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<h1>Welcome to DeepProg’s documentation!<a class="headerlink" href="#welcome-to-deepprog-s-documentation" title="Permalink to this headline">¶</a></h1>
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<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="installation.html#requirements">Requirements</a></li>
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<li class="toctree-l1"><a class="reference internal" href="usage.html">Tutorial: Simple DeepProg model</a><ul>
<li class="toctree-l2"><a class="reference internal" href="usage.html#input-parameters">Input parameters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="usage.html#creating-a-simple-deepprog-model-with-one-autoencoder-for-each-omic">Creating a simple DeepProg model with one autoencoder for each omic</a></li>
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<li class="toctree-l1"><a class="reference internal" href="usage_ensemble.html">Tutorial: Ensemble of DeepProg model</a><ul>
<li class="toctree-l2"><a class="reference internal" href="usage_ensemble.html#instanciation">Instanciation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="usage_advanced.html">Tutorial: Advanced usage of DeepProg model</a><ul>
<li class="toctree-l2"><a class="reference internal" href="usage_advanced.html#visualisation">Visualisation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="usage_advanced.html#computing-cluster-specific-feature-signatures">Computing cluster-specific feature signatures</a></li>
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<li class="toctree-l1"><a class="reference internal" href="case_study.html">Case study: Analyzing TCGA HCC dataset</a><ul>
<li class="toctree-l2"><a class="reference internal" href="case_study.html#dataset-preparation">Dataset preparation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="usage_tuning.html">Tutorial: Tuning DeepProg</a><ul>
<li class="toctree-l2"><a class="reference internal" href="usage_tuning.html#a-first-example">A first example</a></li>
<li class="toctree-l2"><a class="reference internal" href="usage_tuning.html#tuning-using-one-or-multiple-test-datasets">Tuning using one or multiple test datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="usage_tuning.html#results">Results</a></li>
<li class="toctree-l2"><a class="reference internal" href="usage_tuning.html#recommendation">Recommendation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="LICENSE.html">License</a></li>
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<li class="toctree-l2"><a class="reference internal" href="api/simdeep.html#module-simdeep.config">simdeep.config module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="api/simdeep.html#module-simdeep.simdeep_multiple_dataset">simdeep.simdeep_multiple_dataset module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/simdeep.html#module-simdeep.simdeep_utils">simdeep.simdeep_utils module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/simdeep.html#module-simdeep.survival_utils">simdeep.survival_utils module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/simdeep.html#module-simdeep">Module contents</a></li>
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<div class="section" id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
<p>This package allows to combine multi-omics data for individual samples together with survival. Using autoencoders (default) or any alternative embedding methods, the pipeline creates new set of features and identifies those linked with survival. In a second time, the samples are clustered with different possible strategies to obtain robust subtypes linked to survival. The robustness of the obtained subtypes can then be tested externally on one or multiple validation datasets and/or the <em>out-of-bags</em> samples. The omic data used in the original study are RNA-Seq, MiR and Methylation. However, this approach can be extended to any combination of omic data. The current package contains the omic data used in the study and a copy of the model computed. However, it is easy to recreate a new model from scratch using any combination of omic data.
The omic data and the survival files should be in tsv (Tabular Separated Values) format and examples are provided. The deep-learning framework to produce the autoencoders uses Keras with either Theano / tensorflow/ CNTK as background.</p>
<img alt="_images/workflow.png" src="_images/workflow.png" />
</div>
<div class="section" id="access">
<h2>Access<a class="headerlink" href="#access" title="Permalink to this headline">¶</a></h2>
<p>The package is accessible at this link: <a class="reference external" href="https://github.com/lanagarmire/DeepProg">https://github.com/lanagarmire/DeepProg</a>.</p>
</div>
<div class="section" id="contribute">
<h2>Contribute<a class="headerlink" href="#contribute" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><p>Issue Tracker: github.com/lanagarmire/DeepProg/issues</p></li>
<li><p>Source Code: github.com/lanagarmire/DeepProg</p></li>
</ul>
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<div class="section" id="support">
<h2>Support<a class="headerlink" href="#support" title="Permalink to this headline">¶</a></h2>
<p>If you are having issues, please let us know.
You can reach us using the following email address:</p>
<p>Olivier Poirion, Ph.D.
<a class="reference external" href="mailto:o.poirion%40gmail.com">o<span>.</span>poirion<span>@</span>gmail<span>.</span>com</a></p>
</div>
<div class="section" id="citation">
<h2>Citation<a class="headerlink" href="#citation" title="Permalink to this headline">¶</a></h2>
<p>This package refers to our preprint paper: [Multi-omics-based pan-cancer prognosis prediction using an ensemble of deep-learning and machine-learning models](<a class="reference external" href="https://www.medrxiv.org/content/10.1101/19010082v1">https://www.medrxiv.org/content/10.1101/19010082v1</a>)</p>
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<div class="section" id="license">
<h2>License<a class="headerlink" href="#license" title="Permalink to this headline">¶</a></h2>
<p>The project is licensed under the MIT license.</p>
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Datasets
Models
