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validation_IC50s.html
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<!DOCTYPE html>
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<title>Validation neutralization assays versus polyclonal fits — Deep mutational scanning of SARS-CoV-2 Delta spike using a barcoded lentiviral platform against REGN10933 and Delta breakthrough sera documentation</title>
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<section id="Validation-neutralization-assays-versus-polyclonal-fits">
<h1>Validation neutralization assays versus <code class="docutils literal notranslate"><span class="pre">polyclonal</span></code> fits<a class="headerlink" href="#Validation-neutralization-assays-versus-polyclonal-fits" title="Permalink to this headline">¶</a></h1>
<p>Compare actual measured neutralization values for specific mutants to the <code class="docutils literal notranslate"><span class="pre">polyclonal</span></code> fits.</p>
<p>Import Python modules:</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">altair</span> <span class="k">as</span> <span class="nn">alt</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">yaml</span>
</pre></div>
</div>
</div>
<p>Read configuration and validation assay measurements:</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"config.yaml"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">yaml</span><span class="o">.</span><span class="n">safe_load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="n">validation_ic50s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">"validation_ic50s"</span><span class="p">],</span> <span class="n">na_filter</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">validation_ic50s</span>
</pre></div>
</div>
</div>
<div class="nboutput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
</pre></div>
</div>
<div class="output_area rendered_html docutils container">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>antibody</th>
<th>aa_substitutions</th>
<th>measured IC50</th>
<th>lower_bound</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>267C</td>
<td></td>
<td>0.000877</td>
<td>False</td>
</tr>
<tr>
<th>1</th>
<td>267C</td>
<td>S371Y</td>
<td>0.001423</td>
<td>False</td>
</tr>
<tr>
<th>2</th>
<td>267C</td>
<td>R408S</td>
<td>0.000574</td>
<td>False</td>
</tr>
<tr>
<th>3</th>
<td>267C</td>
<td>R452I</td>
<td>0.000678</td>
<td>False</td>
</tr>
<tr>
<th>4</th>
<td>267C</td>
<td>F456L</td>
<td>0.001209</td>
<td>False</td>
</tr>
<tr>
<th>5</th>
<td>267C</td>
<td>E484A</td>
<td>0.003011</td>
<td>False</td>
</tr>
<tr>
<th>6</th>
<td>267C</td>
<td>F486I</td>
<td>0.002161</td>
<td>False</td>
</tr>
<tr>
<th>7</th>
<td>279C</td>
<td></td>
<td>0.000057</td>
<td>False</td>
</tr>
<tr>
<th>8</th>
<td>279C</td>
<td>S371Y</td>
<td>0.000181</td>
<td>False</td>
</tr>
<tr>
<th>9</th>
<td>279C</td>
<td>R408S</td>
<td>0.000045</td>
<td>False</td>
</tr>
<tr>
<th>10</th>
<td>279C</td>
<td>R452I</td>
<td>0.000038</td>
<td>False</td>
</tr>
<tr>
<th>11</th>
<td>279C</td>
<td>F456L</td>
<td>0.000074</td>
<td>False</td>
</tr>
<tr>
<th>12</th>
<td>279C</td>
<td>E484A</td>
<td>0.000112</td>
<td>False</td>
</tr>
<tr>
<th>13</th>
<td>279C</td>
<td>F486I</td>
<td>0.000125</td>
<td>False</td>
</tr>
</tbody>
</table>
</div></div>
</div>
<p>Now get the predictions by the averaged <code class="docutils literal notranslate"><span class="pre">polyclonal</span></code> model fits:</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">validation_vs_prediction</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">antibody</span><span class="p">,</span> <span class="n">antibody_df</span> <span class="ow">in</span> <span class="n">validation_ic50s</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"antibody"</span><span class="p">):</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s2">"escape_dir"</span><span class="p">],</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">antibody</span><span class="si">}</span><span class="s2">.pickle"</span><span class="p">),</span> <span class="s2">"rb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="n">validation_vs_prediction</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">icXX</span><span class="p">(</span><span class="n">antibody_df</span><span class="p">))</span>
<span class="n">validation_vs_prediction</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">validation_vs_prediction</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">validation_vs_prediction</span>
</pre></div>
</div>
</div>
<div class="nboutput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
</pre></div>
</div>
<div class="output_area rendered_html docutils container">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>antibody</th>
<th>aa_substitutions</th>
<th>measured IC50</th>
<th>lower_bound</th>
<th>mean_IC50</th>
<th>median_IC50</th>
<th>std_IC50</th>
<th>n_models</th>
<th>frac_models</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>267C</td>
<td></td>
<td>0.000877</td>
<td>False</td>
<td>0.034641</td>
<td>0.034711</td>
<td>0.034816</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>1</th>
<td>267C</td>
<td>E484A</td>
<td>0.003011</td>
<td>False</td>
<td>0.459317</td>
<td>0.387717</td>
<td>0.473481</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>2</th>
<td>267C</td>
<td>F456L</td>
<td>0.001209</td>
<td>False</td>
<td>1.323086</td>
<td>0.695120</td>
<td>1.754687</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>3</th>
<td>267C</td>
<td>F486I</td>
<td>0.002161</td>
<td>False</td>
<td>0.548088</td>
<td>0.264798</td>
<td>0.718249</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>4</th>
<td>267C</td>
<td>R408S</td>
<td>0.000574</td>
<td>False</td>
<td>0.011290</td>
<td>0.011112</td>
<td>0.008520</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>5</th>
<td>267C</td>
<td>R452I</td>
<td>0.000678</td>
<td>False</td>
<td>0.006932</td>
<td>0.006755</td>
<td>0.004558</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>6</th>
<td>267C</td>
<td>S371Y</td>
<td>0.001423</td>
<td>False</td>
<td>0.934559</td>
<td>0.418290</td>
<td>1.359847</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>7</th>
<td>279C</td>
<td></td>
<td>0.000057</td>
<td>False</td>
<td>0.006658</td>
<td>0.006468</td>
<td>0.004207</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>8</th>
<td>279C</td>
<td>E484A</td>
<td>0.000112</td>
<td>False</td>
<td>0.019545</td>
<td>0.013754</td>
<td>0.019474</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>9</th>
<td>279C</td>
<td>F456L</td>
<td>0.000074</td>
<td>False</td>
<td>0.007652</td>
<td>0.007338</td>
<td>0.005839</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>10</th>
<td>279C</td>
<td>F486I</td>
<td>0.000125</td>
<td>False</td>
<td>0.007503</td>
<td>0.007076</td>
<td>0.004314</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>11</th>
<td>279C</td>
<td>R408S</td>
<td>0.000045</td>
<td>False</td>
<td>0.006452</td>
<td>0.005207</td>
<td>0.004716</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>12</th>
<td>279C</td>
<td>R452I</td>
<td>0.000038</td>
<td>False</td>
<td>0.000839</td>
<td>0.000766</td>
<td>0.000438</td>
<td>4</td>
<td>1.0</td>
</tr>
<tr>
<th>13</th>
<td>279C</td>
<td>S371Y</td>
<td>0.000181</td>
<td>False</td>
<td>0.019463</td>
<td>0.019499</td>
<td>0.012934</td>
<td>4</td>
<td>1.0</td>
</tr>
</tbody>
</table>
</div></div>
</div>
<p>Now plot the results. We will plot the <strong>median</strong> across the replicate <code class="docutils literal notranslate"><span class="pre">polyclonal</span></code> fits to different deep mutational scanning replicates. This is an interactive plot that you can mouse over for details:</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">corr_chart</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Chart</span><span class="p">(</span><span class="n">validation_vs_prediction</span><span class="p">)</span>
<span class="o">.</span><span class="n">encode</span><span class="p">(</span>
<span class="n">x</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">X</span><span class="p">(</span><span class="s2">"measured IC50"</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Scale</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">"log"</span><span class="p">)),</span>
<span class="n">y</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Y</span><span class="p">(</span>
<span class="s2">"median_IC50"</span><span class="p">,</span>
<span class="n">title</span><span class="o">=</span><span class="s2">"predicted IC50 from DMS"</span><span class="p">,</span>
<span class="n">scale</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Scale</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">"log"</span><span class="p">),</span>
<span class="p">),</span>
<span class="n">facet</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Facet</span><span class="p">(</span><span class="s2">"antibody"</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">),</span>
<span class="n">color</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Color</span><span class="p">(</span><span class="s2">"lower_bound"</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">"lower_bound"</span><span class="p">),</span>
<span class="n">tooltip</span><span class="o">=</span><span class="p">[</span>
<span class="n">alt</span><span class="o">.</span><span class="n">Tooltip</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">".3g"</span><span class="p">)</span> <span class="k">if</span> <span class="n">validation_vs_prediction</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="nb">float</span>
<span class="k">else</span> <span class="n">c</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">validation_vs_prediction</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="p">],</span>
<span class="p">)</span>
<span class="o">.</span><span class="n">mark_circle</span><span class="p">(</span><span class="n">filled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span> <span class="n">opacity</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="o">.</span><span class="n">configure_axis</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="o">.</span><span class="n">resolve_scale</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">"independent"</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s2">"independent"</span><span class="p">)</span>
<span class="o">.</span><span class="n">properties</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">150</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">corr_chart</span>
</pre></div>
</div>
</div>
<div class="nboutput docutils container">
<div class="prompt empty docutils container">
</div>
<div class="output_area stderr docutils container">
<div class="highlight"><pre>
/fh/fast/bloom_j/computational_notebooks/bdadonai/2022/vep_dms/SARS-CoV-2_Delta_spike_DMS_REGN10933/.snakemake/conda/7c022d2d81458b7fb39e0b59857b3086_/lib/python3.9/site-packages/altair/utils/core.py:317: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
for col_name, dtype in df.dtypes.iteritems():
</pre></div></div>
</div>
<div class="nboutput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
</pre></div>
</div>
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©Bernadeta Dadonaite, Jesse Bloom, et al (https://www.biorxiv.org/content/10.1101/2022.10.13.512056v1). (2022).
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