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Alyssa Salazar
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  <div class="section" id="survival-integration-of-multi-omics-using-deep-learning-deepprog">
<h1>Survival Integration of Multi-omics using Deep-Learning (DeepProg)<a class="headerlink" href="#survival-integration-of-multi-omics-using-deep-learning-deepprog" title="Permalink to this headline">¶</a></h1>
<p>This package allows to combine multi-omics data together with survival. Using autoencoders, the pipeline creates new features and identify those linked with survival, using CoxPH regression.
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.</p>
<p>The current package contains the omic data used in the study and a copy of the model computed. However, it is very 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 uses Keras, which is a embedding of Theano / tensorflow/ CNTK.</p>
<div class="section" id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Python 2 or 3</li>
<li><a class="reference external" href="http://deeplearning.net/software/theano/install.html">theano</a> (the used version for the manuscript was 0.8.2)</li>
<li><a class="reference external" href="https://www.tensorflow.org/">tensorflow</a> as a more robust alternative to theano</li>
<li><a class="reference external" href="https://github.com/microsoft/CNTK">cntk</a> CNTK is anoter DL library that can present some advantages compared to tensorflow or theano. See <a class="reference external" href="https://docs.microsoft.com/en-us/cognitive-toolkit/">https://docs.microsoft.com/en-us/cognitive-toolkit/</a></li>
<li>R</li>
<li>the R “survival” package installed.</li>
<li>numpy, scipy</li>
<li>scikit-learn (&gt;=0.18)</li>
<li>rpy2 2.8.6 (for python2 rpy2 can be install with: pip install rpy2==2.8.6, for python3 pip3 install rpy2==2.8.6). It seems that newer version of rpy2 might not work due to a bug (not tested)</li>
</ul>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install theano --user <span class="c1"># Original backend used OR</span>
pip install tensorflow --user <span class="c1"># Alternative backend for keras supposely for efficient</span>
pip install keras --user
pip install <span class="nv">rpy2</span><span class="o">==</span><span class="m">2</span>.8.6 --user

<span class="c1">#If you want to use theano or CNTK</span>
nano ~/.keras/keras.json
</pre></div>
</div>
<ul class="simple">
<li>R installation</li>
</ul>
<div class="highlight-R notranslate"><div class="highlight"><pre><span></span><span class="nf">install.package</span><span class="p">(</span><span class="s">&quot;survival&quot;</span><span class="p">)</span>
<span class="nf">install.package</span><span class="p">(</span><span class="s">&quot;glmnet&quot;</span><span class="p">)</span>
<span class="nf">source</span><span class="p">(</span><span class="s">&quot;https://bioconductor.org/biocLite.R&quot;</span><span class="p">)</span>
<span class="nf">biocLite</span><span class="p">(</span><span class="s">&quot;survcomp&quot;</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="support-for-cntk-tensorflow">
<h3>Support for CNTK / tensorflow<a class="headerlink" href="#support-for-cntk-tensorflow" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li>We originally used Keras with theano as backend plateform. However, <a class="reference external" href="https://www.tensorflow.org/">Tensorflow</a> or <a class="reference external" href="https://docs.microsoft.com/en-us/cognitive-toolkit/">CNTK</a> are more recent DL framework that can be faster or more stable than theano. Because keras supports these 3 backends, it is possible to use them as alternative to theano. To change backend, please configure the <code class="docutils literal notranslate"><span class="pre">$HOME/.keras/keras.json</span></code> file. (See official instruction <a class="reference external" href="https://keras.io/backend/">here</a>).</li>
</ul>
<p>The default configuration file looks like this:</p>
<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
    <span class="nt">&quot;image_data_format&quot;</span><span class="p">:</span> <span class="s2">&quot;channels_last&quot;</span><span class="p">,</span>
    <span class="nt">&quot;epsilon&quot;</span><span class="p">:</span> <span class="mf">1e-07</span><span class="p">,</span>
    <span class="nt">&quot;floatx&quot;</span><span class="p">:</span> <span class="s2">&quot;float32&quot;</span><span class="p">,</span>
    <span class="nt">&quot;backend&quot;</span><span class="p">:</span> <span class="s2">&quot;tensorflow&quot;</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="distributed-computation">
<h2>Distributed computation<a class="headerlink" href="#distributed-computation" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>It is possible to use the python ray framework <a class="reference external" href="https://github.com/ray-project/ray">https://github.com/ray-project/ray</a> to control the parallel computation of the multiple models. To use this framework, it is required to install it: <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">ray</span> <span class="pre">--user</span></code></li>
<li>Alternatively, it is also possible to create the model one by one without the need of the ray framework</li>
</ul>
</div>
<div class="section" id="visualisation-module-experimental">
<h2>Visualisation module (Experimental)<a class="headerlink" href="#visualisation-module-experimental" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>To visualise test sets projected into the multi-omic survival space, it is required to install <code class="docutils literal notranslate"><span class="pre">mpld3</span></code> module: <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">mpld3</span> <span class="pre">--user</span></code></li>
<li>Note that the pip version of mpld3 installed on my computer presented a <a class="reference external" href="https://github.com/mpld3/mpld3/issues/434">bug</a>: <code class="docutils literal notranslate"><span class="pre">TypeError:</span> <span class="pre">array([1.])</span> <span class="pre">is</span> <span class="pre">not</span> <span class="pre">JSON</span> <span class="pre">serializable</span> </code>. However, the <a class="reference external" href="https://github.com/mpld3/mpld3">newest</a> version of the mpld3 available from the github solved this issue. It is therefore recommended to install the newest version to avoid this issue.</li>
</ul>
</div>
<div class="section" id="installation-local">
<h2>installation (local)<a class="headerlink" href="#installation-local" title="Permalink to this headline">¶</a></h2>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git clone https://github.com/lanagarmire/SimDeep.git
<span class="nb">cd</span> SimDeep
pip install -r requirements.txt --user
</pre></div>
</div>
</div>
<div class="section" id="usage">
<h2>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>test if simdeep is functional (all the software are correctly installed):</li>
</ul>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>  python test/test_dummy_boosting_stacking.py -v <span class="c1"># OR</span>
  nosetests <span class="nb">test</span> -v <span class="c1"># Improved version of python unit testing</span>
</pre></div>
</div>
<ul class="simple">
<li>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:<ul>
<li>The training dataset (omics + survival input files)<ul>
<li>In addition, the parameters of the test set, i.e. the omic dataset and the survival file</li>
</ul>
</li>
<li>The parameters of the autoencoder (the default parameters works but it might be fine-tuned.</li>
<li>The parameters of the classification procedures (default are still good)</li>
</ul>
</li>
</ul>
</div>
<div class="section" id="example-datasets-and-scripts">
<h2>Example datasets and scripts<a class="headerlink" href="#example-datasets-and-scripts" title="Permalink to this headline">¶</a></h2>
<p>An omic .tsv file must have this format:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>head mir_dummy.tsv

Samples        dummy_mir_0     dummy_mir_1     dummy_mir_2     dummy_mir_3 ...
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 ...
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 ...
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 ...
...
</pre></div>
</div>
<p>a survival file must have this format:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>head survival_dummy.tsv

Samples        days event
sample_test_0  <span class="m">134</span>  <span class="m">1</span>
sample_test_1  <span class="m">291</span>  <span class="m">0</span>
sample_test_2  <span class="m">125</span>  <span class="m">1</span>
sample_test_3  <span class="m">43</span>   <span class="m">0</span>
...
</pre></div>
</div>
<p>As examples, we included two datasets:</p>
<ul class="simple">
<li>A dummy example dataset in the <code class="docutils literal notranslate"><span class="pre">example/data/</span></code> folder:</li>
</ul>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>examples
├── data
│   ├── meth_dummy.tsv
│   ├── mir_dummy.tsv
│   ├── rna_dummy.tsv
│   ├── rna_test_dummy.tsv
│   ├── survival_dummy.tsv
│   └── survival_test_dummy.tsv
</pre></div>
</div>
<ul class="simple">
<li>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:</li>
</ul>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>data
├── meth.tsv.gz
├── mir.tsv.gz
├── rna.tsv.gz
└── survival.tsv
</pre></div>
</div>
</div>
<div class="section" id="creating-a-simple-deepprog-model-with-one-autoencoder-for-each-omic">
<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>
<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>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>
<span class="c1"># SimDeep class can be used to build one model with one autoencoder for each omic</span>
<span class="kn">from</span> <span class="nn">simdeep.simdeep_analysis</span> <span class="kn">import</span> <span class="n">SimDeep</span>
<span class="kn">from</span> <span class="nn">simdeep.extract_data</span> <span class="kn">import</span> <span class="n">LoadData</span>

<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>
<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>

<span class="c1"># Defining training datasets</span>
<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">TRAINING_TSV</span>
<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">SURVIVAL_TSV</span>

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

<span class="n">simDeep</span> <span class="o">=</span> <span class="n">SimDeep</span><span class="p">(</span><span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">)</span> <span class="c1"># instantiate the model with the dummy example training dataset defined in the config file</span>
<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>
<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>

<span class="c1"># Defining test datasets</span>
<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">TEST_TSV</span>
<span class="kn">from</span> <span class="nn">simdeep.config</span> <span class="kn">import</span> <span class="n">SURVIVAL_TSV_TEST</span>

<span class="n">simDeep</span><span class="o">.</span><span class="n">load_new_test_dataset</span><span class="p">(</span><span class="n">TEST_TSV</span><span class="p">,</span> <span class="n">SURVIVAL_TSV_TEST</span><span class="p">,</span> <span class="n">fname_key</span><span class="o">=</span><span class="s1">&#39;dummy&#39;</span><span class="p">)</span>

<span class="c1"># The test set is a dummy rna expression (generated randomly)</span>
<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>
<span class="c1"># The data type of the test set is also defined to match an existing type</span>
<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>
<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>

<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>
<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>

<span class="n">simDeep</span><span class="o">.</span><span class="n">save_encoder</span><span class="p">(</span><span class="s1">&#39;dummy_encoder.h5&#39;</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-a-deepprog-model-using-an-ensemble-of-submodels">
<h2>Creating a DeepProg model using an ensemble of submodels<a class="headerlink" href="#creating-a-deepprog-model-using-an-ensemble-of-submodels" title="Permalink to this headline">¶</a></h2>
<p>Secondly, we will build a more complex DeepProg model constituted of an ensemble of sub-models each originated from a subset of the data. For that purpose, we need to use the <code class="docutils literal notranslate"><span class="pre">SimDeepBoosting</span></code> class:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">simdeep.simdeep_boosting</span> <span class="kn">import</span> <span class="n">SimDeepBoosting</span>

<span class="n">help</span><span class="p">(</span><span class="n">SimDeepBoosting</span><span class="p">)</span>

<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>


<span class="n">path_data</span> <span class="o">=</span> <span class="s2">&quot;../examples/data/&quot;</span>
<span class="c1"># Example tsv files</span>
<span class="n">tsv_files</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">([</span>
          <span class="p">(</span><span class="s1">&#39;MIR&#39;</span><span class="p">,</span> <span class="s1">&#39;mir_dummy.tsv&#39;</span><span class="p">),</span>
          <span class="p">(</span><span class="s1">&#39;METH&#39;</span><span class="p">,</span> <span class="s1">&#39;meth_dummy.tsv&#39;</span><span class="p">),</span>
          <span class="p">(</span><span class="s1">&#39;RNA&#39;</span><span class="p">,</span> <span class="s1">&#39;rna_dummy.tsv&#39;</span><span class="p">),</span>
<span class="p">])</span>

<span class="c1"># File with survival event</span>
<span class="n">survival_tsv</span> <span class="o">=</span> <span class="s1">&#39;survival_dummy.tsv&#39;</span>

<span class="n">project_name</span> <span class="o">=</span> <span class="s1">&#39;stacked_TestProject&#39;</span>
<span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># Autoencoder epochs. Other hyperparameters can be fine-tuned. See the example files</span>
<span class="n">seed</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># random seed used for reproducibility</span>
<span class="n">nb_it</span> <span class="o">=</span> <span class="mi">5</span> <span class="c1"># This is the number of models to be fitted using only a subset of the training data</span>
<span class="n">nb_threads</span> <span class="o">=</span> <span class="mi">2</span> <span class="c1"># These treads define the number of threads to be used to compute survival function</span>

<span class="n">boosting</span> <span class="o">=</span> <span class="n">SimDeepBoosting</span><span class="p">(</span>
    <span class="n">nb_threads</span><span class="o">=</span><span class="n">nb_threads</span><span class="p">,</span>
    <span class="n">nb_it</span><span class="o">=</span><span class="n">nb_it</span><span class="p">,</span>
    <span class="n">split_n_fold</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
    <span class="n">survival_tsv</span><span class="o">=</span><span class="n">tsv_files</span><span class="p">,</span>
    <span class="n">training_tsv</span><span class="o">=</span><span class="n">survival_tsv</span><span class="p">,</span>
    <span class="n">path_data</span><span class="o">=</span><span class="n">path_data</span><span class="p">,</span>
    <span class="n">project_name</span><span class="o">=</span><span class="n">project_name</span><span class="p">,</span>
    <span class="n">path_results</span><span class="o">=</span><span class="n">path_data</span><span class="p">,</span>
    <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span>
    <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>

<span class="c1"># Fit the model</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="c1"># Predict and write the labels</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">predict_labels_on_full_dataset</span><span class="p">()</span>
<span class="c1"># Compute internal metrics</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">compute_clusters_consistency_for_full_labels</span><span class="p">()</span>
<span class="c1"># COmpute the feature importance</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">compute_feature_scores_per_cluster</span><span class="p">()</span>
<span class="c1"># Write the feature importance</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">write_feature_score_per_cluster</span><span class="p">()</span>

<span class="n">boosting</span><span class="o">.</span><span class="n">load_new_test_dataset</span><span class="p">(</span>
    <span class="p">{</span><span class="s1">&#39;RNA&#39;</span><span class="p">:</span> <span class="s1">&#39;rna_dummy.tsv&#39;</span><span class="p">},</span> <span class="c1"># OMIC file of the test set. It doesnt have to be the same as for training</span>
    <span class="s1">&#39;survival_dummy.tsv&#39;</span><span class="p">,</span> <span class="c1"># Survival file of the test set</span>
    <span class="s1">&#39;TEST_DATA_1&#39;</span><span class="p">,</span> <span class="c1"># Name of the test test to be used</span>
<span class="p">)</span>

<span class="c1"># Predict the labels on the test dataset</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">predict_labels_on_test_dataset</span><span class="p">()</span>
<span class="c1"># Compute C-index</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">compute_c_indexes_for_test_dataset</span><span class="p">()</span>
<span class="c1"># See cluster consistency</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">compute_clusters_consistency_for_test_labels</span><span class="p">()</span>

<span class="c1"># [EXPERIMENTAL] method to plot the test dataset amongst the class kernel densities</span>
<span class="n">boosting</span><span class="o">.</span><span class="n">plot_supervised_kernel_for_test_sets</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-a-distributed-deepprog-model-using-an-ensemble-of-submodels">
<h2>Creating a distributed DeepProg model using an ensemble of submodels<a class="headerlink" href="#creating-a-distributed-deepprog-model-using-an-ensemble-of-submodels" title="Permalink to this headline">¶</a></h2>
<p>We can allow DeepProg to distribute the creation of each submodel on different clusters/nodes/CPUs by using the ray framework.
The configuration of the nodes / clusters, or local CPUs to be used needs to be done when instanciating a new ray object with the ray <a class="reference external" href="https://ray.readthedocs.io/en/latest/">API</a>. It is however quite straightforward to define the number of instances launched on a local machine such as in the example below in which 3 instances are used.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Instanciate a ray object that will create multiple workers</span>
<span class="kn">import</span> <span class="nn">ray</span>
<span class="n">ray</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">num_cpus</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="c1"># More options can be used (e.g. remote clusters, AWS, memory,...etc...)</span>
<span class="c1"># ray can be used locally to maximize the use of CPUs on the local machine</span>
<span class="c1"># See ray API: https://ray.readthedocs.io/en/latest/index.html</span>

<span class="n">boosting</span> <span class="o">=</span> <span class="n">SimDeepBoosting</span><span class="p">(</span>
    <span class="o">...</span>
    <span class="n">distribute</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># Additional option to use ray cluster scheduler</span>
    <span class="o">...</span>
<span class="p">)</span>
<span class="o">...</span>
<span class="c1"># Processing</span>
<span class="o">...</span>

<span class="c1"># Close clusters and free memory</span>
<span class="n">ray</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="example-scripts">
<h2>Example scripts<a class="headerlink" href="#example-scripts" title="Permalink to this headline">¶</a></h2>
<p>Example scripts are availables in ./examples/ which will assist you to build a model from scratch with test and real data:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>examples
├── create_autoencoder_from_scratch.py <span class="c1"># Construct a simple deeprog model on the dummy example dataset</span>
├── example_with_dummy_data_distributed.py <span class="c1"># Process the dummy example dataset using ray</span>
├── example_with_dummy_data.py <span class="c1"># Process the dummy example dataset</span>
└── load_3_omics_model.py <span class="c1"># Process the example HCC dataset</span>
</pre></div>
</div>
</div>
<div class="section" id="contact-and-credentials">
<h2>contact and credentials<a class="headerlink" href="#contact-and-credentials" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Developer: Olivier Poirion (PhD)</li>
<li>contact: opoirion&#64;hawaii.edu, o.poirion&#64;gmail.com</li>
</ul>
</div>
</div>


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