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  <main id="main" class="col-md-9"><div class="page-header">
      <img src="../logo.png" class="logo" alt=""><h1>Getting started</h1>
            
      
      <small class="dont-index">Source: <a href="https://github.com/GenomeNet/deepG/blob/HEAD/vignettes/getting_started.Rmd" class="external-link"><code>vignettes/getting_started.Rmd</code></a></small>
      <div class="d-none name"><code>getting_started.Rmd</code></div>
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<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co">#devtools::install_github("GenomeNet/deepG")</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/GenomeNet/deepG" class="external-link">deepG</a></span><span class="op">)</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://magrittr.tidyverse.org" class="external-link">magrittr</a></span><span class="op">)</span></span></code></pre></div>
<style type="text/css">
mark.in {
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<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
<p>The goal of the deepG package is to speed up the development of
bioinformatical tools for sequence classification, homology detection
and other bioinformatical tasks. The package offers several functions
for</p>
<ul>
<li>Data (pre-) processing</li>
<li>Deep learning architectures</li>
<li>Model training</li>
<li>Model evaluation</li>
<li>Visualizing training progress</li>
</ul>
<div class="section level3">
<h3 id="create-dummy-data">Create dummy data<a class="anchor" aria-label="anchor" href="#create-dummy-data"></a>
</h3>
<p>We create two simple dummy training and validation data sets. Both
consist of random <tt>ACGT</tt> sequences but the first category has a
probability of 40% each for drawing <tt>G</tt> or <tt>C</tt> and the
second has equal probability for each nucleotide (first category has
around 80% <tt>GC</tt> content and second one around 50%).</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
<span><span class="va">vocabulary</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A"</span>, <span class="st">"C"</span>, <span class="st">"G"</span>, <span class="st">"T"</span><span class="op">)</span></span>
<span></span>
<span><span class="va">data_type</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"train_1"</span>, <span class="st">"train_2"</span>, <span class="st">"val_1"</span>, <span class="st">"val_2"</span><span class="op">)</span></span>
<span></span>
<span><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">data_type</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
<span>  </span>
<span>  <span class="va">temp_file</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/tempfile.html" class="external-link">tempfile</a></span><span class="op">(</span><span class="op">)</span></span>
<span>  <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">data_type</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, <span class="st">"_dir"</span><span class="op">)</span>, <span class="va">temp_file</span><span class="op">)</span></span>
<span>  <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="va">temp_file</span><span class="op">)</span></span>
<span>  </span>
<span>  <span class="kw">if</span> <span class="op">(</span><span class="va">i</span> <span class="op"><a href="https://rdrr.io/r/base/Arithmetic.html" class="external-link">%%</a></span> <span class="fl">2</span> <span class="op">==</span> <span class="fl">1</span><span class="op">)</span> <span class="op">{</span></span>
<span>    <span class="va">header</span> <span class="op">&lt;-</span> <span class="st">"high_gc"</span></span>
<span>    <span class="va">prob</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.4</span>, <span class="fl">0.4</span>, <span class="fl">0.1</span><span class="op">)</span></span>
<span>  <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
<span>    <span class="va">header</span> <span class="op">&lt;-</span> <span class="st">"equal_dist"</span></span>
<span>    <span class="va">prob</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">4</span><span class="op">)</span></span>
<span>  <span class="op">}</span></span>
<span>  </span>
<span>  <span class="va">fasta_name_start</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">header</span>, <span class="st">"_"</span>, <span class="va">data_type</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, <span class="st">"file"</span><span class="op">)</span></span>
<span>  </span>
<span>  <span class="fu"><a href="../reference/create_dummy_data.html">create_dummy_data</a></span><span class="op">(</span>file_path <span class="op">=</span> <span class="va">temp_file</span>,</span>
<span>                    num_files <span class="op">=</span> <span class="fl">1</span>,</span>
<span>                    seq_length <span class="op">=</span> <span class="fl">10000</span>, </span>
<span>                    num_seq <span class="op">=</span> <span class="fl">1</span>,</span>
<span>                    header <span class="op">=</span> <span class="va">header</span>,</span>
<span>                    prob <span class="op">=</span> <span class="va">prob</span>,</span>
<span>                    fasta_name_start <span class="op">=</span> <span class="va">fasta_name_start</span>,</span>
<span>                    vocabulary <span class="op">=</span> <span class="va">vocabulary</span><span class="op">)</span></span>
<span>  </span>
<span><span class="op">}</span></span></code></pre></div>
</div>
<div class="section level3">
<h3 id="training">Training<a class="anchor" aria-label="anchor" href="#training"></a>
</h3>
<p>We can now train a model that can differentiate between the two
categories. First, we can create our network architecture. We take an
input size of 50 nucleotides. The model has one lstm layer with 16 cells
and two dense layers with 8 and 2 neurons.</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">maxlen</span> <span class="op">&lt;-</span> <span class="fl">50</span></span>
<span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/create_model_lstm_cnn.html">create_model_lstm_cnn</a></span><span class="op">(</span>maxlen <span class="op">=</span> <span class="va">maxlen</span>,</span>
<span>                               layer_lstm <span class="op">=</span> <span class="fl">16</span>,</span>
<span>                               layer_dense <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">8</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## Model: "model"</span></span>
<span><span class="co">## _________________________________________________________________</span></span>
<span><span class="co">##  Layer (type)                Output Shape              Param #   </span></span>
<span><span class="co">## =================================================================</span></span>
<span><span class="co">##  input_1 (InputLayer)        [(None, 50, 4)]           0         </span></span>
<span><span class="co">##                                                                  </span></span>
<span><span class="co">##  lstm (LSTM)                 (None, 16)                1344      </span></span>
<span><span class="co">##                                                                  </span></span>
<span><span class="co">##  dense (Dense)               (None, 8)                 136       </span></span>
<span><span class="co">##                                                                  </span></span>
<span><span class="co">##  dense_1 (Dense)             (None, 2)                 18        </span></span>
<span><span class="co">##                                                                  </span></span>
<span><span class="co">## =================================================================</span></span>
<span><span class="co">## Total params: 1498 (5.85 KB)</span></span>
<span><span class="co">## Trainable params: 1498 (5.85 KB)</span></span>
<span><span class="co">## Non-trainable params: 0 (0.00 Byte)</span></span>
<span><span class="co">## _________________________________________________________________</span></span></code></pre>
<p>Next we can train the model using the <code>train_model</code>
function. Function will internally build a data generator for
training.</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">hist</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/train_model.html">train_model</a></span><span class="op">(</span><span class="va">model</span>,</span>
<span>                    train_type <span class="op">=</span> <span class="st">"label_folder"</span>,</span>
<span>                    run_name <span class="op">=</span> <span class="st">"gc_model_1"</span>,</span>
<span>                    path <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">train_1_dir</span>, <span class="va">train_2_dir</span><span class="op">)</span>,</span>
<span>                    path_val <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">val_1_dir</span>, <span class="va">val_2_dir</span><span class="op">)</span>,</span>
<span>                    epochs <span class="op">=</span> <span class="fl">4</span>,</span>
<span>                    steps_per_epoch <span class="op">=</span> <span class="fl">25</span>, <span class="co"># one epoch = 25 batches</span></span>
<span>                    batch_size <span class="op">=</span> <span class="fl">64</span>,</span>
<span>                    step <span class="op">=</span> <span class="fl">50</span>, <span class="co"># take a sample every 50 nt</span></span>
<span>                    vocabulary_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"high_gc"</span>, <span class="st">"equal_dist"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## Epoch 1/4</span></span>
<span><span class="co">##  1/25 [&gt;.............................] - ETA: 21s - loss: 0.7058 - acc: 0.5938 4/25 [===&gt;..........................] - ETA: 0s - loss: 0.7028 - acc: 0.5430  7/25 [=======&gt;......................] - ETA: 0s - loss: 0.7013 - acc: 0.533510/25 [===========&gt;..................] - ETA: 0s - loss: 0.6976 - acc: 0.539114/25 [===============&gt;..............] - ETA: 0s - loss: 0.6935 - acc: 0.559217/25 [===================&gt;..........] - ETA: 0s - loss: 0.6900 - acc: 0.577221/25 [========================&gt;.....] - ETA: 0s - loss: 0.6860 - acc: 0.607124/25 [===========================&gt;..] - ETA: 0s - loss: 0.6821 - acc: 0.624325/25 [==============================] - 2s 31ms/step - loss: 0.6813 - acc: 0.6256 - val_loss: 0.6511 - val_acc: 0.7563 - lr: 0.0010</span></span>
<span><span class="co">## Epoch 2/4</span></span>
<span><span class="co">##  1/25 [&gt;.............................] - ETA: 0s - loss: 0.6379 - acc: 0.8281 4/25 [===&gt;..........................] - ETA: 0s - loss: 0.6423 - acc: 0.7617 8/25 [========&gt;.....................] - ETA: 0s - loss: 0.6340 - acc: 0.785212/25 [=============&gt;................] - ETA: 0s - loss: 0.6228 - acc: 0.789116/25 [==================&gt;...........] - ETA: 0s - loss: 0.6086 - acc: 0.805720/25 [=======================&gt;......] - ETA: 0s - loss: 0.5892 - acc: 0.825824/25 [===========================&gt;..] - ETA: 0s - loss: 0.5650 - acc: 0.850925/25 [==============================] - 1s 21ms/step - loss: 0.5590 - acc: 0.8556 - val_loss: 0.3910 - val_acc: 0.9719 - lr: 0.0010</span></span>
<span><span class="co">## Epoch 3/4</span></span>
<span><span class="co">##  1/25 [&gt;.............................] - ETA: 0s - loss: 0.3548 - acc: 1.0000 4/25 [===&gt;..........................] - ETA: 0s - loss: 0.3463 - acc: 0.9883 8/25 [========&gt;.....................] - ETA: 0s - loss: 0.3230 - acc: 0.976611/25 [============&gt;.................] - ETA: 0s - loss: 0.3052 - acc: 0.975915/25 [=================&gt;............] - ETA: 0s - loss: 0.2893 - acc: 0.970817/25 [===================&gt;..........] - ETA: 0s - loss: 0.2792 - acc: 0.970621/25 [========================&gt;.....] - ETA: 0s - loss: 0.2665 - acc: 0.969524/25 [===========================&gt;..] - ETA: 0s - loss: 0.2547 - acc: 0.971425/25 [==============================] - 1s 21ms/step - loss: 0.2533 - acc: 0.9706 - val_loss: 0.1765 - val_acc: 0.9719 - lr: 0.0010</span></span>
<span><span class="co">## Epoch 4/4</span></span>
<span><span class="co">##  1/25 [&gt;.............................] - ETA: 0s - loss: 0.1369 - acc: 1.0000 4/25 [===&gt;..........................] - ETA: 0s - loss: 0.1456 - acc: 0.9922 7/25 [=======&gt;......................] - ETA: 0s - loss: 0.1494 - acc: 0.986610/25 [===========&gt;..................] - ETA: 0s - loss: 0.1425 - acc: 0.987514/25 [===============&gt;..............] - ETA: 0s - loss: 0.1376 - acc: 0.986617/25 [===================&gt;..........] - ETA: 0s - loss: 0.1315 - acc: 0.987121/25 [========================&gt;.....] - ETA: 0s - loss: 0.1259 - acc: 0.986625/25 [==============================] - ETA: 0s - loss: 0.1225 - acc: 0.985025/25 [==============================] - 1s 21ms/step - loss: 0.1225 - acc: 0.9850 - val_loss: 0.0992 - val_acc: 0.9812 - lr: 0.0010</span></span></code></pre>
<pre><code><span><span class="co">## Training done.</span></span></code></pre>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">hist</span><span class="op">)</span></span></code></pre></div>
<p><img src="getting_started_files/figure-html/unnamed-chunk-7-1.png" width="700"></p>
</div>
<div class="section level3">
<h3 id="evaluation">Evaluation<a class="anchor" aria-label="anchor" href="#evaluation"></a>
</h3>
<p>We can now evaluate the trained model on all the validation data</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">eval</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/evaluate_model.html">evaluate_model</a></span><span class="op">(</span>path_input <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">val_1_dir</span>, <span class="va">val_2_dir</span><span class="op">)</span>,</span>
<span>                       model <span class="op">=</span> <span class="va">model</span>,</span>
<span>                       batch_size <span class="op">=</span> <span class="fl">100</span>,</span>
<span>                       step <span class="op">=</span> <span class="fl">25</span>, <span class="co"># take a sample every 25 nt </span></span>
<span>                       vocabulary_label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"high_gc"</span>, <span class="st">"equal_dist"</span><span class="op">)</span><span class="op">)</span>,</span>
<span>                       mode <span class="op">=</span> <span class="st">"label_folder"</span>,</span>
<span>                       evaluate_all_files <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>                       verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span>                       auc <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>                       auprc <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## Evaluate 399 samples for class high_gc.</span></span>
<span><span class="co">## Evaluate 399 samples for class equal_dist.</span></span></code></pre>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">eval</span></span></code></pre></div>
<pre><code><span><span class="co">## [[1]]</span></span>
<span><span class="co">## [[1]]$confusion_matrix</span></span>
<span><span class="co">##             Truth</span></span>
<span><span class="co">## Prediction   high_gc equal_dist</span></span>
<span><span class="co">##   high_gc        383          5</span></span>
<span><span class="co">##   equal_dist      16        394</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## [[1]]$accuracy</span></span>
<span><span class="co">## [1] 0.9736842</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## [[1]]$categorical_crossentropy_loss</span></span>
<span><span class="co">## [1] 0.1157783</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## [[1]]$AUC</span></span>
<span><span class="co">## [1] 0.9968593</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## [[1]]$AUPRC</span></span>
<span><span class="co">## [1] 0.9968503</span></span></code></pre>
<p>We can check where our model made mistakes for the sequence with high
GC content.</p>
<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">high_gc_file</span> <span class="op">&lt;-</span> <span class="fu">microseq</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/microseq/man/readFasta.html" class="external-link">readFasta</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.files.html" class="external-link">list.files</a></span><span class="op">(</span><span class="va">val_1_dir</span>, full.names <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
<span><span class="va">high_gc_seq</span> <span class="op">&lt;-</span> <span class="va">high_gc_file</span><span class="op">$</span><span class="va">Sequence</span></span>
<span></span>
<span><span class="va">pred_high_gc</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/predict_model.html">predict_model</a></span><span class="op">(</span>model <span class="op">=</span> <span class="va">model</span>, </span>
<span>                              sequence <span class="op">=</span> <span class="va">high_gc_seq</span>,</span>
<span>                              filename <span class="op">=</span> <span class="cn">NULL</span>, </span>
<span>                              step <span class="op">=</span> <span class="fl">25</span>,</span>
<span>                              batch_size <span class="op">=</span> <span class="fl">512</span>,</span>
<span>                              verbose <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>                              return_states <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>                              mode <span class="op">=</span> <span class="st">"label"</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## layer_name not specified. Using layer dense_1</span></span></code></pre>
<pre><code><span><span class="co">## Computing output for model at layer dense_1 </span></span>
<span><span class="co">## Model: "model_1"</span></span>
<span><span class="co">## ________________________________________________________________________________</span></span>
<span><span class="co">##  Layer (type)                       Output Shape                    Param #     </span></span>
<span><span class="co">## ================================================================================</span></span>
<span><span class="co">##  input_1 (InputLayer)               [(None, 50, 4)]                 0           </span></span>
<span><span class="co">##  lstm (LSTM)                        (None, 16)                      1344        </span></span>
<span><span class="co">##  dense (Dense)                      (None, 8)                       136         </span></span>
<span><span class="co">##  dense_1 (Dense)                    (None, 2)                       18          </span></span>
<span><span class="co">## ================================================================================</span></span>
<span><span class="co">## Total params: 1498 (5.85 KB)</span></span>
<span><span class="co">## Trainable params: 1498 (5.85 KB)</span></span>
<span><span class="co">## Non-trainable params: 0 (0.00 Byte)</span></span>
<span><span class="co">## ________________________________________________________________________________</span></span></code></pre>
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">pred_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="va">pred_high_gc</span><span class="op">$</span><span class="va">states</span>, <span class="va">pred_high_gc</span><span class="op">$</span><span class="va">sample_end_position</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> </span>
<span>  <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">pred_df</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"high_gc_conf"</span>, <span class="st">"equal_dist_conf"</span>, <span class="st">"sample_end_position"</span><span class="op">)</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">pred_df</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">##   high_gc_conf equal_dist_conf sample_end_position</span></span>
<span><span class="co">## 1    0.9330443      0.06695572                  50</span></span>
<span><span class="co">## 2    0.9602452      0.03975480                  75</span></span>
<span><span class="co">## 3    0.9642879      0.03571207                 100</span></span>
<span><span class="co">## 4    0.9596730      0.04032708                 125</span></span>
<span><span class="co">## 5    0.9617251      0.03827484                 150</span></span>
<span><span class="co">## 6    0.9666333      0.03336672                 175</span></span></code></pre>
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">wrong_pred</span> <span class="op">&lt;-</span> <span class="va">pred_df</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">high_gc_conf</span> <span class="op">&lt;</span> <span class="fl">0.5</span><span class="op">)</span></span>
<span><span class="va">wrong_pred</span></span></code></pre></div>
<pre><code><span><span class="co">##    high_gc_conf equal_dist_conf sample_end_position</span></span>
<span><span class="co">## 1    0.13769490       0.8623052                 675</span></span>
<span><span class="co">## 2    0.08829107       0.9117089                 800</span></span>
<span><span class="co">## 3    0.15268661       0.8473134                1150</span></span>
<span><span class="co">## 4    0.10348237       0.8965176                1475</span></span>
<span><span class="co">## 5    0.10325063       0.8967494                1950</span></span>
<span><span class="co">## 6    0.08819685       0.9118031                2700</span></span>
<span><span class="co">## 7    0.08648270       0.9135173                3000</span></span>
<span><span class="co">## 8    0.08025692       0.9197431                3700</span></span>
<span><span class="co">## 9    0.10362279       0.8963772                4675</span></span>
<span><span class="co">## 10   0.21332431       0.7866758                7550</span></span>
<span><span class="co">## 11   0.14129026       0.8587097                7875</span></span>
<span><span class="co">## 12   0.37184438       0.6281556                8200</span></span>
<span><span class="co">## 13   0.07818010       0.9218199                8225</span></span>
<span><span class="co">## 14   0.24804193       0.7519581                8425</span></span>
<span><span class="co">## 15   0.30556497       0.6944351                9900</span></span>
<span><span class="co">## 16   0.10370088       0.8962991                9925</span></span></code></pre>
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">wrong_pred</span><span class="op">)</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span></span>
<span>  <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="st">"All predictions for high GC content class correct"</span><span class="op">)</span></span>
<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
<span>  </span>
<span>  <span class="co"># extract samples where model was wrong</span></span>
<span>  <span class="va">wrong_pred_seq</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/vector.html" class="external-link">vector</a></span><span class="op">(</span><span class="st">"character"</span>, <span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">wrong_pred</span><span class="op">)</span><span class="op">)</span></span>
<span>  <span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">wrong_pred_seq</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
<span>    <span class="va">sample_end</span> <span class="op">&lt;-</span> <span class="va">wrong_pred</span><span class="op">$</span><span class="va">sample_end_position</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span>
<span>    <span class="va">sample_start</span> <span class="op">&lt;-</span> <span class="va">sample_end</span> <span class="op">-</span> <span class="va">maxlen</span> <span class="op">+</span> <span class="fl">1</span></span>
<span>    <span class="va">wrong_pred_seq</span><span class="op">[</span><span class="va">i</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/substr.html" class="external-link">substr</a></span><span class="op">(</span><span class="va">high_gc_seq</span>, <span class="va">sample_start</span>, <span class="va">sample_end</span><span class="op">)</span></span>
<span>  <span class="op">}</span></span>
<span>  </span>
<span>  <span class="va">wrong_pred_seq</span></span>
<span><span class="op">}</span></span></code></pre></div>
<pre><code><span><span class="co">##  [1] "CTTAGAGACCTCGCCGCCACCGCCCGAGGTTCCGCTCCGGCGTCCCGCGG"</span></span>
<span><span class="co">##  [2] "CCCACTTCGTGTCTATGCCGGACACGCCTCGATAGGCGCAGGCGATGGGC"</span></span>
<span><span class="co">##  [3] "ACAGGAGAGACCCTCGGTTGCCGGCGACGCCGTGTCGTTGGTAGGCCCAC"</span></span>
<span><span class="co">##  [4] "GATAGCTCCACACCCACCTCAGCGTCCCGGGCCGCCGGCGTTCCGCCTGC"</span></span>
<span><span class="co">##  [5] "GCCCAACAAGGACGGTGAACTCCCCCGGGTACGGAAGAGGGTATGGCCGC"</span></span>
<span><span class="co">##  [6] "AGGAGTCCTCCTAGAGCTCATGGGTTGAGACGTGCCTCGACGCCCGACCT"</span></span>
<span><span class="co">##  [7] "CCCATTAGACCGTCCTGGCGGACACCCGTACGGGTGAGACCCTCCGGGTC"</span></span>
<span><span class="co">##  [8] "TGCTTATCATGGCCGCCCTGATGACGTGTCAGGGGGAGGACTGAGCGGGG"</span></span>
<span><span class="co">##  [9] "ATCCCGCATTCGCCGACGTCTCCACAGGAGGATCAGCGGGTCCGGGGCGA"</span></span>
<span><span class="co">## [10] "TTTGCGCCCCCTAAGGCACAGCCGCGACCCCAGGTTGGGAACCGCCGAAC"</span></span>
<span><span class="co">## [11] "CTACGGAACGTGGCTCCGAGCATCGGCGCATCGGCATGTGTCTGCCGGCG"</span></span>
<span><span class="co">## [12] "GTCGGGCGGAGCGCCACCACCGAGGGGCGGGCCCTTCAATTCTATAAGCG"</span></span>
<span><span class="co">## [13] "GGCGGGCCCTTCAATTCTATAAGCGACGCCGCCCTTGTCTGACGCTGGGC"</span></span>
<span><span class="co">## [14] "CACCCTATGTAGCCCCCTGCCTCGCCGGCCAGCCTGGGCTGATCGGGGCC"</span></span>
<span><span class="co">## [15] "TGGCCGTCGCGCTCCGGAGCCGTCACACCGGCGTACCTGTTATAAAGTCG"</span></span>
<span><span class="co">## [16] "CACCGGCGTACCTGTTATAAAGTCGCCCGCGCTCCCCCGGGCGCACCACG"</span></span></code></pre>
<p>We can check the nucleotide distribution of those sequences</p>
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="kw">for</span> <span class="op">(</span><span class="va">i</span> <span class="kw">in</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">wrong_pred_seq</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
<span>  <span class="va">l</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu">stringr</span><span class="fu">::</span><span class="fu"><a href="https://stringr.tidyverse.org/reference/str_split.html" class="external-link">str_split</a></span><span class="op">(</span><span class="va">wrong_pred_seq</span><span class="op">[</span><span class="va">i</span><span class="op">]</span>, <span class="st">""</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/table.html" class="external-link">table</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/proportions.html" class="external-link">prop.table</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/t.html" class="external-link">t</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="../reference/pipe.html">%&gt;%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="op">}</span></span>
<span><span class="va">dist_matrix</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/do.call.html" class="external-link">do.call</a></span><span class="op">(</span><span class="va">rbind</span>, <span class="va">l</span><span class="op">)</span></span>
<span><span class="va">dist_matrix</span></span></code></pre></div>
<pre><code><span><span class="co">##          A    C    G    T</span></span>
<span><span class="co">##  [1,] 0.10 0.46 0.30 0.14</span></span>
<span><span class="co">##  [2,] 0.16 0.34 0.32 0.18</span></span>
<span><span class="co">##  [3,] 0.16 0.32 0.36 0.16</span></span>
<span><span class="co">##  [4,] 0.12 0.48 0.26 0.14</span></span>
<span><span class="co">##  [5,] 0.24 0.30 0.36 0.10</span></span>
<span><span class="co">##  [6,] 0.18 0.32 0.30 0.20</span></span>
<span><span class="co">##  [7,] 0.16 0.38 0.30 0.16</span></span>
<span><span class="co">##  [8,] 0.16 0.22 0.42 0.20</span></span>
<span><span class="co">##  [9,] 0.18 0.34 0.34 0.14</span></span>
<span><span class="co">## [10,] 0.20 0.40 0.28 0.12</span></span>
<span><span class="co">## [11,] 0.14 0.32 0.36 0.18</span></span>
<span><span class="co">## [12,] 0.18 0.32 0.36 0.14</span></span>
<span><span class="co">## [13,] 0.14 0.34 0.30 0.22</span></span>
<span><span class="co">## [14,] 0.10 0.44 0.30 0.16</span></span>
<span><span class="co">## [15,] 0.16 0.34 0.30 0.20</span></span>
<span><span class="co">## [16,] 0.16 0.44 0.26 0.14</span></span></code></pre>
<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>distribution <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/vector.html" class="external-link">as.vector</a></span><span class="op">(</span><span class="va">dist_matrix</span><span class="op">)</span>,</span>
<span>                 nt <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">factor</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="va">vocabulary</span>, each <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">dist_matrix</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>,</span>
<span>                 sample_id <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">dist_matrix</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span>
<span></span>
<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">df</span>, <span class="fu">aes</span><span class="op">(</span>fill<span class="op">=</span><span class="va">nt</span>, y<span class="op">=</span><span class="va">distribution</span>, x<span class="op">=</span><span class="va">nt</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
<span>    <span class="fu">geom_bar</span><span class="op">(</span>position<span class="op">=</span><span class="st">"dodge"</span>, stat<span class="op">=</span><span class="st">"identity"</span><span class="op">)</span>  <span class="op">+</span> <span class="fu">facet_wrap</span><span class="op">(</span><span class="op">~</span><span class="va">sample_id</span><span class="op">)</span></span></code></pre></div>
<p><img src="getting_started_files/figure-html/unnamed-chunk-10-1.png" width="700"></p>
<p>Finally, we may want to aggregate all predictions, we made for the
sequence. We can do this using the <code>summarize_states</code>
function. The function returns the mean confidence, the maximum
prediction and the vote percentages (percentage of predictions per
class).</p>
<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">label_names</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"high_gc"</span>, <span class="st">"equal_dist"</span><span class="op">)</span></span>
<span><span class="va">pred_summary</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/summarize_states.html">summarize_states</a></span><span class="op">(</span>label_names <span class="op">=</span> <span class="va">label_names</span>, df <span class="op">=</span> <span class="va">pred_df</span><span class="op">[</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span><span class="op">)</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">pred_summary</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">##    file_name mean_conf_high_gc mean_conf_equal_dist max_conf_high_gc</span></span>
<span><span class="co">##       &lt;lgcl&gt;             &lt;num&gt;                &lt;num&gt;            &lt;num&gt;</span></span>
<span><span class="co">## 1:        NA         0.9148641           0.08513589        0.9714182</span></span>
<span><span class="co">##    max_conf_equal_dist vote_perc_high_gc vote_perc_equal_dist mean_prediction</span></span>
<span><span class="co">##                  &lt;num&gt;             &lt;num&gt;                &lt;num&gt;          &lt;char&gt;</span></span>
<span><span class="co">## 1:           0.9218199         0.9598997           0.04010025         high_gc</span></span>
<span><span class="co">##    max_prediction vote_prediction num_prediction</span></span>
<span><span class="co">##            &lt;char&gt;          &lt;char&gt;          &lt;int&gt;</span></span>
<span><span class="co">## 1:        high_gc         high_gc            399</span></span></code></pre>
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