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<meta name="description" content="The software formalises a framework for classification in R.
There are four stages; Data transformation, feature selection, classifier training,
and prediction. The requirements of variable types and names are
fixed, but specialised variables for functions can also be provided.
The classification framework is wrapped in a driver loop, that
reproducibly carries out a number of cross-validation schemes.
Functions for differential expression, differential variability,
and differential distribution are included. Additional functions
may be developed by the user, by creating an interface to the framework.">
<title>A framework for cross-validated classification problems, with
applications to differential variability and differential
distribution testing • ClassifyR</title>
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There are four stages; Data transformation, feature selection, classifier training,
and prediction. The requirements of variable types and names are
fixed, but specialised variables for functions can also be provided.
The classification framework is wrapped in a driver loop, that
reproducibly carries out a number of cross-validation schemes.
Functions for differential expression, differential variability,
and differential distribution are included. Additional functions
may be developed by the user, by creating an interface to the framework.">
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<a class="navbar-brand me-2" href="index.html">ClassifyR</a>
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<div class="page-header"><h1 id="classifyr-performance-evaluation-for-multi-view-data-sets-and-seamless-integration-with-multiassayexperiment-and-bioconductor">ClassifyR: Performance evaluation for multi-view data sets and seamless integration with MultiAssayExperiment and Bioconductor<a class="anchor" aria-label="anchor" href="#classifyr-performance-evaluation-for-multi-view-data-sets-and-seamless-integration-with-multiassayexperiment-and-bioconductor"></a>
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<p><img src="reference/figures/ClassifyRsticker.png" align="right" width="250" style="margin-left: 10px;"></p>
<p>ClassifyR’s performance evaluation focuses on model stability and interpretability. Based on repeated cross-validation, it is possible to evaluate feature selection stability and also per-sample prediction accuracy. Also, multiple omics data assays on the same samples are becoming more popular and ClassifyR supports a range of multi-view methods to evaluate which data view is the most predictive and combine data views to evaluate if multiple views provide superior predictive performance to a single data view.</p>
<div class="section level2">
<h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
</h2>
<p>The recommended method of installing ClassifyR is by using Bioconductor’s BiocManager installer:</p>
<pre><code><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">BiocManager</span><span class="op">)</span></span>
<span><span class="fu"><a href="https://rdrr.io/pkg/BiocManager/man/install.html" class="external-link">install</a></span><span class="op">(</span><span class="st">"ClassifyR"</span>, dependencies <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre>
<p>The above code will install all packages that provide feature selection or model-building functionality. If only one or two methods are desired then the dependencies option could be omitted and those packages providing functionality installed manually.</p>
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<h2 id="website">Website<a class="anchor" aria-label="anchor" href="#website"></a>
</h2>
<p>Please visit <a href="https://sydneybiox.github.io/ClassifyR/">the ClassifyR website</a> to view the main vignette as well as articles that provide more in-depth explanations for various aspects of the package. Details of performance evaluation, multi-view methods and contributing a wrapper for a new algorithm to the package are provided.</p>
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<h2 id="reference">Reference<a class="anchor" aria-label="anchor" href="#reference"></a>
</h2>
<p>Strbenac D., Mann, G.J., Ormerod, J.T., and Yang, J. Y. H. (2015) ClassifyR: An R package for performance assessment of classification with applications to transcriptomics, <em>Bioinformatics</em>.</p>
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<h2 data-toc-skip>Links</h2>
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<li><a href="https://www.bioconductor.org/packages/ClassifyR" class="external-link">View on Bioconductor</a></li>
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<h2 data-toc-skip>License</h2>
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<li><a href="https://www.r-project.org/Licenses/GPL-3" class="external-link">GPL-3</a></li>
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<h2 data-toc-skip>Citation</h2>
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<li><a href="authors.html#citation">Citing ClassifyR</a></li>
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<h2 data-toc-skip>Developers</h2>
<ul class="list-unstyled">
<li>Dario Strbenac <br><small class="roles"> Author, maintainer </small> </li>
<li>Ellis Patrick <br><small class="roles"> Author </small> </li>
<li>Sourish Iyengar <br><small class="roles"> Author </small> </li>
<li>Harry Robertson <br><small class="roles"> Author </small> </li>
<li>Andy Tran <br><small class="roles"> Author </small> </li>
<li>John Ormerod <br><small class="roles"> Author </small> </li>
<li>Graham Mann <br><small class="roles"> Author </small> </li>
<li>Jean Yang <br><small class="roles"> Author </small> </li>
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<p>Developed by Dario Strbenac, Ellis Patrick, Sourish Iyengar, Harry Robertson, Andy Tran, John Ormerod, Graham Mann, Jean Yang.</p>
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