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<title>Omics Data Integration Using Kernel Methods • mixKernel</title>
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<meta property="og:description" content="Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view <doi:10.1093/bioinformatics/btx682>. A method to select (as well as funtions to display) important variables is also provided <doi:10.1093/nargab/lqac014>.">
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">0.9-2</span>
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<div class="page-header"><h1 id="mixkernel">mixKernel<a class="anchor" aria-label="anchor" href="#mixkernel"></a>
</h1></div>
<p>Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view. Functions to select and display important variables are also provided in the package in an unsupervised and kernel association frameworks.</p>
<p>Installation instructions are provided below.</p>
<div class="section level2">
<h2 id="installation-of-python-dependencies">Installation of python dependencies<a class="anchor" aria-label="anchor" href="#installation-of-python-dependencies"></a>
</h2>
<p>The following python modules are required for the functions performing feature selection in <code>mixKernel</code>: autograd, scipy, sklearn, numpy</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a>pip3 install autograd</span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>pip3 install scipy</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>pip3 install sklearn</span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>pip3 install numpy</span></code></pre></div>
</div>
<div class="section level2">
<h2 id="installation-of-bioconductor-dependencies">Installation of Bioconductor dependencies<a class="anchor" aria-label="anchor" href="#installation-of-bioconductor-dependencies"></a>
</h2>
<p>Two Bioconductor packages are required for <code>mixKernel</code> installation: <code>mixOmics</code> and <code>phyloseq</code>:</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/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"BiocManager"</span><span class="op">)</span></span>
<span><span class="fu">BiocManager</span><span class="fu">::</span><span class="fu"><a href="https://bioconductor.github.io/BiocManager/reference/install.html" class="external-link">install</a></span><span class="op">(</span><span class="st">"mixOmics"</span><span class="op">)</span></span>
<span><span class="fu">BiocManager</span><span class="fu">::</span><span class="fu"><a href="https://bioconductor.github.io/BiocManager/reference/install.html" class="external-link">install</a></span><span class="op">(</span><span class="st">"phyloseq"</span><span class="op">)</span></span></code></pre></div>
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<div class="section level2">
<h2 id="mixkernel-installation">mixKernel installation<a class="anchor" aria-label="anchor" href="#mixkernel-installation"></a>
</h2>
<p>Finally the installation is completed with:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"mixKernel"</span><span class="op">)</span></span></code></pre></div>
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<div class="section level2">
<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
</h2>
<p>Mariette, J. and Villa-Vialaneix, N. (2018). Unsupervised multiple kernel learning for heterogeneous data integration. <em>Bioinformatics</em>, <strong>34</strong>(6), 1009-1015.</p>
<p>Brouard, C., Mariette, J., Flamary, R., & Vialaneix, N. (2022). Feature selection for kernel methods in systems biology. <em>NAR Genomics and Bioinformatics</em>, <strong>4</strong>(1), lqac014.</p>
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<li><a href="https://cloud.r-project.org/package=mixKernel" class="external-link">View on CRAN</a></li>
<li><a href="https://forgemia.inra.fr/genotoul-bioinfo/mixKernel/-/issues" class="external-link">Report a bug</a></li>
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<li>GPL (>= 2)</li>
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<li><a href="authors.html#citation">Citing mixKernel</a></li>
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<h2 data-toc-skip>Developers</h2>
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<li>Nathalie Vialaneix <br><small class="roles"> Author, maintainer </small> </li>
<li>Celine Brouard <br><small class="roles"> Author </small> </li>
<li>Remi Flamary <br><small class="roles"> Author </small> </li>
<li>Julien Henry <br><small class="roles"> Author </small> </li>
<li>Jerome Mariette <br><small class="roles"> Author </small> </li>
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<p>Developed by Nathalie Vialaneix, Celine Brouard, Remi Flamary, Julien Henry, Jerome Mariette.</p>
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