Dendritic spine simulation from a probabilistic model
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<title>Spine simulation User’s Guide</title>



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<h1 class="title toc-ignore">Spine simulation User’s Guide</h1>
<h4 class="author"><em>Sergio Luengo-Sanchez et al.</em></h4>
<h4 class="date"><em>2017-10-20</em></h4>



<div id="abstract" class="section level2">
<h2>Abstract</h2>
<p>The dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex. They have a wide variety of morphologies, and their morphology appears to be critical from the functional point of view. To further characterize dendritic spine geometry, we used in this paper over 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons to group dendritic spines using model-based clustering. This approach uncovered six separate groups of human dendritic spines. To better understand the differences between these groups, the discriminative characteristics of each group were identified as a set of rules. Model-based clustering was also useful for simulating realistic 3D virtual representations of spines that matched the morphological definitions of each cluster. This mathematical approach could provide a useful tool for theoretical predictions on the functional features of human pyramidal neurons based on the morphology of dendritic spines.</p>
</div>
<div id="prerequirements" class="section level2">
<h2>Prerequirements</h2>
<p>This software has been developed as an R package. Consequently, it is needed an R enviroment and internet connectivity to download additional package dependencies. R software can be downloaded from <a href="http://cran.rstudio.com/index.html" class="uri">http://cran.rstudio.com/index.html</a>. We suggested to install 64 bits version of R and RStudio (<a href="https://www.rstudio.com/products/rstudio/download/" class="uri">https://www.rstudio.com/products/rstudio/download/</a>).</p>
</div>
<div id="package-installation" class="section level2">
<h2>Package installation</h2>
<p>Some R packages are needed to perform some specific tasks releated with 3D processing, data management, or modeling. They must be installed through the command <code>install.packages(&quot;name_of_the_package&quot;)</code> to be able to use simulateSpines. The R dependencies of the package are:</p>
<table>
<thead>
<tr class="header">
<th>Package</th>
<th>Version</th>
<th>License</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Rcpp</td>
<td>0.12.9</td>
<td>GPL2/3</td>
</tr>
<tr class="even">
<td>Rvcg</td>
<td>0.0.15</td>
<td>GPL2/3</td>
</tr>
<tr class="odd">
<td>geometry</td>
<td>0.3.6</td>
<td>GPL3</td>
</tr>
<tr class="even">
<td>Morpho</td>
<td>2.4.1.1</td>
<td>GPL2</td>
</tr>
<tr class="odd">
<td>data.table</td>
<td>1.10.0</td>
<td>GPL3</td>
</tr>
<tr class="even">
<td>mclust</td>
<td>5.2.3</td>
<td>GPL2/3</td>
</tr>
<tr class="odd">
<td>foreign</td>
<td>0.8.69</td>
<td>GPL2/3</td>
</tr>
<tr class="even">
<td>rgl</td>
<td>0.98</td>
<td>GPL2/3</td>
</tr>
<tr class="odd">
<td>ROSE</td>
<td>0.0.3</td>
<td>GPL2</td>
</tr>
<tr class="even">
<td>MASS</td>
<td>7.3.47</td>
<td>GPL2/3</td>
</tr>
<tr class="odd">
<td>tmvtnorm</td>
<td>1.4.10</td>
<td>GPL2/3</td>
</tr>
<tr class="even">
<td>ggplot2</td>
<td>2.2.2.1</td>
<td>GPL2</td>
</tr>
<tr class="odd">
<td>MixSim</td>
<td>1.1.3</td>
<td>GPL2/3</td>
</tr>
<tr class="even">
<td>scales</td>
<td>0.5.0</td>
<td>MIT</td>
</tr>
</tbody>
</table>
<p>Updated versions of the R dependencies packages should be supported.</p>
<p>simulateSpines package can be downloaded from <a href="http://cig.fi.upm.es/sites/default/files/software/simulateSpines/simulateSpines_0.1.tar.gz">simulateSpines</a>. Once you have the file in you computer you can install the package introducing the next line into the R console:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">install.packages</span>(<span class="st">&quot;path/to/file/simulateSpines_0.1.tar.gz&quot;</span>, <span class="dt">repos =</span> <span class="ot">NULL</span>, <span class="dt">type=</span><span class="st">&quot;source&quot;</span>)</code></pre></div>
<p>Finally, to have accessed to the functionalities of simulateSpines you must load the package with the command:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(spineSimulation)</code></pre></div>
<p>After that, simulateSpines is loaded in the R workspace so you can start to cluster dendritic spines and analyze the results. In the next section we show some cases of use to exploit the possibilities that the package provides. To test the package, the model and the dataset of morphological features are included as part of the package to allow proper reproducibility.</p>
</div>
<div id="using-spinesimulation-package" class="section level2">
<h2>Using spineSimulation package</h2>
<p>Lets start writing <code>?spineSimulation</code> into the R console. The page that appears in front of you provides general information about the package. Next, if you click on the hyperlink <em>Index</em> at the end of the page you will be redirected to the index page where you can see all the functions of the software and a short description of each one of them.</p>
<div id="clustering" class="section level3">
<h3>Clustering</h3>
<p>The common executing flow is based on obtaining a probabilistic clustering from the features computed on the surface of the spines with multiresolution Reeb graph. This dataset can be generated from the library <a href="https://github.com/ComputationalIntelligenceGroup/3DSpineMFE">3DSpineMFE</a>. Given a dataset, probabilistic clustering is performed according to the next line:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">model &lt;-<span class="st"> </span><span class="kw">spineClustering</span>(<span class="dt">csvSpines =</span> <span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;spineDataset.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&quot;</span>), <span class="dt">numClusters =</span> <span class="kw">c</span>(<span class="dv">2</span>:<span class="dv">10</span>), <span class="dt">scale =</span> T)</code></pre></div>
<p>This computation can take several days so we provide the resulting model. Also enable the <code>scale</code> flag can increase the computational time considerably.</p>
<p>BIC score is the heuristic score used by the algorithm to select the number of clusters. The higher is the score, the better is the cluster. To plot the BIC score obtained for each number of the clusters evaluated during the clustering process you can run.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Plot the BIC score by the number of cluster and their model name.</span>
<span class="co"># Get the BIC score for each number of clusters</span>
df&lt;-<span class="kw">data.frame</span>(<span class="dt">clusters=</span><span class="kw">as.numeric</span>(<span class="kw">rownames</span>(model$BIC)),<span class="dt">BIC=</span><span class="kw">apply</span>(model$BIC,<span class="dv">1</span>,function(x){<span class="kw">return</span>(<span class="kw">max</span>(x,<span class="dt">na.rm=</span>T))}))

<span class="co">#Plot the BIC score remove the case where there is only 1 cluster as in the paper</span>
<span class="kw">ggplot</span>(<span class="dt">data=</span>df[<span class="kw">which</span>(df$clusters!=<span class="dv">1</span>),], <span class="kw">aes</span>(<span class="dt">x=</span>clusters, <span class="dt">y=</span>BIC, <span class="dt">group=</span><span class="dv">1</span>)) +<span class="kw">geom_line</span>(<span class="dt">size=</span><span class="dv">1</span>)+<span class="kw">geom_point</span>(<span class="dt">size=</span><span class="dv">2</span>)+<span class="kw">labs</span>(<span class="dt">x=</span><span class="st">&quot;# of clusters&quot;</span>,<span class="dt">y=</span><span class="st">&quot;BIC score&quot;</span>)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#Show the number of clusters that maximize BIC</span>
<span class="kw">print</span>(<span class="kw">paste</span>(<span class="st">&quot;The number of clusters is&quot;</span>,model$G))</code></pre></div>
<pre><code>## [1] &quot;The number of clusters is 6&quot;</code></pre>
</div>
<div id="mds" class="section level3">
<h3>MDS</h3>
<p>To make visualization and interpretation of the groups of dendritic spines easier, distance between clusters in a n-dimensional space can be scaled to a 2-dimensional space with multidimensional scaling. It represents the similarity between the morphology of the clusters. Clusters that are close in the MDS plot present analogous shapes. Additionally, in those cases where the cluster is represented just as a point that means that all the spines in the cluster belongs to that cluster with a probability close to 1. However, when there is a continuum of points between two clusters, it suggests that there are some spines whose morphology is a mix of the two clusters and cannot be assigned to any of them certainly.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">MDS&lt;-<span class="kw">computeMDS</span>(model,<span class="dv">2</span>)
<span class="kw">plotMDS</span>(model,MDS)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="overlapping" class="section level3">
<h3>Overlapping</h3>
<p>Ideally, a clustering method should find well defined clusters. Thus, overlapping between clusters is undesirable. To measure the overlapping between pairs of clusters the next function can be applied:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">computeOverlapping</span>(model)</code></pre></div>
<p>As result a table is generated where each value is the degree of overlapping between each pair of cluster where 1 is total overlapping and 0 that there is not overlap at all. As it can be expected, the diagonal of the table is 1 in all cases because it is measuring the overlapping of each cluster with itself.</p>
<table>
<thead>
<tr class="header">
<th></th>
<th align="right">Cluster 1</th>
<th align="right">Cluster 2</th>
<th align="right">Cluster 3</th>
<th align="right">Cluster 4</th>
<th align="right">Cluster 5</th>
<th align="right">Cluster 6</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Cluster 1</td>
<td align="right">1e+00</td>
<td align="right">2.00e-07</td>
<td align="right">2.00e-07</td>
<td align="right">1.00e-06</td>
<td align="right">0.00e+00</td>
<td align="right">0.0e+00</td>
</tr>
<tr class="even">
<td>Cluster 2</td>
<td align="right">1e-07</td>
<td align="right">1.00e+00</td>
<td align="right">8.60e-06</td>
<td align="right">8.30e-06</td>
<td align="right">0.00e+00</td>
<td align="right">0.0e+00</td>
</tr>
<tr class="odd">
<td>Cluster 3</td>
<td align="right">2e-07</td>
<td align="right">2.35e-05</td>
<td align="right">1.00e+00</td>
<td align="right">1.69e-05</td>
<td align="right">2.00e-05</td>
<td align="right">0.0e+00</td>
</tr>
<tr class="even">
<td>Cluster 4</td>
<td align="right">8e-07</td>
<td align="right">2.00e-05</td>
<td align="right">1.53e-05</td>
<td align="right">1.00e+00</td>
<td align="right">4.50e-06</td>
<td align="right">0.0e+00</td>
</tr>
<tr class="odd">
<td>Cluster 5</td>
<td align="right">0e+00</td>
<td align="right">0.00e+00</td>
<td align="right">4.08e-05</td>
<td align="right">1.06e-05</td>
<td align="right">1.00e+00</td>
<td align="right">5.9e-06</td>
</tr>
<tr class="even">
<td>Cluster 6</td>
<td align="right">0e+00</td>
<td align="right">0.00e+00</td>
<td align="right">0.00e+00</td>
<td align="right">0.00e+00</td>
<td align="right">1.63e-05</td>
<td align="right">1.0e+00</td>
</tr>
</tbody>
</table>
</div>
<div id="characterization-of-clusters" class="section level3">
<h3>Characterization of clusters</h3>
<p>To characterize each cluster according to its most representative features we use RIPPER, a based rule classifier. To apply RIPPER each spine has to be attributed to its most probable cluster turning the problem into a binary classification problem. Finding the rules that govern it cluster we obtain its characterization. We use the version implemented in Weka of RIPPER, that is, JRip. We provide a script to export spines with their cluster asignation to .arff format.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">generateArff</span>(<span class="st">'/path/to/folder/'</span>,model)</code></pre></div>
</div>
<div id="distributions-of-clusters" class="section level3">
<h3>Distributions of clusters</h3>
<p>To show the bar plot of the distribution of dendritic spines when they are crisply ascribed to a unique cluster and a dendritic compartment, age or combination of both are selected run:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotGlobalDistribution</span>(model)</code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotDendriticCompartment</span>(model)</code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotAge</span>(model,<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;ageDataset.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&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">plotCombination</span>(model,<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;ageDataset.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&quot;</span>))</code></pre></div>
<p><img 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" /><!-- --></p>
<p>To check if distribution of spines is independent of their dendritic compartment, age or combination of both we use a <span class="math inline">\(\chi^2\)</span> hypothesis test. To run the test execute:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">chiSquareTest</span>(model,<span class="st">&quot;dendrite&quot;</span>)</code></pre></div>
<pre><code>## [1] 3.806081e-34</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">chiSquareTest</span>(model,<span class="st">&quot;age&quot;</span>,<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;ageDataset.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&quot;</span>))</code></pre></div>
<pre><code>## [1] 3.726239e-06</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">chiSquareTest</span>(model,<span class="st">&quot;both&quot;</span>,<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;ageDataset.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&quot;</span>))</code></pre></div>
<pre><code>## [1] 4.117763e-36</code></pre>
<p>Because appearently discrepances between the distributions are placed only in a few clusters, a <span class="math inline">\(\chi^2\)</span> test was performed cluster by cluster to check if the distribution of each individual cluster was independent of its dendritic compartmente, age or combination of both.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">chiSquareTestCluster</span>(model,<span class="st">&quot;dendrite&quot;</span>)</code></pre></div>
<pre><code>## [1] 6.734985e-24 2.199326e-01 6.989953e-04 1.235750e-01 8.656368e-10
## [6] 5.192490e-04</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">chiSquareTestCluster</span>(model,<span class="st">&quot;age&quot;</span>,<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;ageDataset.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&quot;</span>))</code></pre></div>
<pre><code>## [1] 0.2757818755 0.0021052495 0.1424060135 0.0130182148 0.1855871385
## [6] 0.0004510625</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">chiSquareTestCluster</span>(model,<span class="st">&quot;both&quot;</span>,<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;ageDataset.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&quot;</span>))</code></pre></div>
<pre><code>## [1] 2.324293e-23 8.965051e-03 4.537788e-03 3.982821e-02 6.907732e-09
## [6] 9.104353e-06</code></pre>
</div>
<div id="distance-from-soma" class="section level3">
<h3>Distance from soma</h3>
<p>The analysis based on the distribution of dendritic spines as a function of their distance from soma can be tackled running the next line:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plotDistance2Soma</span>(model,<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;distanceSpines2soma.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&quot;</span>),<span class="dv">6</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">chiSquareTestDistance</span>(model,<span class="kw">system.file</span>(<span class="st">&quot;extdata&quot;</span>, <span class="st">&quot;distanceSpines2soma.csv&quot;</span>, <span class="dt">package =</span> <span class="st">&quot;spineSimulation&quot;</span>),<span class="dv">6</span>)</code></pre></div>
<pre><code>## [1] 8.007588e-23</code></pre>
</div>
<div id="simulation-3d" class="section level3">
<h3>Simulation 3D</h3>
<p>Finally, to simulate 3D virtual spines the user must run the following lines:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Simulate 5 spines from the cluster 1 and render the second of them</span>
<span class="co"># Sample spines from the probability distribution</span>
newSpines &lt;-<span class="st"> </span><span class="kw">spineSampling</span>(model,<span class="dt">nSpines=</span><span class="dv">5</span>,<span class="dt">cluster=</span><span class="dv">1</span>,<span class="dt">seed=</span><span class="dv">1</span>)
<span class="co"># Generate the 3D of the sampled spine</span>
mesh &lt;-<span class="st"> </span><span class="kw">simulation3Dmesh</span>(newSpines,<span class="dt">idx=</span><span class="dv">2</span>,<span class="dt">iterations=</span><span class="dv">4</span>)
<span class="co"># Render the spine</span>
<span class="kw">shade3d</span>(mesh,<span class="dt">col=</span><span class="st">&quot;red&quot;</span>)</code></pre></div>
</div>
</div>



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