span class="co"># to 1e-6 (almost zero), as we plot them on a log scale later on</span>
<span class="kw">attr</span>(bs_splits, <span class="st">&quot;weights&quot;</span>)[] &lt;-<span class="st"> </span><span class="fl">1e-6</span>
<span class="co"># combine the splits from the bootstrap and neighbor net</span>
<span class="co"># and delete duplicates and add the confidence values</span>
<span class="co"># we get rid of trivial splits</span>
all_splits &lt;-<span class="st"> </span><span class="kw">c</span>(Nnet<span class="op">$</span>splits, bs_splits) <span class="op">%&gt;%</span><span class="st"> </span>unique <span class="op">%&gt;%</span><span class="st"> </span>removeTrivialSplits <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">addConfidences</span>(bs_splits, <span class="dt">scaler=</span><span class="dv">100</span>)

<span class="co"># For easier plotting we create a matrix with the confidences and </span>
<span class="co"># weights as columns</span>
tab &lt;-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">SplitWeight =</span> <span class="kw">attr</span>(all_splits, <span class="st">&quot;weights&quot;</span>), <span class="dt">Bootstrap=</span><span class="kw">attr</span>(all_splits, <span class="st">&quot;confidences&quot;</span>), <span class="dt">Tree=</span><span class="ot">FALSE</span>)
<span class="co"># we add a logical variable pto indicate which splits are in the RAxML tree</span>
tab<span class="op">$</span>Tree[<span class="kw">matchSplits</span>(tree_splits, all_splits, <span class="ot">FALSE</span>)] =<span class="st"> </span><span class="ot">TRUE</span>

tab[<span class="kw">is.na</span>(tab[,<span class="st">&quot;Bootstrap&quot;</span>]),<span class="st">&quot;Bootstrap&quot;</span>] &lt;-<span class="st"> </span><span class="dv">0</span>
tab[,<span class="st">&quot;Bootstrap&quot;</span>] &lt;-<span class="st"> </span><span class="kw">round</span>(tab[,<span class="st">&quot;Bootstrap&quot;</span>])
<span class="kw">rownames</span>(tab) &lt;-<span class="st"> </span><span class="kw">apply</span>(<span class="kw">as.matrix</span>(all_splits, <span class="dt">zero.print =</span> <span class="st">&quot;.&quot;</span>, <span class="dt">one.print =</span> <span class="st">&quot;|&quot;</span>), <span class="dv">1</span>, paste0, <span class="dt">collapse=</span><span class="st">&quot;&quot;</span>)
tab[<span class="dv">1</span><span class="op">:</span><span class="dv">10</span>,]</code></pre></div>
<pre><code>##                              SplitWeight Bootstrap  Tree
## ..||........................   0.0171433       100  TRUE
## ..||||......................   0.0013902        14 FALSE
## ..||||......|||||...........   0.0001589         0 FALSE
## ||.........................|   0.0027691         1 FALSE
## ||..........................   0.0840367       100  TRUE
## ...|||......................   0.0001773         0 FALSE
## ...|||........|.|...........   0.0003663         0 FALSE
## |.|.........................   0.0060907         0 FALSE
## ....||......................   0.0385909       100  TRUE
## ||||........................   0.0018195        34  TRUE</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">col &lt;-<span class="st"> </span><span class="kw">rep</span>(<span class="st">&quot;blue&quot;</span>, <span class="kw">nrow</span>(tab))
col[tab[,<span class="st">&quot;Bootstrap&quot;</span>]<span class="op">==</span><span class="dv">0</span>] &lt;-<span class="st"> &quot;green&quot;</span>
col[tab[,<span class="st">&quot;SplitWeight&quot;</span>]<span class="op">==</span><span class="fl">1e-6</span>] &lt;-<span class="st"> &quot;red&quot;</span>

pch =<span class="st"> </span><span class="kw">rep</span>(<span class="dv">19</span>, <span class="kw">nrow</span>(tab))
pch[tab<span class="op">$</span>Tree] &lt;-<span class="st"> </span><span class="dv">17</span>

<span class="kw">par</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="fl">5.1</span>, <span class="fl">4.1</span>, <span class="fl">4.1</span>, <span class="fl">8.1</span>), <span class="dt">xpd=</span><span class="ot">TRUE</span>)
<span class="kw">plot</span>(tab[,<span class="st">&quot;SplitWeight&quot;</span>], tab[,<span class="st">&quot;Bootstrap&quot;</span>], <span class="dt">log=</span><span class="st">&quot;x&quot;</span>, <span class="dt">col=</span>col, <span class="dt">pch=</span>pch,  
     <span class="dt">xlab=</span><span class="st">&quot;Split weight (log scale)&quot;</span>, <span class="dt">ylab=</span><span class="st">&quot;Bootstrap (%)&quot;</span>)
<span class="kw">legend</span>(<span class="st">&quot;topright&quot;</span>, <span class="dt">inset=</span><span class="kw">c</span>(<span class="op">-</span><span class="fl">0.35</span>,<span class="dv">0</span>), <span class="kw">c</span>(<span class="st">&quot;Pattern 1&quot;</span>, <span class="st">&quot;Pattern 2&quot;</span>, <span class="st">&quot;Pattern 3&quot;</span>, <span class="st">&quot;Pattern in the</span><span class="ch">\n</span><span class="st">best tree&quot;</span>), <span class="dt">pch=</span><span class="kw">c</span>(<span class="dv">19</span>,<span class="dv">19</span>,<span class="dv">19</span>,<span class="dv">17</span>), <span class="dt">col=</span><span class="kw">c</span>(<span class="st">&quot;blue&quot;</span>, <span class="st">&quot;green&quot;</span>, <span class="st">&quot;red&quot;</span>, <span class="st">&quot;black&quot;</span>), <span class="dt">bty=</span><span class="st">&quot;n&quot;</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="figure-4" class="section level3">
<h3>Figure 4</h3>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">YCh &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;RAxML_bestTree.YCh&quot;</span>)) 
mtG &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;RAxML_bestTree.mtG&quot;</span>)) 
ncAI &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;RAxML_bestTree.AIs&quot;</span>)) 
all_data &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;RAxML_bestTree.3moles&quot;</span>)) 
YCh_boot &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;RAxML_bootstrap.YCh&quot;</span>)) 
mtG_boot &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;RAxML_bootstrap.mtG&quot;</span>)) 
ncAI_boot &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;RAxML_bootstrap.AIs&quot;</span>)) 
all_data_boot &lt;-<span class="st"> </span><span class="kw">read.tree</span>(<span class="kw">file.path</span>(fdir, <span class="st">&quot;RAxML_bootstrap.3moles&quot;</span>)) </code></pre></div>
<p>There are several option plotting a co-phylogeny. In the following we use the <code>cophylo</code> function of the <code>phytools</code> package.</p>
<pre><code>library(phytools)
par(mfrow=c(2,1))
obj &lt;- cophylo(YCh, mtG) 
plot(obj, mar=c(.1,.1,2,.1),scale.bar=c(.005,.05), ylim=c(-.2,1))  
title(&quot;A. YCh                                    B. mtG&quot;)
obj &lt;-  cophylo(ncAI, all_data)
plot(obj, mar=c(.1,.1,2,.1),scale.bar=c(.005,.05), ylim=c(-.2,1))
title(&quot;C. ncAI                              D. All data&quot;)</code></pre>
<p><img 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" /> Unfortunately this function does not (yet) offer to add confidences to some splits, but we can do this easily with more basic plot functions.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mfrow=</span><span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">2</span>), <span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">2</span>,<span class="dv">4</span>,<span class="dv">2</span>))
YCh &lt;-<span class="st"> </span><span class="kw">plotBS</span>(<span class="kw">midpoint</span>(YCh), YCh_boot, <span class="st">&quot;phylogram&quot;</span>, <span class="dt">p=</span><span class="dv">0</span>, <span class="dt">main =</span> <span class="st">&quot;YCh&quot;</span>)
mtG &lt;-<span class="st"> </span><span class="kw">plotBS</span>(<span class="kw">midpoint</span>(mtG), mtG_boot, <span class="st">&quot;phylogram&quot;</span>, <span class="dt">p=</span><span class="dv">0</span>, <span class="dt">main =</span> <span class="st">&quot;mtG&quot;</span>)
ncAI &lt;-<span class="st"> </span><span class="kw">plotBS</span>(<span class="kw">midpoint</span>(ncAI), ncAI_boot, <span class="st">&quot;phylogram&quot;</span>, <span class="dt">p=</span><span class="dv">0</span>, <span class="dt">main =</span> <span class="st">&quot;ncAI&quot;</span>)
all_data &lt;-<span class="st"> </span><span class="kw">plotBS</span>(<span class="kw">midpoint</span>(all_data), all_data_boot, <span class="st">&quot;phylogram&quot;</span>, <span class="dt">p=</span><span class="dv">0</span>, <span class="dt">main =</span> <span class="st">&quot;All data&quot;</span>)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>We can compare this with consensus network with the different bootstrap values for the different genes.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">par</span>(<span class="dt">mfrow=</span><span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">1</span>))
cn &lt;-<span class="st"> </span><span class="kw">consensusNet</span>(<span class="kw">c</span>(YCh, mtG, ncAI))
cn &lt;-<span class="st"> </span><span class="kw">addConfidences</span>(cn, YCh_boot) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">addConfidences</span>(mtG_boot, <span class="dt">add=</span><span class="ot">TRUE</span>) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">addConfidences</span>(ncAI_boot, <span class="dt">add=</span><span class="ot">TRUE</span>) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">addConfidences</span>(all_data_boot, <span class="dt">add=</span><span class="ot">TRUE</span>)
<span class="kw">plot</span>(cn, <span class="st">&quot;2D&quot;</span>, <span class="dt">show.edge.label=</span><span class="ot">TRUE</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
<div id="refs" class="references">
<div id="ref-Schliep2017">
<p>Schliep, Klaus, Alastair J. Potts, David A. Morrison, and Guido W. Grimm. 2017. “Intertwining Phylogenetic Trees and Networks.” <em>Methods in Ecology and Evolution</em>, n/a–n/a. doi:<a href="https://doi.org/10.1111/2041-210X.12760">10.1111/2041-210X.12760</a>.</p>
</div>
<div id="ref-Wang2012">
<p>Wang, Ning, Edward L. Braun, and Rebecca T. Kimball. 2012. “Testing Hypotheses About the Sister Group of the Passeriformes Using an Independent 30-Locus Data Set.” <em>Molecular Biology and Evolution</em> 29 (2): 737–50. doi:<a href="https://doi.org/10.1093/molbev/msr230">10.1093/molbev/msr230</a>.</p>
</div>
</div>
</div>



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r-cran-phangorn 2.4.0-1 / usr / lib / R / site-library / phangorn / doc / IntertwiningTreesAndNetworks.html
