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<h1 class="title toc-ignore">DevKidCC</h1>
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<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">library</span>(DevKidCC)</a>
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<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">library</span>(tidyverse)</a></code></pre></div>
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<div id="introduction" class="section level1">
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<h1>Introduction</h1>
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<p>In this vignette I’ll run through how to use DevKidCC to classify kidney cells in scRNA-Seq data. I’ll then highlight the various visualisation tools that are included in the package.<br> <br></p>
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<div id="devkidcc" class="section level2">
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<h2>DevKidCC</h2>
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<p>First we need to load the data and generate a Seurat object. To generate a Seurat object from raw files, follow the tutorials at the <a href="https://satijalab.org/seurat/articles/pbmc3k_tutorial.html">Satija Lab website</a><br> <br> Here we will load the organoid dataset included with the package, from <a href>Howden et al. 2019</a></p>
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<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw">load</span>(<span class="st">&quot;/group/kidn1/Group-Little_MCRI/People/Sean/PhD/R-projects/DevKidCC/data/organoid.rda&quot;</span>)</a>
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<a class="sourceLine" id="cb2-2" data-line-number="2">organoid</a>
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<a class="sourceLine" id="cb2-3" data-line-number="3"><span class="co">#&gt; An object of class Seurat </span></a>
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<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="co">#&gt; 2000 features across 5365 samples within 1 assay </span></a>
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<a class="sourceLine" id="cb2-5" data-line-number="5"><span class="co">#&gt; Active assay: RNA (2000 features, 2000 variable features)</span></a>
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<a class="sourceLine" id="cb2-6" data-line-number="6"><span class="co">#&gt; 5 dimensional reductions calculated: cca, cca.aligned, tsne, pca, umap</span></a></code></pre></div>
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