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Add small plotGeneLoadings example to liger-vignette
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@vkozareva
vkozareva committed Jun 4, 2019
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7 changes: 5 additions & 2 deletions vignettes/liger-vignette.Rmd
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Expand Up @@ -64,8 +64,7 @@ ligerex = quantileAlignSNF(ligerex) #SNF clustering and quantile alignment
## Visualizing the results
We can visualize the results by using dimensionality reduction techniques like t-SNE or UMAP (recommended
for larger datasets). Visualizations can be colored by dataset of origin or cluster assignment.
`plotWordClouds` is a useful way to visualize the most highly loading genes (both shared and dataset
specific) for each factor, in conjunction with the factor loadings across cells.
`plotWordClouds` and `plotGeneLoadings` are useful ways to visualize the most highly loading genes (both shared and dataset specific) for each factor, in conjunction with the factor loadings across cells.
```r
ligerex = runTSNE(ligerex)
# for larger datasets, may want to use UMAP instead
Expand All @@ -76,6 +75,10 @@ pdf("word_clouds.pdf")
plotWordClouds(ligerex)
dev.off()

pdf("gene_loadings.pdf")
plotGeneLoadings(ligerex)
dev.off()

```

## Exploring factors and clusters
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39 changes: 34 additions & 5 deletions vignettes/liger-vignette.html
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Expand Up @@ -12,14 +12,14 @@

<meta name="author" content="Joshua D. Welch and Velina Kozareva" />

<meta name="date" content="2019-03-12" />
<meta name="date" content="2019-06-04" />

<title>Comparing and contrasting heterogeneous single cell profiles using liger</title>



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<h1 class="title toc-ignore">Comparing and contrasting heterogeneous single cell profiles using liger</h1>
<h4 class="author"><em>Joshua D. Welch and Velina Kozareva</em></h4>
<h4 class="date"><em>2019-03-12</em></h4>
<h4 class="author">Joshua D. Welch and Velina Kozareva</h4>
<h4 class="date">2019-06-04</h4>



Expand Down Expand Up @@ -109,14 +134,18 @@ <h2>Performing the factorization</h2>
</div>
<div id="visualizing-the-results" class="section level2">
<h2>Visualizing the results</h2>
<p>We can visualize the results by using dimensionality reduction techniques like t-SNE or UMAP (recommended for larger datasets). Visualizations can be colored by dataset of origin or cluster assignment. <code>plotWordClouds</code> is a useful way to visualize the most highly loading genes (both shared and dataset specific) for each factor, in conjunction with the factor loadings across cells.</p>
<p>We can visualize the results by using dimensionality reduction techniques like t-SNE or UMAP (recommended for larger datasets). Visualizations can be colored by dataset of origin or cluster assignment. <code>plotWordClouds</code> and <code>plotGeneLoadings</code> are useful ways to visualize the most highly loading genes (both shared and dataset specific) for each factor, in conjunction with the factor loadings across cells.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ligerex =<span class="st"> </span><span class="kw">runTSNE</span>(ligerex)
<span class="co"># for larger datasets, may want to use UMAP instead</span>
ligerex =<span class="st"> </span><span class="kw">runUMAP</span>(ligerex)
<span class="kw">plotByDatasetAndCluster</span>(ligerex) <span class="co">#Can also pass in different set of cluster labels to plot</span>

<span class="kw">pdf</span>(<span class="st">&quot;word_clouds.pdf&quot;</span>)
<span class="kw">plotWordClouds</span>(ligerex)
<span class="kw">dev.off</span>()

<span class="kw">pdf</span>(<span class="st">&quot;gene_loadings.pdf&quot;</span>)
<span class="kw">plotGeneLoadings</span>(ligerex)
<span class="kw">dev.off</span>()</code></pre></div>
</div>
<div id="exploring-factors-and-clusters" class="section level2">
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