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  <div class="section" id="tutorial-simple-deepprog-model">





  
  

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<h1>Tutorial: Simple DeepProg model<a class="headerlink" href="#tutorial-simple-deepprog-model" title="Permalink to this headline">¶</a></h1>





  
  

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<p>The principle of DeepProg can be summarized as follow:</p>





  
  

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<ul class="simple">





  
  

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<li><p>Loading of multiple samples x OMIC matrices</p></li>





  
  

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<li><p>Preprocessing ,normalisation, and sub-sampling of the input matrices</p></li>





  
  

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<li><p>Matrix transformation using autoencoder</p></li>





  
  

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<li><p>Detection of survival features</p></li>





  
  

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<li><p>Survival feature agglomeration and clustering</p></li>





  
  

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<li><p>Creation of supervised models to predict the output of new samples</p></li>





  
  

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</ul>





  
  

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<div class="section" id="input-parameters">





  
  

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<h2>Input parameters<a class="headerlink" href="#input-parameters" title="Permalink to this headline">¶</a></h2>





  
  

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<p>All the default parameters are defined in the config file: <code class="docutils literal notranslate"><span class="pre">./simdeep/config.py</span></code> but can be passed dynamically. Three types of parameters must be defined:</p>





  
  

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<ul class="simple">





  
  

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<li><p>The training dataset (omics + survival input files)</p>





  
  

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<ul>





  
  

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<li><p>In addition, the parameters of the test set, i.e. the omic dataset and the survival file</p></li>





  
  

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</ul>





  
  

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</li>





  
  

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<li><p>The parameters of the autoencoder (the default parameters works but it might be fine-tuned.</p></li>





  
  

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<li><p>The parameters of the classification procedures (default are still good)</p></li>





  
  

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</ul>





  
  

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</div>





  
  

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<div class="section" id="input-matrices">





  
  

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<h2>Input matrices<a class="headerlink" href="#input-matrices" title="Permalink to this headline">¶</a></h2>





  
  

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<p>As examples, we included two datasets:</p>





  
  

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<ul class="simple">





  
  

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<li><p>A dummy example dataset in the <code class="docutils literal notranslate"><span class="pre">example/data/</span></code> folder:</p></li>





  
  

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</ul>





  
  

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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>examples





  
  

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├── data





  
  

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│   ├── meth_dummy.tsv





  
  

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│   ├── mir_dummy.tsv





  
  

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│   ├── rna_dummy.tsv





  
  

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│   ├── rna_test_dummy.tsv





  
  

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│   ├── survival_dummy.tsv





  
  

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│   └── survival_test_dummy.tsv





  
  

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</pre></div>





  
  

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</div>





  
  

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<ul class="simple">





  
  

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<li><p>And a real dataset in the <code class="docutils literal notranslate"><span class="pre">data</span></code> folder. This dataset derives from the TCGA HCC cancer dataset. This dataset needs to be decompressed before processing:</p></li>





  
  

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</ul>





  
  

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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>data





  
  

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├── meth.tsv.gz





  
  

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├── mir.tsv.gz





  
  

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├── rna.tsv.gz





  
  

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└── survival.tsv





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>An input matrix file should follow this format:</p>





  
  

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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>head mir_dummy.tsv





  
  

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Samples        dummy_mir_0     dummy_mir_1     dummy_mir_2     dummy_mir_3 ...





  
  

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sample_test_0  <span class="m">0</span>.469656032287  <span class="m">0</span>.347987447237  <span class="m">0</span>.706633335508  <span class="m">0</span>.440068758445 ...





  
  

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sample_test_1  <span class="m">0</span>.0453108219657 <span class="m">0</span>.0234642968791 <span class="m">0</span>.593393816691  <span class="m">0</span>.981872970341 ...





  
  

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sample_test_2  <span class="m">0</span>.908784043793  <span class="m">0</span>.854397550009  <span class="m">0</span>.575879144667  <span class="m">0</span>.553333958713 ...





  
  

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...





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>Also, if multiple matrices are used as input, they must keep the sample order. For example:</p>





  
  

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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>head rna_dummy.tsv





  
  

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Samples        dummy_gene_0     dummy_gene_1     dummy_gene_2     dummy_gene_3 ...





  
  

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sample_test_0  <span class="m">0</span>.69656032287  <span class="m">0</span>.47987447237  <span class="m">0</span>.06633335508  <span class="m">0</span>.40068758445 ...





  
  

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sample_test_1  <span class="m">0</span>.53108219657 <span class="m">0</span>.234642968791 <span class="m">0</span>.93393816691  <span class="m">0</span>.81872970341 ...





  
  

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sample_test_2  <span class="m">0</span>.8784043793  <span class="m">0</span>.54397550009  <span class="m">0</span>.75879144667  <span class="m">0</span>.53333958713 ...





  
  

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...





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>The  arguments <code class="docutils literal notranslate"><span class="pre">training_tsv</span></code> and <code class="docutils literal notranslate"><span class="pre">path_data</span></code> from the <code class="docutils literal notranslate"><span class="pre">extract_data</span></code> module are used to defined the input matrices.</p>





  
  

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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># The keys/values of this dict represent the name of the omic and the corresponding input matrix</span>





  
  

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<span class="n">training_tsv</span> <span class="o">=</span> <span class="p">{</span>





  
  

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    <span class="s1">&#39;GE&#39;</span><span class="p">:</span> <span class="s1">&#39;rna_dummy.tsv&#39;</span><span class="p">,</span>





  
  

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    <span class="s1">&#39;MIR&#39;</span><span class="p">:</span> <span class="s1">&#39;mir_dummy.tsv&#39;</span><span class="p">,</span>





  
  

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    <span class="s1">&#39;METH&#39;</span><span class="p">:</span> <span class="s1">&#39;meth_dummy.tsv&#39;</span><span class="p">,</span>





  
  

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<span class="p">}</span>





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>a survival file must have this format:</p>





  
  

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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>head survival_dummy.tsv





  
  

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barcode        days recurrence





  
  

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sample_test_0  <span class="m">134</span>  <span class="m">1</span>





  
  

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sample_test_1  <span class="m">291</span>  <span class="m">0</span>





  
  

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sample_test_2  <span class="m">125</span>  <span class="m">1</span>





  
  

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sample_test_3  <span class="m">43</span>   <span class="m">0</span>





  
  

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...





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>In addition, the fields corresponding to the patient IDs, the survival time, and the event should be defined using the <code class="docutils literal notranslate"><span class="pre">survival_flag</span></code> argument:</p>





  
  

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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1">#Default value</span>





  
  

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<span class="n">survival_flag</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;patient_id&#39;</span><span class="p">:</span> <span class="s1">&#39;barcode&#39;</span><span class="p">,</span>





  
  

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                  <span class="s1">&#39;survival&#39;</span><span class="p">:</span> <span class="s1">&#39;days&#39;</span><span class="p">,</span>





  
  

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                 <span class="s1">&#39;event&#39;</span><span class="p">:</span> <span class="s1">&#39;recurrence&#39;</span><span class="p">}</span>





  
  

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</pre></div>





  
  

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</div>





  
  

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</div>





  
  

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<div class="section" id="creating-a-simple-deepprog-model-with-one-autoencoder-for-each-omic">





  
  

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<h2>Creating a simple DeepProg model with one autoencoder for each omic<a class="headerlink" href="#creating-a-simple-deepprog-model-with-one-autoencoder-for-each-omic" title="Permalink to this headline">¶</a></h2>





  
  

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<p>First, we will build a model using the example dataset from <code class="docutils literal notranslate"><span class="pre">./examples/data/</span></code> (These example files are set as default in the config.py file). We will use them to show how to construct a single DeepProg model inferring a autoencoder for each omic</p>





  
  

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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>





  
  

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<span class="c1"># SimDeep class can be used to build one model with one autoencoder for each omic</span>





  
  

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<span class="kn">from</span> <span class="nn">simdeep.simdeep_analysis</span> <span class="kn">import</span> <span class="n">SimDeep</span>





  
  

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<span class="kn">from</span> <span class="nn">simdeep.extract_data</span> <span class="kn">import</span> <span class="n">LoadData</span>





  
  

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<span class="n">help</span><span class="p">(</span><span class="n">SimDeep</span><span class="p">)</span> <span class="c1"># to see all the functions</span>





  
  

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<span class="n">help</span><span class="p">(</span><span class="n">LoadData</span><span class="p">)</span> <span class="c1"># to see all the functions related to loading datasets</span>





  
  

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<span class="c1"># Defining training datasets</span>





  
  

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<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">TRAINING_TSV</span>





  
  

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<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">SURVIVAL_TSV</span>





  
  

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<span class="c1"># Location of the input matrices and survival file</span>





  
  

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<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">PATH_DATA</span>





  
  

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<span class="n">dataset</span> <span class="o">=</span> <span class="n">LoadData</span><span class="p">(</span><span class="n">training_tsv</span><span class="o">=</span><span class="n">TRAINING_TSV</span><span class="p">,</span>





  
  

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        <span class="n">survival_tsv</span><span class="o">=</span><span class="n">SURVIVAL_TSV</span><span class="p">,</span>





  
  

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        <span class="n">path_data</span><span class="o">=</span><span class="n">PATH_DATA</span><span class="p">)</span>





  
  

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<span class="c1"># Defining the result path in which will be created an output folder</span>





  
  

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<span class="n">PATH_RESULTS</span> <span class="o">=</span> <span class="s2">&quot;./TEST_DUMMY/&quot;</span>





  
  

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<span class="c1"># instantiate the model with the dummy example training dataset defined in the config file</span>





  
  

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<span class="n">simDeep</span> <span class="o">=</span> <span class="n">SimDeep</span><span class="p">(</span>





  
  

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        <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>





  
  

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        <span class="n">path_results</span><span class="o">=</span><span class="n">PATH_RESULTS</span><span class="p">,</span>





  
  

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        <span class="n">path_to_save_modelPATH_RESULTS</span><span class="p">,</span> <span class="c1"># This result path can be used to save the autoencoder</span>





  
  

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        <span class="p">)</span>





  
  

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<span class="n">simDeep</span><span class="o">.</span><span class="n">load_training_dataset</span><span class="p">()</span> <span class="c1"># load the training dataset</span>





  
  

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<span class="n">simDeep</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span> <span class="c1"># fit the model</span>





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>At that point, the model is fitted and some output files are available in the output folder:</p>





  
  

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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>TEST_DUMMY





  
  

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├── test_dummy_dataset_KM_plot_training_dataset.png





  
  

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└── test_dummy_dataset_training_set_labels.tsv





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>The tsv file contains the label and the label probability for each sample:</p>





  
  

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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sample_test_0   <span class="m">1</span>       <span class="m">7</span>.22678272919e-12





  
  

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sample_test_1   <span class="m">1</span>       <span class="m">4</span>.48594196888e-09





  
  

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sample_test_4   <span class="m">1</span>       <span class="m">1</span>.53363205571e-06





  
  

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sample_test_5   <span class="m">1</span>       <span class="m">6</span>.72170409655e-08





  
  

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sample_test_6   <span class="m">0</span>       <span class="m">0</span>.9996581662





  
  

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sample_test_7   <span class="m">1</span>       <span class="m">3</span>.38139255666e-08





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>And we also have the visualisation of a Kaplan-Meier Curve:</p>





  
  

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<p><img alt="KM plot" src="_images/test_dummy_dataset_KM_plot_training_dataset.png" /></p>





  
  

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<p>Now we are ready to use a test dataset and to infer the class label for the test samples.





  
  

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The test dataset do not need to have the same input omic matrices than the training dataset and not even the sample features for a given omic. However, it needs to have at least some features in common.</p>





  
  

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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Defining test datasets</span>





  
  

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<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">TEST_TSV</span>





  
  

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<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">SURVIVAL_TSV_TEST</span>





  
  

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<span class="n">simDeep</span><span class="o">.</span><span class="n">load_new_test_dataset</span><span class="p">(</span>





  
  

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    <span class="n">TEST_TSV</span><span class="p">,</span>





  
  

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    <span class="n">fname_key</span><span class="o">=</span><span class="s1">&#39;dummy&#39;</span>





  
  

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    <span class="n">SURVIVAL_TSV_TEST</span><span class="p">,</span> <span class="c1"># [OPTIONAL] test survival file useful to compute accuracy of test dataset</span>





  
  

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    <span class="p">)</span>





  
  

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<span class="c1"># The test set is a dummy rna expression (generated randomly)</span>





  
  

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<span class="nb">print</span><span class="p">(</span><span class="n">simDeep</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">test_tsv</span><span class="p">)</span> <span class="c1"># Defined in the config file</span>





  
  

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<span class="c1"># The data type of the test set is also defined to match an existing type</span>





  
  

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<span class="nb">print</span><span class="p">(</span><span class="n">simDeep</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">data_type</span><span class="p">)</span> <span class="c1"># Defined in the config file</span>





  
  

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<span class="n">simDeep</span><span class="o">.</span><span class="n">predict_labels_on_test_dataset</span><span class="p">()</span> <span class="c1"># Perform the classification analysis and label the set dataset</span>





  
  

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<span class="nb">print</span><span class="p">(</span><span class="n">simDeep</span><span class="o">.</span><span class="n">test_labels</span><span class="p">)</span>





  
  

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<span class="nb">print</span><span class="p">(</span><span class="n">simDeep</span><span class="o">.</span><span class="n">test_labels_proba</span><span class="p">)</span>





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>The assigned class and class probabilities for the test samples are now available in the output folder:</p>





  
  

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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>TEST_DUMMY





  
  

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├── test_dummy_dataset_dummy_KM_plot_test.png





  
  

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├── test_dummy_dataset_dummy_test_labels.tsv





  
  

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├── test_dummy_dataset_KM_plot_training_dataset.png





  
  

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└── test_dummy_dataset_training_set_labels.tsv





  
  

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head test_dummy_dataset_training_set_labels.tsv





  
  

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</pre></div>





  
  

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</div>





  
  

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<p>And a KM plot is also constructed using the test labels</p>





  
  

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<p><img alt="KM plot test" src="_images/test_dummy_dataset_dummy_KM_plot_test.png" /></p>





  
  

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<p>Finally, it is possible to save the keras model:</p>





  
  

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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">simDeep</span><span class="o">.</span><span class="n">save_encoders</span><span class="p">(</span><span class="s1">&#39;dummy_encoder.h5&#39;</span><span class="p">)</span>





  
  

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</pre></div>





  
  

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