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<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongRepositoryversionstrongahrefhttpsgithubcomstephenslabsinglecelljamboreetree6cf9aa720cc16dc543e93b36d57bc1a85555860dtargetblank6cf9aa7a">
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<span class="glyphicon glyphicon-ok text-success"
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aria-hidden="true"></span> <strong>Repository version:</strong>
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<a href="https://github.com/stephenslab/single-cell-jamboree/tree/9440e05bc58ce8e42a1d61a79ffdfd18896e60cb" target="_blank">9440e05</a>
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<a href="https://github.com/stephenslab/single-cell-jamboree/tree/6cf9aa720cc16dc543e93b36d57bc1a85555860d" target="_blank">6cf9aa7</a>
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id="strongRepositoryversionstrongahrefhttpsgithubcomstephenslabsinglecelljamboreetree9440e05bc58ce8e42a1d61a79ffdfd18896e60cbtargetblank9440e05a"
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id="strongRepositoryversionstrongahrefhttpsgithubcomstephenslabsinglecelljamboreetree6cf9aa720cc16dc543e93b36d57bc1a85555860dtargetblank6cf9aa7a"
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<p>
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The results in this page were generated with repository version
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<a href="https://github.com/stephenslab/single-cell-jamboree/tree/9440e05bc58ce8e42a1d61a79ffdfd18896e60cb" target="_blank">9440e05</a>.
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<a href="https://github.com/stephenslab/single-cell-jamboree/tree/6cf9aa720cc16dc543e93b36d57bc1a85555860d" target="_blank">6cf9aa7</a>.
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See the <em>Past versions</em> tab to see a history of the changes made
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to the R Markdown and HTML files.
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</p>
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were generated:
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<pre><code>
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Rmd
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<a href="https://github.com/stephenslab/single-cell-jamboree/blob/6cf9aa720cc16dc543e93b36d57bc1a85555860d/analysis/pancreas_annotate.Rmd" target="_blank">6cf9aa7</a>
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Peter Carbonetto
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2025-02-20
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wflow_publish("pancreas_annotate.Rmd", verbose = TRUE, view = FALSE)
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<a href="https://github.com/stephenslab/single-cell-jamboree/blob/105293f8f21a4fa50160bc5f09a98a67b30c9dd7/analysis/pancreas_annotate.Rmd" target="_blank">105293f</a>
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Peter Carbonetto
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html
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<a href="https://rawcdn.githack.com/stephenslab/single-cell-jamboree/66b44ce7013ab2f76c1f3627d66e58b09b53d603/docs/pancreas_annotate.html" target="_blank">66b44ce</a>
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Peter Carbonetto
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2025-02-20
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Added plots for semi-NMF to pancreas_annotate analysis.
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<a href="https://github.com/stephenslab/single-cell-jamboree/blob/9440e05bc58ce8e42a1d61a79ffdfd18896e60cb/analysis/pancreas_annotate.Rmd" target="_blank">9440e05</a>
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single “one-size-fits-all” strategy that works best, and so we recommend
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exploring different annotation strategies. Also, careful interpretation
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of the matrix factorization is discussed.</p>
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<p>The plotting functions used in this analysis are from <a
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href="https://github.com/stephenslab/fastTopics/">fastTopics</a>.</p>
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<p>A side benefit of this investigation is to illustate some useful
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plotting strategies, including the <code>annotation_heatmap()</code>
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function from the [fastTopics][fastTopics package].</p>
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<p>First, load the packages needed for this analysis.</p>
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<pre class="r"><code>library(Matrix)
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library(flashier)
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library(fastTopics)
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library(ggplot2)
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library(ggrepel)
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library(cowplot)</code></pre>
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<p>Set the seed for reproducibility.</p>
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<pre class="r"><code>set.seed(1)</code></pre>
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<p>Strategy (i) picks out some canonical marker genes for islet cells
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such as <em>INS</em> for beta cells and <em>GCG</em> for alpha cells.
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But it also picks out other genes that are highly expressed in multiple
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islet cell types, such as <em>TTR</em> and <em>CHGB</em>. Strategy (ii)
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islet cell types, such as <em>SCGN</em> and <em>TTR</em>. Strategy (ii)
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focusses more strongly on genes that distinguish one cell type from
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another, and as a result marker genes such as <em>MAFA</em> (beta cells)
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and <em>GC</em> (alpha cells) are ranked more highly with this
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strategy.</p>
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<p>Below, we take a closer look at the ranking of the genes based on
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these two strategies, and suggest another simple visualization which
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could be useful. (See: “A closer look at ranking genes by largest versus
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distinctive”.)</p>
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<p>The better strategy will depend on the setting and on the goals of
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the analysis, which is why the <code>annotation_heatmap</code> function
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provides both options. These selection strategies can also reveal
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perplexity = 70,n = Inf) +
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labs(y = &quot;membership&quot;,fill = &quot;factor&quot;,color = &quot;factor&quot;)</code></pre>
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<p><img src="figure/pancreas_annotate.Rmd/structure-plot-flashier-snmf-1.png" width="960" style="display: block; margin: auto;" /></p>
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<button type="button" class="btn btn-default btn-xs btn-workflowr btn-workflowr-fig" data-toggle="collapse" data-target="#fig-structure-plot-flashier-snmf-1">
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<a href="https://github.com/stephenslab/single-cell-jamboree/blob/66b44ce7013ab2f76c1f3627d66e58b09b53d603/docs/figure/pancreas_annotate.Rmd/structure-plot-flashier-snmf-1.png" target="_blank">66b44ce</a>
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Peter Carbonetto
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2025-02-20
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<p>As a result, we would expect that the factors themselves would tend
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to pick more “distinctive” features; for example, factor 8 capturing
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expression specific to dselta, gamma and epsilon cells doesn’t need to
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theme(plot.title = element_text(face = &quot;plain&quot;,size = 9))
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plot_grid(p1,p2,nrow = 1,ncol = 2)</code></pre>
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<p><img src="figure/pancreas_annotate.Rmd/annotation-plot-flashier-snmf-1.png" width="720" style="display: block; margin: auto;" /></p>
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<button type="button" class="btn btn-default btn-xs btn-workflowr btn-workflowr-fig" data-toggle="collapse" data-target="#fig-annotation-plot-flashier-snmf-1">
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<a href="https://github.com/stephenslab/single-cell-jamboree/blob/66b44ce7013ab2f76c1f3627d66e58b09b53d603/docs/figure/pancreas_annotate.Rmd/annotation-plot-flashier-snmf-1.png" target="_blank">66b44ce</a>
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Peter Carbonetto
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<pre><code># Features selected for plot: GCG TTR TM4SF4 GC CHGB PCSK2 MALAT1 IGFBP7 INS IAPP HADH NPTX2 MAFA RBP4 PCSK1 SCD5 SST AQP3 PPY LEPR DPYSL3 AKAP12
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c(&quot;GCG&quot;, &quot;TTR&quot;, &quot;TM4SF4&quot;, &quot;GC&quot;, &quot;CHGB&quot;, &quot;PCSK2&quot;, &quot;MALAT1&quot;, &quot;IGFBP7&quot;,
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&quot;INS&quot;, &quot;IAPP&quot;, &quot;HADH&quot;, &quot;NPTX2&quot;, &quot;MAFA&quot;, &quot;RBP4&quot;, &quot;PCSK1&quot;, &quot;SCD5&quot;,
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&quot;INS&quot;, &quot;IAPP&quot;, &quot;MAFA&quot;, &quot;NPTX2&quot;, &quot;ADCYAP1&quot;, &quot;PFKFB2&quot;, &quot;MEG3&quot;,
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&quot;DLK1&quot;, &quot;SST&quot;, &quot;AQP3&quot;, &quot;LEPR&quot;, &quot;AKAP12&quot;, &quot;MTUS1&quot;, &quot;EGR1&quot;, &quot;PPY&quot;,
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&quot;S100A6&quot;)</code></pre>
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<p>Note that the F matrix in the semi-NMF allows for both positive and
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negative log-fold changes.</p>
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</div>
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<div id="a-closer-look-at-ranking-genes-by-largest-vs.-distinctive"
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class="section level2">
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<h2>A closer look at ranking genes by largest vs. distinctive</h2>
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<p>Above, we compared gene selection strategies for some annotation
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heatmaps of NMF results. Here we visualize how these two different
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strategies result in two different gene rankings. And this visualization
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may be useful on its own to annotate the factors.</p>
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<p>First we define a couple of functions used to create some plots.</p>
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<p>This function computes the “least extreme” (l.e.) effect differences
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for a non-negative effects matrix:</p>
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<pre class="r"><code>compute_le_diff &lt;- function (effects_matrix,
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compare_dims = seq(1,ncol(effects_matrix))) {
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m &lt;- ncol(effects_matrix)
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out &lt;- effects_matrix
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for (i in 1:m) {
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dims &lt;- setdiff(compare_dims,i)
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out[,i] &lt;- effects_matrix[,i] - apply(effects_matrix[,dims],1,max)
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}
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return(out)
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}</code></pre>
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<p>This function will be used to create the scatterplots:</p>
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<pre class="r"><code>distinctive_genes_scatterplot &lt;- function (effects_matrix, k,
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effect_quantile_prob = 0.999,
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lediff_quantile_prob = 0.999) {
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lediff &lt;- compute_le_diff(effects_matrix)
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genes &lt;- rownames(effects_matrix)
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pdat &lt;- data.frame(gene = genes,
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effect = effects_matrix[,k],
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lediff = lediff[,k])
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effect_quantile &lt;- quantile(pdat$effect,effect_quantile_prob)
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lediff_quantile &lt;- quantile(pdat$lediff,lediff_quantile_prob)
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i &lt;- which(pdat$effect &lt; effect_quantile &amp; pdat$lediff &lt; lediff_quantile)
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pdat[i,&quot;gene&quot;] &lt;- NA
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return(ggplot(pdat,aes(x = effect,y = lediff,label = gene)) +
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geom_point(color = &quot;dodgerblue&quot;) +
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geom_hline(yintercept = 0,color = &quot;magenta&quot;,linetype = &quot;dotted&quot;,
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linewidth = 0.5) +
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geom_text_repel(color = &quot;black&quot;,size = 2,
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fontface = &quot;italic&quot;,segment.color = &quot;black&quot;,
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segment.size = 0.25,min.segment.length = 0,
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max.overlaps = Inf,na.rm = TRUE) +
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labs(x = &quot;log-fold change&quot;,y = &quot;l.e. difference&quot;) +
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theme_cowplot(font_size = 9))
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}</code></pre>
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<p>Now we compare the two different gene rankings in the scatterplots
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for factors 4, 5 and 6 of the flashier NMF result:</p>
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<pre class="r"><code>F &lt;- fl_nmf_ldf$F
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colnames(F) &lt;- paste0(&quot;k&quot;,1:9)
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kset &lt;- paste0(&quot;k&quot;,4:6)
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p1 &lt;- distinctive_genes_scatterplot(F[,kset],&quot;k4&quot;) + ggtitle(&quot;factor k4&quot;)
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p2 &lt;- distinctive_genes_scatterplot(F[,kset],&quot;k5&quot;) + ggtitle(&quot;factor k5&quot;)
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p3 &lt;- distinctive_genes_scatterplot(F[,kset],&quot;k6&quot;) + ggtitle(&quot;factor k6&quot;)
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print(plot_grid(p1,p2,p3,nrow = 1,ncol = 3))</code></pre>
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<p><img src="figure/pancreas_annotate.Rmd/distinctive-gene-scatterplots-flashier-nmf-1.png" width="900" style="display: block; margin: auto;" /></p>
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<p>It is clear from these scatterplots that the rankings are very
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different, and strikingly so for factor 5 representing alpha cells. This
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means that many of the top-ranked genes for factor 5 (largest increases
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in expression) also show very large increases in other islet cells,
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e.g., <em>SCG5</em>.</p>
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# [5] ebnm_1.1-34 Matrix_1.6-5
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