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<body>
<h1 class="title toc-ignore">An example of run SnapCCESS with R</h1>
<h4 class="author">Lijia Yu</h4>
<h4 class="date">2023-04-18</h4>
<div id="snapccess" class="section level2">
<h2>SnapCCESS</h2>
<p>SnapCCESS enables you to perform the clustering on multimodality
datasets. To learn more about SnapCCESS see <a href="https://github.com/PYangLab/SnapCCESS" class="uri">https://github.com/PYangLab/SnapCCESS</a>.</p>
</div>
<div id="install-snapccess-in-r" class="section level2">
<h2>Install SnapCCESS in R</h2>
<p>To install SnapCCESS, you need to use
<code>remote::install_github</code>.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>remotes<span class="sc">::</span><span class="fu">install_github</span>(<span class="at">repo=</span><span class="st">'PYangLab/SnapCCESS'</span>,<span class="at">branch=</span><span class="st">'main'</span>,<span class="at">subdir=</span><span class="st">'snapccess-r/SnapCCESS'</span>)</span></code></pre></div>
<p>After install SnapCCESS package, we need to install the python
dependencies that to run the SnapCCESS in R, for example, the python
environment name is SnapCCESS, and I use conda to install the pypi
package, and my conda location is
<code>/home/lijiay/miniconda3/condabin/conda</code>.</p>
<p>If we don’t set up the conda path, by default it will be installed in
the R r-miniconda folder.</p>
<p>We only need to run this step once when you first install the
SnapCCESS package.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(SnapCCESS)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">install_SnapCCESS</span>(<span class="at">envname =</span> <span class="st">"SnapCCESS"</span>,<span class="at">method =</span> <span class="st">"conda"</span>,<span class="at">conda=</span><span class="st">"/home/lijiay/miniconda3/condabin/conda"</span>)</span></code></pre></div>
</div>
<div id="loading-all-python-packages" class="section level2">
<h2>Loading all python packages</h2>
<p>If you have installed the package successfully, we next need to use
the installed environment and load all packages from this
environment.</p>
<p>If you are unable to load the modules, please ensure that the
SnapCCESS environment is installed on your computer. After confirming
its installation, please restart a new Rsession and attempt to load the
package once again</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>reticulate<span class="sc">::</span><span class="fu">use_condaenv</span>(<span class="at">condaenv =</span> <span class="st">"SnapCCESS"</span>,</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> <span class="at">required=</span>T,</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="at">conda=</span><span class="st">"/home/lijiay/miniconda3/condabin/conda"</span>)</span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>SnapCCESS<span class="sc">::</span><span class="fu">loadmodule</span>()</span></code></pre></div>
</div>
<div id="read-the-example-data-and-do-preprocessing." class="section level2">
<h2>Read the example data and do preprocessing.</h2>
<p>In preprocessing stage, we preform nomalisation of each modality</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>rna<span class="ot">=</span><span class="fu">read.csv</span>(<span class="st">"../in/CITEseq_logRNA.csv.gz"</span>,<span class="at">row.names =</span> <span class="dv">1</span>,<span class="at">check.names =</span> F)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>adt<span class="ot">=</span><span class="fu">read.csv</span>(<span class="st">"../in/CITEseq_logADT.csv.gz"</span>,<span class="at">row.names =</span> <span class="dv">1</span>,<span class="at">check.names =</span> F)</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>train<span class="ot">=</span><span class="fu">preprocess</span>(<span class="at">x=</span><span class="fu">list</span>(rna,adt),<span class="at">mb_size=</span><span class="dv">64</span>)</span></code></pre></div>
</div>
<div id="build-model" class="section level2">
<h2>Build model</h2>
<p>We next build the SnapCCESS model with two modalities.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>model<span class="ot">=</span><span class="fu">build_model</span>(<span class="at">num_features=</span><span class="fu">list</span>(<span class="fu">nrow</span>(rna),<span class="fu">nrow</span>(adt)),</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> <span class="at">num_hidden_features=</span><span class="fu">list</span>(<span class="dv">185</span>,<span class="dv">30</span>),</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="at">num_latent_features=</span><span class="dv">100</span>)</span></code></pre></div>
</div>
<div id="run-snapccess-to-get-embeddings" class="section level2">
<h2>Run SnapCCESS to get embeddings</h2>
<p>Here, we propose an example with a total of 10 epochs, divided into 5
cycles, with 2 epochs per cycle.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>output<span class="ot">=</span><span class="fu">run_SnapCCESS</span>(model,train,</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> <span class="at">epochs=</span><span class="dv">10</span>,<span class="at">epochs_per_cycle=</span><span class="dv">2</span>,</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="at">save_path=</span><span class="st">""</span>,<span class="at">snapshot=</span><span class="cn">TRUE</span>)</span></code></pre></div>
</div>
<div id="clustering" class="section level2">
<h2>Clustering</h2>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>clusts<span class="ot">=</span><span class="fu">list</span>()</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>n<span class="ot">=</span><span class="dv">1</span></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span>(i <span class="cf">in</span> output[[<span class="dv">3</span>]]){ </span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> clusts[[n]]<span class="ot">=</span><span class="fu">kmeans</span>(reticulate<span class="sc">::</span><span class="fu">py_to_r</span>(i),<span class="at">centers =</span> <span class="dv">4</span>)</span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> n<span class="ot">=</span>n<span class="sc">+</span><span class="dv">1</span></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>consensus_c <span class="ot">=</span> clue<span class="sc">::</span><span class="fu">cl_consensus</span>(clusts, <span class="at">method =</span> <span class="st">"HE"</span>)</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>consensus <span class="ot">=</span> clue<span class="sc">::</span><span class="fu">cl_class_ids</span>(consensus_c<span class="sc">$</span>.Data)</span></code></pre></div>
</div>
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