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  <div class="section" id="multi-omics-autoencoder-integration-maui">

<h1>Multi-omics Autoencoder Integration (maui)<a class="headerlink" href="#multi-omics-autoencoder-integration-maui" title="Permalink to this headline"></a></h1>

<p>maui is an autoencoder-based framework for multi-omics data analysis. It consists of two main modules, <a class="reference internal" href="maui.html"><span class="doc">The Maui Class</span></a>, and <a class="reference internal" href="utils.html"><span class="doc">Maui Utilities</span></a>. For an introduction of the use of autoencoders for multi-omics integration, see <a class="reference internal" href="autoencoder-integration.html"><span class="doc">Multi-modal Autoencoders</span></a>.</p>

<div class="section" id="table-of-contents">

<h2>Table of contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline"></a></h2>

<div class="toctree-wrapper compound">

<ul>

<li class="toctree-l1"><a class="reference internal" href="autoencoder-integration.html">Multi-modal Autoencoders</a><ul>

<li class="toctree-l2"><a class="reference internal" href="autoencoder-integration.html#variational-autoencoders">Variational Autoencoders</a></li>

<li class="toctree-l2"><a class="reference internal" href="autoencoder-integration.html#stacked-autoencoders">Stacked Autoencoders</a></li>

</ul>

</li>

<li class="toctree-l1"><a class="reference internal" href="data-normalization.html">Data and Normalization</a></li>

<li class="toctree-l1"><a class="reference internal" href="filtering-and-merging-latent-factors.html">Filtering and Merging latent factors</a><ul>

<li class="toctree-l2"><a class="reference internal" href="filtering-and-merging-latent-factors.html#dropping-unexplanatory-latent-factors">Dropping unexplanatory latent factors</a></li>

<li class="toctree-l2"><a class="reference internal" href="filtering-and-merging-latent-factors.html#merging-similar-latent-factors">Merging similar latent factors</a></li>

<li class="toctree-l2"><a class="reference internal" href="filtering-and-merging-latent-factors.html#supervised-filtering-of-latent-factors">Supervised filtering of latent factors</a></li>

</ul>

</li>

<li class="toctree-l1"><a class="reference internal" href="saving-and-loading-models.html">Saving and loading models</a><ul>

<li class="toctree-l2"><a class="reference internal" href="saving-and-loading-models.html#saving-a-trained-model-to-disk">Saving a trained model to disk</a></li>

<li class="toctree-l2"><a class="reference internal" href="saving-and-loading-models.html#loading-a-model-from-disk">Loading a model from disk</a></li>

</ul>

</li>

<li class="toctree-l1"><a class="reference internal" href="maui.html">The Maui Class</a></li>

<li class="toctree-l1"><a class="reference internal" href="utils.html">Maui Utilities</a></li>

</ul>

</div>

</div>

<div class="section" id="quickstart">

<h2>Quickstart<a class="headerlink" href="#quickstart" title="Permalink to this headline"></a></h2>

<p>The <code class="docutils literal notranslate"><span class="pre">Maui</span></code> class implements <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code>’s <code class="docutils literal notranslate"><span class="pre">BaseEstimator</span></code>. In order to infer latent factors in multi-omics data, first instantiate a <code class="docutils literal notranslate"><span class="pre">Maui</span></code> model with the desired parameters, and then fit it to some data:</p>

<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">maui</span> <span class="kn">import</span> <span class="n">Maui</span>



<span class="n">maui_model</span> <span class="o">=</span> <span class="n">maui</span><span class="o">.</span><span class="n">Maui</span><span class="p">(</span><span class="n">n_hidden</span><span class="o">=</span><span class="p">[</span><span class="mi">900</span><span class="p">],</span> <span class="n">n_latent</span><span class="o">=</span><span class="mi">70</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>

<span class="n">z</span> <span class="o">=</span> <span class="n">maui_model</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">({</span><span class="s1">&#39;mRNA&#39;</span><span class="p">:</span> <span class="n">gex</span><span class="p">,</span> <span class="s1">&#39;Mutations&#39;</span><span class="p">:</span> <span class="n">mut</span><span class="p">,</span> <span class="s1">&#39;CNV&#39;</span><span class="p">:</span> <span class="n">cnv</span><span class="p">})</span>

</pre></div>

</div>

<p>This will instantiate a maui model with one hidden layer of 900 nodes, and a middle layer of 70 nodes, which will be traiend for 100 epochs. It then feeds the multi-omics data in <code class="docutils literal notranslate"><span class="pre">gex</span></code>, <code class="docutils literal notranslate"><span class="pre">mut</span></code>, and <code class="docutils literal notranslate"><span class="pre">cnv</span></code> to the fitting procedure. The omics data (<code class="docutils literal notranslate"><span class="pre">gex</span></code> et. al.) are <code class="docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code> objects of dimension (n_features, n_samples). The return object <code class="docutils literal notranslate"><span class="pre">z</span></code> is a <code class="docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code> (n_samples, n_latent), and may be used for further analysis.</p>

<p>In order to check the model’s convergance, the <code class="docutils literal notranslate"><span class="pre">hist</span></code> object may be inspected, and plotted:</p>

<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">maui_model</span><span class="o">.</span><span class="n">hist</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>

</pre></div>

</div>

<img alt="_images/hist.png" src="_images/hist.png" />

<p>For a more comprehensive example, check out <a class="reference external" href="https://github.com/BIMSBbioinfo/maui/blob/master/vignette/maui_vignette.ipynb">our vignette</a>.</p>

<div class="section" id="indices-and-tables">

<h3>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h3>

<ul class="simple">

<li><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></li>

<li><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></li>

<li><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></li>

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