diff --git a/404.html b/404.html
index 962061c..b286159 100644
--- a/404.html
+++ b/404.html
@@ -376,8 +376,8 @@ <h1>Page not found</h1>
   "url": "https://ccb-hms.github.io/osca-carpentries/404.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/404.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index 5edbc4f..6f26d47 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -399,7 +399,7 @@ <h1>Contributor Code of Conduct</h1>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/CODE_OF_CONDUCT.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-09-24",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/LICENSE.html b/LICENSE.html
index 950a333..3dbc218 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -447,7 +447,7 @@ <h1>Licenses</h1>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/LICENSE.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-09-24",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/aio.html b/aio.html
index ea5e297..e88756f 100644
--- a/aio.html
+++ b/aio.html
@@ -1003,7 +1003,7 @@ <h3 class="callout-title">Key Points</h3>
 any links that you are not going to use.
  --></section></section><section id="aio-eda_qc"><p>Content from <a href="eda_qc.html">Exploratory data analysis and quality control</a></p>
 <hr>
-<p>Last updated on 2024-10-03 |
+<p>Last updated on 2024-10-10 |
 
         <a href="https://github.com/ccb-hms/osca-carpentries/edit/main/episodes/eda_qc.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
 <div class="text-end">
@@ -1073,28 +1073,37 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span><span class="kw">library</span><span class="op">(</span><span class="va">scran</span><span class="op">)</span></span>
 <span><span class="kw">library</span><span class="op">(</span><span class="va">scDblFinder</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">WTChimeraData</span><span class="op">(</span>samples <span class="op">=</span> <span class="fl">5</span>, type <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">WTChimeraData</span><span class="op">(</span>samples <span class="op">=</span> <span class="fl">5</span>, type <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 522554
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(522554): AAACCTGAGAAACCAT AAACCTGAGAAACCGC ...
-  TTTGTCATCTTTACGT TTTGTCATCTTTCCTC
-colData names(0):
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error: failed to load resource
+  name: EH3019
+  title: WT chimera raw counts (sample 5)
+  reason: 1 resources failed to download</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb3">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb5">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>This is the same data we examined in the previous lesson.</p>
 </section><section><h2 class="section-heading" id="droplet-processing">Droplet processing<a class="anchor" aria-label="anchor" href="#droplet-processing"></a>
@@ -1104,36 +1113,6 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 while we can see that we have data for more than 500,000 droplets. It is
 likely that most of these droplets are empty and are capturing only
 ambient or background RNA.</p>
-<div class="codewrapper sourceCode" id="cb3">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">bcrank</span> <span class="op">&lt;-</span> <span class="fu">barcodeRanks</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Only showing unique points for plotting speed.</span></span>
-<span><span class="va">uniq</span> <span class="op">&lt;-</span> <span class="op">!</span><span class="fu">duplicated</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">rank</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">line_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>cutoff <span class="op">=</span> <span class="fu">names</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>                      value  <span class="op">=</span> <span class="fu">unlist</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">bcrank</span><span class="op">[</span><span class="va">uniq</span>,<span class="op">]</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">rank</span>, <span class="va">total</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">geom_hline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">line_df</span>,</span>
-<span>               <span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">cutoff</span>,</span>
-<span>                   yintercept <span class="op">=</span> <span class="va">value</span><span class="op">)</span>,</span>
-<span>               lty <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Total UMI count"</span><span class="op">)</span></span></code></pre>
-</div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-2-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The distribution of total counts (called the unique molecular
-identifier or UMI count) exhibits a sharp transition between barcodes
-with large and small total counts, probably corresponding to
-cell-containing and empty droplets respectively.</p>
-<p>A simple approach would be to apply a threshold on the total count to
-only retain those barcodes with large totals. However, this may
-unnecessarily discard libraries derived from cell types with low RNA
-content.</p>
 <div id="callout1" class="callout">
 <div class="callout-square">
 <i class="callout-icon" data-feather="bell"></i>
@@ -1155,6 +1134,71 @@ <h3 class="callout-title">Callout</h3>
 </div>
 </div>
 </div>
+<p>We can visualize barcode read totals to visualize the distinction
+between empty droplets and properly profiled single cells in a so-called
+“knee plot”:</p>
+<div class="codewrapper sourceCode" id="cb7">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">bcrank</span> <span class="op">&lt;-</span> <span class="fu">barcodeRanks</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'm' in selecting a method for function 'barcodeRanks': error in evaluating the argument 'object' in selecting a method for function 'counts': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb9">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Only showing unique points for plotting speed.</span></span>
+<span><span class="va">uniq</span> <span class="op">&lt;-</span> <span class="op">!</span><span class="fu">duplicated</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">rank</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'duplicated': object 'bcrank' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb11">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">line_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>cutoff <span class="op">=</span> <span class="fu">names</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>                      value  <span class="op">=</span> <span class="fu">unlist</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'metadata': object 'bcrank' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb13">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">bcrank</span><span class="op">[</span><span class="va">uniq</span>,<span class="op">]</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">rank</span>, <span class="va">total</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">geom_hline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">line_df</span>,</span>
+<span>               <span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">cutoff</span>,</span>
+<span>                   yintercept <span class="op">=</span> <span class="va">value</span><span class="op">)</span>,</span>
+<span>               lty <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Total UMI count"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'bcrank' not found</code></pre>
+</div>
+<p>The distribution of total counts (called the unique molecular
+identifier or UMI count) exhibits a sharp transition between barcodes
+with large and small total counts, probably corresponding to
+cell-containing and empty droplets respectively.</p>
+<p>A simple approach would be to apply a threshold on the total count to
+only retain those barcodes with large totals. However, this may
+unnecessarily discard libraries derived from cell types with low RNA
+content.</p>
 <div id="challenge1" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
@@ -1171,18 +1215,18 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb4">
+<div class="codewrapper sourceCode" id="cb15">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">median</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">total</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>[1] 2</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'median': object 'bcrank' not found</code></pre>
 </div>
 <p>Just 2! Clearly many barcodes produce practically no output.</p>
 <!-- This is a direct application challenge of of  "It is likely that most of these droplets are empty and are capturing only ambient or background RNA" and a synthesis challenge of the previous episode where they learned to access columns of DataFrames. -->
@@ -1202,7 +1246,7 @@ <h3 id="testing-for-empty-droplets">Testing for empty droplets<a class="anchor"
 <p>We call cells at a false discovery rate (FDR) of 0.1%, meaning that
 no more than 0.1% of our called barcodes should be empty droplets on
 average.</p>
-<div class="codewrapper sourceCode" id="cb6">
+<div class="codewrapper sourceCode" id="cb17">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1211,40 +1255,45 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># this may take a few minutes</span></span>
-<span><span class="va">e.out</span> <span class="op">&lt;-</span> <span class="fu">emptyDrops</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span></span></code></pre>
+<span><span class="va">e.out</span> <span class="op">&lt;-</span> <span class="fu">emptyDrops</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Mode   FALSE    TRUE    NA's
-logical    6184    3131  513239 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'm' in selecting a method for function 'emptyDrops': error in evaluating the argument 'object' in selecting a method for function 'counts': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb8">
+<div class="codewrapper sourceCode" id="cb19">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="fu">which</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 3131
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(3131): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGTCAGTCTGATTG
-  TTTGTCATCTGAGTGT
-colData names(0):
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'e.out' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb21">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="fu">which</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb23">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>The result confirms our expectation: only 3,131 droplets contain a
 cell, while the large majority of droplets are empty.</p>
@@ -1253,7 +1302,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler1" aria-expanded="false" aria-controls="collapseSpoiler1">
   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> Setting the Random Seed </h3>
 </button>
-<div id="collapseSpoiler1" class="accordion-collapse collapse" aria-labelledby="headingSpoiler1" data-bs-parent="#accordionSpoiler1">
+<div id="collapseSpoiler1" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler1" aria-labelledby="headingSpoiler1">
 <div class="accordion-body">
 <p>Whenever your code involves the generation of random numbers, it’s a
 good practice to set the random seed in R with
@@ -1313,115 +1362,115 @@ <h3 id="choice-of-quality-control-metrics">Choice of quality control metrics<a c
 <code>EnsDb</code> mouse package available in Bioconductor, but a more
 updated version of Ensembl can be used through the
 <code>AnnotationHub</code> or <code>biomaRt</code> packages.</p>
-<div class="codewrapper sourceCode" id="cb10">
+<div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">chr.loc</span> <span class="op">&lt;-</span> <span class="fu">mapIds</span><span class="op">(</span><span class="va">EnsDb.Mmusculus.v79</span>,</span>
 <span>                  keys    <span class="op">=</span> <span class="fu">rownames</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>,</span>
 <span>                  keytype <span class="op">=</span> <span class="st">"GENEID"</span>, </span>
-<span>                  column  <span class="op">=</span> <span class="st">"SEQNAME"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">is.mito</span> <span class="op">&lt;-</span> <span class="fu">which</span><span class="op">(</span><span class="va">chr.loc</span> <span class="op">==</span> <span class="st">"MT"</span><span class="op">)</span></span></code></pre>
+<span>                  column  <span class="op">=</span> <span class="st">"SEQNAME"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb27">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">is.mito</span> <span class="op">&lt;-</span> <span class="fu">which</span><span class="op">(</span><span class="va">chr.loc</span> <span class="op">==</span> <span class="st">"MT"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'which': object 'chr.loc' not found</code></pre>
 </div>
 <p>We can use the <code>scuttle</code> package to compute a set of
 quality control metrics, specifying that we want to use the
 mitochondrial genes as a special set of features.</p>
-<div class="codewrapper sourceCode" id="cb11">
+<div class="codewrapper sourceCode" id="cb29">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu">perCellQCMetrics</span><span class="op">(</span><span class="va">sce</span>, subsets <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>Mito <span class="op">=</span> <span class="va">is.mito</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">cbind</span><span class="op">(</span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="va">df</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu">perCellQCMetrics</span><span class="op">(</span><span class="va">sce</span>, subsets <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>Mito <span class="op">=</span> <span class="va">is.mito</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'perCellQCMetrics': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb31">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">cbind</span><span class="op">(</span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="va">df</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb33">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>DataFrame with 3131 rows and 6 columns
-                       sum  detected subsets_Mito_sum subsets_Mito_detected
-                 &lt;numeric&gt; &lt;integer&gt;        &lt;numeric&gt;             &lt;integer&gt;
-AAACCTGAGACTGTAA     27577      5418              471                    10
-AAACCTGAGATGCCTT     29309      5405              679                    10
-AAACCTGAGCAGCCTC     28795      5218              480                    12
-AAACCTGCATACTCTT     34794      4781              496                    12
-AAACCTGGTGGTACAG       262       229                0                     0
-...                    ...       ...              ...                   ...
-TTTGGTTTCGCCATAA     38398      6020              252                    12
-TTTGTCACACCCTATC      3013      1451              123                     9
-TTTGTCACATTCTCAT      1472       675              599                    11
-TTTGTCAGTCTGATTG       361       293                0                     0
-TTTGTCATCTGAGTGT       267       233               16                     6
-                 subsets_Mito_percent     total
-                            &lt;numeric&gt; &lt;numeric&gt;
-AAACCTGAGACTGTAA              1.70795     27577
-AAACCTGAGATGCCTT              2.31669     29309
-AAACCTGAGCAGCCTC              1.66696     28795
-AAACCTGCATACTCTT              1.42553     34794
-AAACCTGGTGGTACAG              0.00000       262
-...                               ...       ...
-TTTGGTTTCGCCATAA             0.656284     38398
-TTTGTCACACCCTATC             4.082310      3013
-TTTGTCACATTCTCAT            40.692935      1472
-TTTGTCAGTCTGATTG             0.000000       361
-TTTGTCATCTGAGTGT             5.992509       267</code></pre>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'sce' not found</code></pre>
 </div>
 <p>Now that we have computed the metrics, we have to decide on
 thresholds to define high- and low-quality samples. We could check how
 many cells are above/below a certain fixed threshold. For instance,</p>
-<div class="codewrapper sourceCode" id="cb13">
+<div class="codewrapper sourceCode" id="cb35">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">sum</span> <span class="op">&lt;</span> <span class="fl">10000</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>
-FALSE  TRUE
- 2477   654 </code></pre>
+<pre class="error" tabindex="0"><code>Error in df$sum: object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb15">
+<div class="codewrapper sourceCode" id="cb37">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">subsets_Mito_percent</span> <span class="op">&gt;</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>
-FALSE  TRUE
- 2761   370 </code></pre>
+<pre class="error" tabindex="0"><code>Error in df$subsets_Mito_percent: object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>or we could look at the distribution of such metrics and use a data
 adaptive threshold.</p>
-<div class="codewrapper sourceCode" id="cb17">
+<div class="codewrapper sourceCode" id="cb39">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">detected</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-     98    4126    5168    4455    5670    7908 </code></pre>
+<pre class="error" tabindex="0"><code>Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'summary': object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb19">
+<div class="codewrapper sourceCode" id="cb41">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">subsets_Mito_percent</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-  0.000   1.155   1.608   5.079   2.182  66.968 </code></pre>
+<pre class="error" tabindex="0"><code>Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'summary': object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>We can use the <code>perCellQCFilters</code> function to apply a set
 of common adaptive filters to identify low-quality cells. By default, we
@@ -1429,38 +1478,39 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 deviations (MADs) from the median in the “problematic” direction. This
 is loosely motivated by the fact that such a filter will retain 99% of
 non-outlier values that follow a normal distribution.</p>
-<div class="codewrapper sourceCode" id="cb21">
+<div class="codewrapper sourceCode" id="cb43">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">reasons</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">reasons</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">reasons</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>DataFrame with 3131 rows and 4 columns
-         low_lib_size   low_n_features high_subsets_Mito_percent   discard
-     &lt;outlier.filter&gt; &lt;outlier.filter&gt;          &lt;outlier.filter&gt; &lt;logical&gt;
-1               FALSE            FALSE                     FALSE     FALSE
-2               FALSE            FALSE                     FALSE     FALSE
-3               FALSE            FALSE                     FALSE     FALSE
-4               FALSE            FALSE                     FALSE     FALSE
-5                TRUE             TRUE                     FALSE      TRUE
-...               ...              ...                       ...       ...
-3127            FALSE            FALSE                     FALSE     FALSE
-3128             TRUE             TRUE                      TRUE      TRUE
-3129             TRUE             TRUE                      TRUE      TRUE
-3130             TRUE             TRUE                     FALSE      TRUE
-3131             TRUE             TRUE                      TRUE      TRUE</code></pre>
+<pre class="error" tabindex="0"><code>Error in x[[sum.field]]: object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb23">
+<div class="codewrapper sourceCode" id="cb45">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">reasons</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'reasons' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb47">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span> <span class="op">&lt;-</span> <span class="va">reasons</span><span class="op">$</span><span class="va">discard</span></span></code></pre>
 </div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'reasons' not found</code></pre>
+</div>
 <div id="challenge2" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
@@ -1469,7 +1519,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
 <p>Maybe our sample preparation was poor and we want the QC to be more
-strict. How could we change the set the QC filtering to use 4 MADs as
+strict. How could we change the set the QC filtering to use 2.5 MADs as
 the threshold for outlier calling?</p>
 </div>
 </div>
@@ -1479,14 +1529,19 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
-<p>You set <code>nmads = 4</code> like so:</p>
-<div class="codewrapper sourceCode" id="cb24">
+<p>You set <code>nmads = 2.5</code> like so:</p>
+<div class="codewrapper sourceCode" id="cb49">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">reasons_strict</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, nmads <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">reasons_strict</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, nmads <span class="op">=</span> <span class="fl">2.5</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in x[[sum.field]]: object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>You would then need to reassign the <code>discard</code> column as
 well, but we’ll stick with the 3 MADs default for now.
@@ -1505,69 +1560,87 @@ <h3 id="diagnostic-plots">Diagnostic plots<a class="anchor" aria-label="anchor"
 outliers and with a marked difference from “regular” cells (e.g., a
 bimodal distribution or a long tail). Moreover, if there are too many
 discarded cells, further exploration might be needed.</p>
-<div class="codewrapper sourceCode" id="cb25">
+<div class="codewrapper sourceCode" id="cb51">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Total count"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb26">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb53">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"detected"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Detected features"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb27">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb55">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Mito percent"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-3.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>While the univariate distribution of QC metrics can give some insight
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>While the univariate distribution of QC metrics can give some insight
 on the quality of the sample, often looking at the bivariate
 distribution of QC metrics is useful, e.g., to confirm that there are no
 cells with both large total counts and large mitochondrial counts, to
 ensure that we are not inadvertently removing high-quality cells that
 happen to be highly metabolically active.</p>
-<div class="codewrapper sourceCode" id="cb28">
+<div class="codewrapper sourceCode" id="cb57">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>,  x <span class="op">=</span><span class="st">"sum"</span>, y <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It could also be a good idea to perform a differential expression
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>It could also be a good idea to perform a differential expression
 analysis between retained and discarded cells to check wether we are
 removing an unusual cell population rather than low-quality libraries
 (see <a href="https://bioconductor.org/books/release/OSCA.advanced/quality-control-redux.html#qc-discard-cell-types" class="external-link">Section
 1.5 of OSCA advanced</a>).</p>
 <p>Once we are happy with the results, we can discard the low-quality
 cells by subsetting the original object.</p>
-<div class="codewrapper sourceCode" id="cb29">
+<div class="codewrapper sourceCode" id="cb59">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="op">!</span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span><span class="op">]</span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="op">!</span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span><span class="op">]</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(7): sum detected ... total discard
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb61">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 </div>
 </section><section><h2 class="section-heading" id="normalization">Normalization<a class="anchor" aria-label="anchor" href="#normalization"></a>
@@ -1602,31 +1675,53 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 defined such that the mean size factor across all cells is equal to 1.
 This ensures that the normalized expression values are on the same scale
 as the original counts.</p>
-<div class="codewrapper sourceCode" id="cb31">
+<div class="codewrapper sourceCode" id="cb63">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">lib.sf</span> <span class="op">&lt;-</span> <span class="fu">librarySizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">lib.sf</span> <span class="op">&lt;-</span> <span class="fu">librarySizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.2730  0.7879  0.9600  1.0000  1.1730  2.5598 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'librarySizeFactors': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb33">
+<div class="codewrapper sourceCode" id="cb65">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>size_factor <span class="op">=</span> <span class="va">lib.sf</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'lib.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb67">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>size_factor <span class="op">=</span> <span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'lib.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb69">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_histogram</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-14-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="section level3">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<div class="section level3">
 <h3 id="normalization-by-deconvolution">Normalization by deconvolution<a class="anchor" aria-label="anchor" href="#normalization-by-deconvolution"></a>
 </h3>
 <p>Library size normalization is not optimal, as it assumes that the
@@ -1649,83 +1744,127 @@ <h3 id="normalization-by-deconvolution">Normalization by deconvolution<a class="
 <p>Note that while we focus on normalization by deconvolution here, many
 other methods have been proposed and lead to similar performance (see <a href="reference.html#litref">Borella 2022</a> for a comparative
 review).</p>
-<div class="codewrapper sourceCode" id="cb34">
+<div class="codewrapper sourceCode" id="cb71">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="fu">quickCluster</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> </span>
-<span></span>
-<span><span class="fu">table</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span></span></code></pre>
+<span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="fu">quickCluster</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> </span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>clust
-  1   2   3   4   5   6   7   8   9  10  11  12  13
-273 159 250 122 187 201 154 252 152 169 199 215 104 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'quickCluster': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb36">
+<div class="codewrapper sourceCode" id="cb73">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb75">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">deconv.sf</span> <span class="op">&lt;-</span> <span class="fu">pooledSizeFactors</span><span class="op">(</span><span class="va">sce</span>, cluster <span class="op">=</span> <span class="va">clust</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pooledSizeFactors': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb77">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb79">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb81">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deconv.sf</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span>, cluster <span class="op">=</span> <span class="va">clust</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.3100  0.8028  0.9626  1.0000  1.1736  2.7858 </code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb38">
+<div class="codewrapper sourceCode" id="cb83">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span>
-<span></span>
-<span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span>, <span class="va">deconv_sf</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span>, <span class="va">deconv_sf</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_abline</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">clust</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-16-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>Once we have computed the size factors, we compute the normalized
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<p>Once we have computed the size factors, we compute the normalized
 expression values for each cell by dividing the count for each gene with
 the appropriate size factor for that cell. Since we are typically going
 to work with log-transformed counts, the function
 <code>logNormCounts</code> also log-transforms the normalized values,
 creating a new assay called <code>logcounts</code>.</p>
-<div class="codewrapper sourceCode" id="cb39">
+<div class="codewrapper sourceCode" id="cb85">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span>
-<span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">logNormCounts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb87">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">logNormCounts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'logNormCounts': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb89">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <div id="challenge3" class="callout challenge">
 <div class="callout-square">
@@ -1736,16 +1875,16 @@ <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
 <p>Fill in the blanks for normalization that uses simpler library size
 factors instead of deconvolution.</p>
-<div class="codewrapper sourceCode" id="cb41">
+<div class="codewrapper sourceCode" id="cb91">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
-<span id="cb41-2"><a href="#cb41-2" tabindex="-1"></a></span>
-<span id="cb41-3"><a href="#cb41-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
-<span id="cb41-4"><a href="#cb41-4" tabindex="-1"></a></span>
-<span id="cb41-5"><a href="#cb41-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
-<span id="cb41-6"><a href="#cb41-6" tabindex="-1"></a></span>
-<span id="cb41-7"><a href="#cb41-7" tabindex="-1"></a>sce</span></code></pre>
+<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb91-1"><a href="#cb91-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
+<span id="cb91-2"><a href="#cb91-2" tabindex="-1"></a></span>
+<span id="cb91-3"><a href="#cb91-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
+<span id="cb91-4"><a href="#cb91-4" tabindex="-1"></a></span>
+<span id="cb91-5"><a href="#cb91-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
+<span id="cb91-6"><a href="#cb91-6" tabindex="-1"></a></span>
+<span id="cb91-7"><a href="#cb91-7" tabindex="-1"></a>sce</span></code></pre>
 </div>
 </div>
 </div>
@@ -1755,9 +1894,9 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb42">
+<div class="codewrapper sourceCode" id="cb92">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1804,25 +1943,57 @@ <h3 id="quantifying-per-gene-variation">Quantifying per-gene variation<a class="
 its abundance than its underlying biological heterogeneity. To account
 for this, the <code>modelGeneVar</code> function fits a trend to the
 variance with respect to abundance across all genes.</p>
-<div class="codewrapper sourceCode" id="cb43">
+<div class="codewrapper sourceCode" id="cb93">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dec.sce</span> <span class="op">&lt;-</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">fit.sce</span> <span class="op">&lt;-</span> <span class="fu">metadata</span><span class="op">(</span><span class="va">dec.sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">mean_var_df</span> <span class="op">=</span> <span class="fu">data.frame</span><span class="op">(</span>mean <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>                         var  <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">var</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">mean_var_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">mean</span>, <span class="va">var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="va">dec.sce</span> <span class="op">&lt;-</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'modelGeneVar': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb95">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">fit.sce</span> <span class="op">&lt;-</span> <span class="fu">metadata</span><span class="op">(</span><span class="va">dec.sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'metadata': object 'dec.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb97">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">mean_var_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>mean <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">mean</span>,</span>
+<span>                          var  <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">var</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'fit.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb99">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">mean_var_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">mean</span>, <span class="va">var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_function</span><span class="op">(</span>fun <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">trend</span>, </span>
 <span>                  color <span class="op">=</span> <span class="st">"dodgerblue"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>x <span class="op">=</span> <span class="st">"Mean of log-expression"</span>,</span>
 <span>         y <span class="op">=</span> <span class="st">"Variance of log-expression"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The blue line represents the uninteresting “technical” variance for
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'mean_var_df' not found</code></pre>
+</div>
+<p>The blue line represents the uninteresting “technical” variance for
 any given gene abundance. The genes with a lot of additional variance
 exhibit interesting “biological” variation.</p>
 </div>
@@ -1839,19 +2010,27 @@ <h3 id="selecting-highly-variable-genes">Selecting highly variable genes<a class
 differentially expressed across our cell populations and hence select
 10% of genes as highly variable (e.g., by setting
 <code>prop = 0.1</code>).</p>
-<div class="codewrapper sourceCode" id="cb44">
+<div class="codewrapper sourceCode" id="cb101">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">hvg.sce.var</span> <span class="op">&lt;-</span> <span class="fu">getTopHVGs</span><span class="op">(</span><span class="va">dec.sce</span>, n <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">head</span><span class="op">(</span><span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">hvg.sce.var</span> <span class="op">&lt;-</span> <span class="fu">getTopHVGs</span><span class="op">(</span><span class="va">dec.sce</span>, n <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dec.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb103">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">head</span><span class="op">(</span><span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>[1] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919"
-[4] "ENSMUSG00000052187" "ENSMUSG00000048583" "ENSMUSG00000051855"</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'hvg.sce.var' not found</code></pre>
 </div>
 <div id="challenge4" class="callout challenge">
 <div class="callout-square">
@@ -1871,7 +2050,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p><code>modelGeneVar()</code> can take a <code>block</code>
 argument.</p>
@@ -1884,11 +2063,11 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <p>Use the <code>block</code> argument in the call to
 <code>modelGeneVar()</code> like so:</p>
-<div class="codewrapper sourceCode" id="cb46">
+<div class="codewrapper sourceCode" id="cb105">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1931,7 +2110,7 @@ <h3 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a c
 replacing the original variables (genes) by fewer linear combinations of
 these variables, that are orthogonal and have successively maximal
 variance. Such linear combinations seek to “separate out” the
-observations (cells), while loosing as little information as
+observations (cells), while losing as little information as
 possible.</p>
 <p>Without getting into the technical details, one nice feature of PCA
 is that the principal components (PCs) are ordered by how much variance
@@ -1944,70 +2123,90 @@ <h3 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a c
 <p>One simple way to maximize our chance of capturing biological
 variation is by computing the PCs starting from the highly variable
 genes identified before.</p>
-<div class="codewrapper sourceCode" id="cb47">
+<div class="codewrapper sourceCode" id="cb106">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runPCA</span><span class="op">(</span><span class="va">sce</span>, subset_row <span class="op">=</span> <span class="va">hvg.sce.var</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runPCA</span><span class="op">(</span><span class="va">sce</span>, subset_row <span class="op">=</span> <span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(1): PCA
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'runPCA': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb108">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>By default, <code>runPCA</code> computes the first 50 principal
 components. We can check how much original variability they explain.
 These values are stored in the attributes of the <code>percentVar</code>
 reducedDim:</p>
-<div class="codewrapper sourceCode" id="cb49">
+<div class="codewrapper sourceCode" id="cb110">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pct_var_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>PC <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">50</span>,</span>
-<span>                         pct_var <span class="op">=</span> <span class="fu">attr</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="st">"percentVar"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">pct_var_df</span>,</span>
+<span>                         pct_var <span class="op">=</span> <span class="fu">attr</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="st">"percentVar"</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb112">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">pct_var_df</span>,</span>
 <span>       <span class="fu">aes</span><span class="op">(</span><span class="va">PC</span>, <span class="va">pct_var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Variance explained (%)"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>You can see the first two PCs capture the largest amount of
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'pct_var_df' not found</code></pre>
+</div>
+<p>You can see the first two PCs capture the largest amount of
 variation, but in this case you have to take the first 8 PCs before
 you’ve captured 50% of the total.</p>
 <p>And we can of course visualize the first 2-3 components, perhaps
 color-coding each point by an interesting feature, in this case the
 total number of UMIs per cell.</p>
-<div class="codewrapper sourceCode" id="cb50">
+<div class="codewrapper sourceCode" id="cb114">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotPCA</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"sum"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-25-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It can be helpful to compare pairs of PCs. This can be done with the
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'plotPCA': object 'sce' not found</code></pre>
+</div>
+<p>It can be helpful to compare pairs of PCs. This can be done with the
 <code>ncomponents</code> argument to <code>plotReducedDim()</code>. For
 example if one batch or cell type splits off on a particular PC, this
 can help visualize the effect of that.</p>
-<div class="codewrapper sourceCode" id="cb51">
+<div class="codewrapper sourceCode" id="cb116">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotReducedDim</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span>, ncomponents <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-26-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
 </div>
 <div class="section level3">
 <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anchor" href="#non-linear-methods"></a>
@@ -2019,27 +2218,55 @@ <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anch
 <p>These methods attempt to find a low-dimensional representation of the
 data that attempt to preserve pair-wise distance and structure in
 high-dimensional gene space as best as possible.</p>
-<div class="codewrapper sourceCode" id="cb52">
+<div class="codewrapper sourceCode" id="cb118">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runTSNE</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runTSNE</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb120">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-27-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb53">
+<div class="codewrapper sourceCode" id="cb122">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">111</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runUMAP</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runUMAP</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb124">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
+<p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
 relative sizes and positions of the visual clusters may be misleading,
 as they tend to inflate dense clusters and compress sparse ones, such
 that we cannot use the size as a measure of subpopulation
@@ -2053,28 +2280,16 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 and replotting without the need for recomputing. Note the added
 <code>reducedDimNames</code> row when printing <code>sce</code>
 here:</p>
-<div class="codewrapper sourceCode" id="cb54">
+<div class="codewrapper sourceCode" id="cb126">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(3): PCA TSNE UMAP
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>Despite their shortcomings, t-SNE and UMAP can be useful
 visualization techniques. When using them, it is important to consider
@@ -2100,9 +2315,9 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb56">
+<div class="codewrapper sourceCode" id="cb128">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -2171,78 +2386,139 @@ <h3 id="computing-doublet-densities">Computing doublet densities<a class="anchor
 very likely to be a doublet itself.</p>
 <p>This approach is implemented below using the <code>scDblFinder</code>
 library. We then visualize the scores in a t-SNE plot.</p>
-<div class="codewrapper sourceCode" id="cb57">
+<div class="codewrapper sourceCode" id="cb129">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">dbl.dens</span> <span class="op">&lt;-</span> <span class="fu">computeDoubletDensity</span><span class="op">(</span><span class="va">sce</span>, subset.row <span class="op">=</span> <span class="va">hvg.sce.var</span>,</span>
-<span>                                  d <span class="op">=</span> <span class="fu">ncol</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.dens</span><span class="op">)</span></span></code></pre>
+<span>                                  d <span class="op">=</span> <span class="fu">ncol</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
- 0.04874  0.28757  0.46790  0.65614  0.82371 14.88032 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'computeDoubletDensity': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb59">
+<div class="codewrapper sourceCode" id="cb131">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">DoubletScore</span> <span class="op">&lt;-</span> <span class="va">dbl.dens</span></span>
-<span></span>
-<span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.dens</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'dbl.dens' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb133">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">DoubletScore</span> <span class="op">&lt;-</span> <span class="va">dbl.dens</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.dens' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb135">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-31-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>We can explicitly convert this into doublet calls by identifying
+<p>We can explicitly convert this into doublet calls by identifying
 large outliers for the score within each sample. Here we use the
 “griffiths” method to do so.</p>
-<div class="codewrapper sourceCode" id="cb60">
+<div class="codewrapper sourceCode" id="cb137">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dbl.calls</span> <span class="op">&lt;-</span> <span class="fu">doubletThresholding</span><span class="op">(</span><span class="fu">data.frame</span><span class="op">(</span>score <span class="op">=</span> <span class="va">dbl.dens</span><span class="op">)</span>,</span>
 <span>                                 method <span class="op">=</span> <span class="st">"griffiths"</span>,</span>
-<span>                                 returnType <span class="op">=</span> <span class="st">"call"</span><span class="op">)</span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.calls</span><span class="op">)</span></span></code></pre>
+<span>                                 returnType <span class="op">=</span> <span class="st">"call"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>singlet doublet
-   2124     313 </code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.dens' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb62">
+<div class="codewrapper sourceCode" id="cb139">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">doublet</span> <span class="op">&lt;-</span> <span class="va">dbl.calls</span></span>
-<span></span>
-<span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"DoubletScore"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.calls</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'dbl.calls' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb141">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">doublet</span> <span class="op">&lt;-</span> <span class="va">dbl.calls</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-32-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb63">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.calls' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb143">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"DoubletScore"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb145">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-32-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>One way to determine whether a cell is in a real transient state or
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<p>One way to determine whether a cell is in a real transient state or
 it is a doublet is to check the number of detected genes and total UMI
 counts.</p>
-<div class="codewrapper sourceCode" id="cb64">
+<div class="codewrapper sourceCode" id="cb147">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, <span class="st">"detected"</span>, <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-33-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb65">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb149">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, <span class="st">"detected"</span>, <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-33-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>In this case, we only have a few doublets at the periphery of some
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>In this case, we only have a few doublets at the periphery of some
 clusters. It could be fine to keep the doublets in the analysis, but it
 may be safer to discard them to avoid biases in downstream analysis
 (e.g., differential expression).</p>
@@ -2271,38 +2547,69 @@ <h3 class="callout-title">Exercise 1: Normalization</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb66">
+<div class="codewrapper sourceCode" id="cb151">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deconv.sf2</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="co"># dropped `cluster = clust` here</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf2</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">deconv.sf2</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="co"># dropped `cluster = clust` here</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.2985  0.8142  0.9583  1.0000  1.1582  2.7054 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pooledSizeFactors': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb68">
+<div class="codewrapper sourceCode" id="cb153">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf2</span> <span class="op">&lt;-</span> <span class="va">deconv.sf2</span></span>
-<span></span>
-<span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">deconv_sf</span>, <span class="va">deconv_sf2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf2</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'deconv.sf2' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb155">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf2</span> <span class="op">&lt;-</span> <span class="va">deconv.sf2</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf2' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb157">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb159">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">deconv_sf</span>, <span class="va">deconv_sf2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_abline</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">clust</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">facet_wrap</span><span class="op">(</span><span class="fu">vars</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-34-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>You can see that we get largely similar results, though for clusters
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<p>You can see that we get largely similar results, though for clusters
 3 and 9 there’s a slight deviation from the <code>y=x</code>
 relationship because these clusters (which are fairly distinct from the
 other clusters) are now being pooled with cells from other clusters.
@@ -2330,12 +2637,12 @@ <h3 class="callout-title">Exercise 2: PBMC Data</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
   <h4 class="accordion-header" id="headingSolution7"> Show me the solution </h4>
 </button>
-<div id="collapseSolution7" class="accordion-collapse collapse" aria-labelledby="headingSolution7" data-bs-parent="#accordionSolution7">
+<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
 <div class="accordion-body">
 <p>The first few lines here read the data from ExperimentHub and the
 mitochondrial genes are identified by gene symbols in the row data.
 Otherwise the steps are the same:</p>
-<div class="codewrapper sourceCode" id="cb69">
+<div class="codewrapper sourceCode" id="cb161">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -2438,7 +2745,7 @@ <h3 class="callout-title">Extension challenge 1: Spike-ins</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
   <h4 class="accordion-header" id="headingSolution8"> Show me the solution </h4>
 </button>
-<div id="collapseSolution8" class="accordion-collapse collapse" aria-labelledby="headingSolution8" data-bs-parent="#accordionSolution8">
+<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
 <div class="accordion-body">
 <p>Spike-ins are deliberately-introduced exogeneous RNA from an exotic
 or synthetic source at a known concentration. This provides a known
@@ -2482,7 +2789,7 @@ <h3 class="callout-title">Extension challenge 3: Reduced dimensionality represen
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
   <h4 class="accordion-header" id="headingSolution9"> Show me the solution </h4>
 </button>
-<div id="collapseSolution9" class="accordion-collapse collapse" aria-labelledby="headingSolution9" data-bs-parent="#accordionSolution9">
+<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
 <div class="accordion-body">
 <p>Mathematically, this would require the data to fall on a
 two-dimensional plane (for linear methods like PCA) or a smooth 2D
@@ -2756,7 +3063,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <p>We see in the help documentation for <code>?clusterCells</code> that
 all of the clustering algorithm details are handled through the
@@ -2953,7 +3260,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <p>You can see that at least among the top markers, cluster 6 (pale
 green) tends to have the least separation from cluster 1.</p>
@@ -3251,7 +3558,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb31">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -3515,7 +3822,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb43">
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@@ -3665,7 +3972,7 @@ <h3 class="callout-title">Exercise 1: Clustering</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>The <code>NNGraphParam</code> constructor has an argument
 <code>cluster.args</code>. This allows to specify arguments passed on to
@@ -3682,7 +3989,7 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
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@@ -3721,7 +4028,7 @@ <h3 class="callout-title">Exercise 2: Reference marker genes</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb47">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -3794,7 +4101,7 @@ <h3 class="callout-title">Extension Challenge 1: Group pair comparisons</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
   <h4 class="accordion-header" id="headingSolution7"> Show me the solution </h4>
 </button>
-<div id="collapseSolution7" class="accordion-collapse collapse" aria-labelledby="headingSolution7" data-bs-parent="#accordionSolution7">
+<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
 <div class="accordion-body">
 <p>One important reason why is because averages over all other clusters
 can be sensitive to the cell type composition. If a rare cell type shows
@@ -3828,7 +4135,7 @@ <h3 class="callout-title">Extension Challenge 2: Parallelizing SingleR</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
   <h4 class="accordion-header" id="headingSolution8"> Show me the solution </h4>
 </button>
-<div id="collapseSolution8" class="accordion-collapse collapse" aria-labelledby="headingSolution8" data-bs-parent="#accordionSolution8">
+<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
 <div class="accordion-body">
 <p>Use <code>BiocParallel</code> and the <code>BPPARAM</code> argument!
 This example will set it to use four cores on your laptop, but you can
@@ -3870,7 +4177,7 @@ <h3 class="callout-title">Extension Challenge 3: Critical inspection of diagnost
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
   <h4 class="accordion-header" id="headingSolution9"> Show me the solution </h4>
 </button>
-<div id="collapseSolution9" class="accordion-collapse collapse" aria-labelledby="headingSolution9" data-bs-parent="#accordionSolution9">
+<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
 <div class="accordion-body">
 <p>The example that jumps out most strongly to the eye is ExE endoderm,
 which doesn’t show clear separate modes. Simultaneously, Endothelium
@@ -4163,7 +4470,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <p>Samples 5 and 6 were from the same “pool” of cells. Looking at the
 documentation for the dataset under <code>?WTChimeraData</code> we see
@@ -4228,7 +4535,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
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 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <p>False. Batch-level data can be retained through confounding with
 experimental factors or poor ability to distinguish experimental effects
@@ -4681,7 +4988,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
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 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
 <p>“logFC” stands for log fold-change. <code>edgeR</code> uses a log2
 convention. Rather than reporting e.g. a 5-fold increase, it’s better to
@@ -5007,7 +5314,7 @@ <h3 class="callout-title">Exercise 1: Heatmaps</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>You can simply hand <code>pheatmap()</code> a matrix as its only
 argument. <code>pheatmap()</code> has a million options you can tweak,
@@ -5021,7 +5328,7 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb52">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -5056,7 +5363,7 @@ <h3 class="callout-title">Exercise 2: Model specification and comparison</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint2" aria-expanded="false" aria-controls="collapseHint2">
   <h4 class="accordion-header" id="headingHint2"> Give me a hint </h4>
 </button>
-<div id="collapseHint2" class="accordion-collapse collapse" aria-labelledby="headingHint2" data-bs-parent="#accordionHint2">
+<div id="collapseHint2" class="accordion-collapse collapse" data-bs-parent="#accordionHint2" aria-labelledby="headingHint2">
 <div class="accordion-body">
 <p>After running the second pseudobulk DGE, you can join the two
 <code>DataFrame</code>s of <code>Erythroid3</code> statistics using the
@@ -5071,7 +5378,7 @@ <h4 class="accordion-header" id="headingHint2"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb53">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -5154,7 +5461,7 @@ <h3 class="callout-title">Extension challenge 1: Group effects</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
 <p>It’s important to have multiple samples within each experimental
 group because it helps the batch effect correction algorithm distinguish
@@ -6942,7 +7249,7 @@ <h3 class="callout-title">Exercise 1: Basic counting + piping</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
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 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb23">
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@@ -6993,7 +7300,7 @@ <h3 class="callout-title">Exercise 2: Tissue &amp; type counting</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
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 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
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@@ -7034,7 +7341,7 @@ <h3 class="callout-title">Exercise 3: Comparing metadata categories</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
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@@ -7126,7 +7433,7 @@ <h3 class="callout-title">Exercise 4: Highly specific cell groups</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
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 <div class="codewrapper sourceCode" id="cb31">
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@@ -7235,8 +7542,8 @@ <h3 class="callout-title">Key Points</h3>
   "url": "https://ccb-hms.github.io/osca-carpentries/aio.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/aio.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/cell_type_annotation.html b/cell_type_annotation.html
index 9552ddb..1a66a2f 100644
--- a/cell_type_annotation.html
+++ b/cell_type_annotation.html
@@ -516,7 +516,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <p>We see in the help documentation for <code>?clusterCells</code> that
 all of the clustering algorithm details are handled through the
@@ -710,7 +710,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <p>You can see that at least among the top markers, cluster 6 (pale
 green) tends to have the least separation from cluster 1.</p>
@@ -1005,7 +1005,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
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 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb31">
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 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
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@@ -1413,7 +1413,7 @@ <h3 class="callout-title">Exercise 1: Clustering</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>The <code>NNGraphParam</code> constructor has an argument
 <code>cluster.args</code>. This allows to specify arguments passed on to
@@ -1430,7 +1430,7 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb46">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1468,7 +1468,7 @@ <h3 class="callout-title">Exercise 2: Reference marker genes</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb47">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1541,7 +1541,7 @@ <h3 class="callout-title">Extension Challenge 1: Group pair comparisons</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
   <h4 class="accordion-header" id="headingSolution7"> Show me the solution </h4>
 </button>
-<div id="collapseSolution7" class="accordion-collapse collapse" aria-labelledby="headingSolution7" data-bs-parent="#accordionSolution7">
+<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
 <div class="accordion-body">
 <p>One important reason why is because averages over all other clusters
 can be sensitive to the cell type composition. If a rare cell type shows
@@ -1575,7 +1575,7 @@ <h3 class="callout-title">Extension Challenge 2: Parallelizing SingleR</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
   <h4 class="accordion-header" id="headingSolution8"> Show me the solution </h4>
 </button>
-<div id="collapseSolution8" class="accordion-collapse collapse" aria-labelledby="headingSolution8" data-bs-parent="#accordionSolution8">
+<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
 <div class="accordion-body">
 <p>Use <code>BiocParallel</code> and the <code>BPPARAM</code> argument!
 This example will set it to use four cores on your laptop, but you can
@@ -1617,7 +1617,7 @@ <h3 class="callout-title">Extension Challenge 3: Critical inspection of diagnost
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
   <h4 class="accordion-header" id="headingSolution9"> Show me the solution </h4>
 </button>
-<div id="collapseSolution9" class="accordion-collapse collapse" aria-labelledby="headingSolution9" data-bs-parent="#accordionSolution9">
+<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
 <div class="accordion-body">
 <p>The example that jumps out most strongly to the eye is ExE endoderm,
 which doesn’t show clear separate modes. Simultaneously, Endothelium
@@ -1749,7 +1749,7 @@ <h3 class="callout-title">Key Points</h3>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/cell_type_annotation.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-02",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/eda_qc.html b/eda_qc.html
index d88dea7..f0a5d9f 100644
--- a/eda_qc.html
+++ b/eda_qc.html
@@ -333,7 +333,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Exploratory data analysis and quality control</h1>
-        <p>Last updated on 2024-10-03 |
+        <p>Last updated on 2024-10-10 |
 
         <a href="https://github.com/ccb-hms/osca-carpentries/edit/main/episodes/eda_qc.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
 
@@ -401,28 +401,37 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span><span class="kw">library</span><span class="op">(</span><span class="va">scran</span><span class="op">)</span></span>
 <span><span class="kw">library</span><span class="op">(</span><span class="va">scDblFinder</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">WTChimeraData</span><span class="op">(</span>samples <span class="op">=</span> <span class="fl">5</span>, type <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">WTChimeraData</span><span class="op">(</span>samples <span class="op">=</span> <span class="fl">5</span>, type <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error: failed to load resource
+  name: EH3019
+  title: WT chimera raw counts (sample 5)
+  reason: 1 resources failed to download</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb3">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 522554
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(522554): AAACCTGAGAAACCAT AAACCTGAGAAACCGC ...
-  TTTGTCATCTTTACGT TTTGTCATCTTTCCTC
-colData names(0):
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb5">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>This is the same data we examined in the previous lesson.</p>
 </section><section><h2 class="section-heading" id="droplet-processing">Droplet processing<a class="anchor" aria-label="anchor" href="#droplet-processing"></a></h2>
@@ -430,36 +439,6 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 while we can see that we have data for more than 500,000 droplets. It is
 likely that most of these droplets are empty and are capturing only
 ambient or background RNA.</p>
-<div class="codewrapper sourceCode" id="cb3">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">bcrank</span> <span class="op">&lt;-</span> <span class="fu">barcodeRanks</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Only showing unique points for plotting speed.</span></span>
-<span><span class="va">uniq</span> <span class="op">&lt;-</span> <span class="op">!</span><span class="fu">duplicated</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">rank</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">line_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>cutoff <span class="op">=</span> <span class="fu">names</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>                      value  <span class="op">=</span> <span class="fu">unlist</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">bcrank</span><span class="op">[</span><span class="va">uniq</span>,<span class="op">]</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">rank</span>, <span class="va">total</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">geom_hline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">line_df</span>,</span>
-<span>               <span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">cutoff</span>,</span>
-<span>                   yintercept <span class="op">=</span> <span class="va">value</span><span class="op">)</span>,</span>
-<span>               lty <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Total UMI count"</span><span class="op">)</span></span></code></pre>
-</div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-2-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The distribution of total counts (called the unique molecular
-identifier or UMI count) exhibits a sharp transition between barcodes
-with large and small total counts, probably corresponding to
-cell-containing and empty droplets respectively.</p>
-<p>A simple approach would be to apply a threshold on the total count to
-only retain those barcodes with large totals. However, this may
-unnecessarily discard libraries derived from cell types with low RNA
-content.</p>
 <div id="callout1" class="callout">
 <div class="callout-square">
 <i class="callout-icon" data-feather="bell"></i>
@@ -481,6 +460,71 @@ <h3 class="callout-title">Callout</h3>
 </div>
 </div>
 </div>
+<p>We can visualize barcode read totals to visualize the distinction
+between empty droplets and properly profiled single cells in a so-called
+“knee plot”:</p>
+<div class="codewrapper sourceCode" id="cb7">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">bcrank</span> <span class="op">&lt;-</span> <span class="fu">barcodeRanks</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'm' in selecting a method for function 'barcodeRanks': error in evaluating the argument 'object' in selecting a method for function 'counts': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb9">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Only showing unique points for plotting speed.</span></span>
+<span><span class="va">uniq</span> <span class="op">&lt;-</span> <span class="op">!</span><span class="fu">duplicated</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">rank</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'duplicated': object 'bcrank' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb11">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">line_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>cutoff <span class="op">=</span> <span class="fu">names</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>                      value  <span class="op">=</span> <span class="fu">unlist</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'metadata': object 'bcrank' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb13">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">bcrank</span><span class="op">[</span><span class="va">uniq</span>,<span class="op">]</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">rank</span>, <span class="va">total</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">geom_hline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">line_df</span>,</span>
+<span>               <span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">cutoff</span>,</span>
+<span>                   yintercept <span class="op">=</span> <span class="va">value</span><span class="op">)</span>,</span>
+<span>               lty <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Total UMI count"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'bcrank' not found</code></pre>
+</div>
+<p>The distribution of total counts (called the unique molecular
+identifier or UMI count) exhibits a sharp transition between barcodes
+with large and small total counts, probably corresponding to
+cell-containing and empty droplets respectively.</p>
+<p>A simple approach would be to apply a threshold on the total count to
+only retain those barcodes with large totals. However, this may
+unnecessarily discard libraries derived from cell types with low RNA
+content.</p>
 <div id="challenge1" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
@@ -497,18 +541,18 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb4">
+<div class="codewrapper sourceCode" id="cb15">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">median</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">total</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>[1] 2</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'median': object 'bcrank' not found</code></pre>
 </div>
 <p>Just 2! Clearly many barcodes produce practically no output.</p>
 <!-- This is a direct application challenge of of  "It is likely that most of these droplets are empty and are capturing only ambient or background RNA" and a synthesis challenge of the previous episode where they learned to access columns of DataFrames. -->
@@ -527,7 +571,7 @@ <h3 id="testing-for-empty-droplets">Testing for empty droplets<a class="anchor"
 <p>We call cells at a false discovery rate (FDR) of 0.1%, meaning that
 no more than 0.1% of our called barcodes should be empty droplets on
 average.</p>
-<div class="codewrapper sourceCode" id="cb6">
+<div class="codewrapper sourceCode" id="cb17">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -536,40 +580,45 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># this may take a few minutes</span></span>
-<span><span class="va">e.out</span> <span class="op">&lt;-</span> <span class="fu">emptyDrops</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span></span></code></pre>
+<span><span class="va">e.out</span> <span class="op">&lt;-</span> <span class="fu">emptyDrops</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Mode   FALSE    TRUE    NA's
-logical    6184    3131  513239 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'm' in selecting a method for function 'emptyDrops': error in evaluating the argument 'object' in selecting a method for function 'counts': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb8">
+<div class="codewrapper sourceCode" id="cb19">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="fu">which</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'e.out' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb21">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="fu">which</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb23">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 3131
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(3131): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGTCAGTCTGATTG
-  TTTGTCATCTGAGTGT
-colData names(0):
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>The result confirms our expectation: only 3,131 droplets contain a
 cell, while the large majority of droplets are empty.</p>
@@ -578,7 +627,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler1" aria-expanded="false" aria-controls="collapseSpoiler1">
   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> Setting the Random Seed </h3>
 </button>
-<div id="collapseSpoiler1" class="accordion-collapse collapse" aria-labelledby="headingSpoiler1" data-bs-parent="#accordionSpoiler1">
+<div id="collapseSpoiler1" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler1" aria-labelledby="headingSpoiler1">
 <div class="accordion-body">
 <p>Whenever your code involves the generation of random numbers, it’s a
 good practice to set the random seed in R with
@@ -631,115 +680,115 @@ <h3 id="choice-of-quality-control-metrics">Choice of quality control metrics<a c
 <code>EnsDb</code> mouse package available in Bioconductor, but a more
 updated version of Ensembl can be used through the
 <code>AnnotationHub</code> or <code>biomaRt</code> packages.</p>
-<div class="codewrapper sourceCode" id="cb10">
+<div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">chr.loc</span> <span class="op">&lt;-</span> <span class="fu">mapIds</span><span class="op">(</span><span class="va">EnsDb.Mmusculus.v79</span>,</span>
 <span>                  keys    <span class="op">=</span> <span class="fu">rownames</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>,</span>
 <span>                  keytype <span class="op">=</span> <span class="st">"GENEID"</span>, </span>
-<span>                  column  <span class="op">=</span> <span class="st">"SEQNAME"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">is.mito</span> <span class="op">&lt;-</span> <span class="fu">which</span><span class="op">(</span><span class="va">chr.loc</span> <span class="op">==</span> <span class="st">"MT"</span><span class="op">)</span></span></code></pre>
+<span>                  column  <span class="op">=</span> <span class="st">"SEQNAME"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb27">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">is.mito</span> <span class="op">&lt;-</span> <span class="fu">which</span><span class="op">(</span><span class="va">chr.loc</span> <span class="op">==</span> <span class="st">"MT"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'which': object 'chr.loc' not found</code></pre>
 </div>
 <p>We can use the <code>scuttle</code> package to compute a set of
 quality control metrics, specifying that we want to use the
 mitochondrial genes as a special set of features.</p>
-<div class="codewrapper sourceCode" id="cb11">
+<div class="codewrapper sourceCode" id="cb29">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu">perCellQCMetrics</span><span class="op">(</span><span class="va">sce</span>, subsets <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>Mito <span class="op">=</span> <span class="va">is.mito</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">cbind</span><span class="op">(</span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="va">df</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>DataFrame with 3131 rows and 6 columns
-                       sum  detected subsets_Mito_sum subsets_Mito_detected
-                 &lt;numeric&gt; &lt;integer&gt;        &lt;numeric&gt;             &lt;integer&gt;
-AAACCTGAGACTGTAA     27577      5418              471                    10
-AAACCTGAGATGCCTT     29309      5405              679                    10
-AAACCTGAGCAGCCTC     28795      5218              480                    12
-AAACCTGCATACTCTT     34794      4781              496                    12
-AAACCTGGTGGTACAG       262       229                0                     0
-...                    ...       ...              ...                   ...
-TTTGGTTTCGCCATAA     38398      6020              252                    12
-TTTGTCACACCCTATC      3013      1451              123                     9
-TTTGTCACATTCTCAT      1472       675              599                    11
-TTTGTCAGTCTGATTG       361       293                0                     0
-TTTGTCATCTGAGTGT       267       233               16                     6
-                 subsets_Mito_percent     total
-                            &lt;numeric&gt; &lt;numeric&gt;
-AAACCTGAGACTGTAA              1.70795     27577
-AAACCTGAGATGCCTT              2.31669     29309
-AAACCTGAGCAGCCTC              1.66696     28795
-AAACCTGCATACTCTT              1.42553     34794
-AAACCTGGTGGTACAG              0.00000       262
-...                               ...       ...
-TTTGGTTTCGCCATAA             0.656284     38398
-TTTGTCACACCCTATC             4.082310      3013
-TTTGTCACATTCTCAT            40.692935      1472
-TTTGTCAGTCTGATTG             0.000000       361
-TTTGTCATCTGAGTGT             5.992509       267</code></pre>
+<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu">perCellQCMetrics</span><span class="op">(</span><span class="va">sce</span>, subsets <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>Mito <span class="op">=</span> <span class="va">is.mito</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'perCellQCMetrics': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb31">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">cbind</span><span class="op">(</span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="va">df</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb33">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'sce' not found</code></pre>
 </div>
 <p>Now that we have computed the metrics, we have to decide on
 thresholds to define high- and low-quality samples. We could check how
 many cells are above/below a certain fixed threshold. For instance,</p>
-<div class="codewrapper sourceCode" id="cb13">
+<div class="codewrapper sourceCode" id="cb35">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">sum</span> <span class="op">&lt;</span> <span class="fl">10000</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>
-FALSE  TRUE
- 2477   654 </code></pre>
+<pre class="error" tabindex="0"><code>Error in df$sum: object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb15">
+<div class="codewrapper sourceCode" id="cb37">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">subsets_Mito_percent</span> <span class="op">&gt;</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>
-FALSE  TRUE
- 2761   370 </code></pre>
+<pre class="error" tabindex="0"><code>Error in df$subsets_Mito_percent: object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>or we could look at the distribution of such metrics and use a data
 adaptive threshold.</p>
-<div class="codewrapper sourceCode" id="cb17">
+<div class="codewrapper sourceCode" id="cb39">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">detected</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-     98    4126    5168    4455    5670    7908 </code></pre>
+<pre class="error" tabindex="0"><code>Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'summary': object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb19">
+<div class="codewrapper sourceCode" id="cb41">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">subsets_Mito_percent</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-  0.000   1.155   1.608   5.079   2.182  66.968 </code></pre>
+<pre class="error" tabindex="0"><code>Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'summary': object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>We can use the <code>perCellQCFilters</code> function to apply a set
 of common adaptive filters to identify low-quality cells. By default, we
@@ -747,38 +796,39 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 deviations (MADs) from the median in the “problematic” direction. This
 is loosely motivated by the fact that such a filter will retain 99% of
 non-outlier values that follow a normal distribution.</p>
-<div class="codewrapper sourceCode" id="cb21">
+<div class="codewrapper sourceCode" id="cb43">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">reasons</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">reasons</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>DataFrame with 3131 rows and 4 columns
-         low_lib_size   low_n_features high_subsets_Mito_percent   discard
-     &lt;outlier.filter&gt; &lt;outlier.filter&gt;          &lt;outlier.filter&gt; &lt;logical&gt;
-1               FALSE            FALSE                     FALSE     FALSE
-2               FALSE            FALSE                     FALSE     FALSE
-3               FALSE            FALSE                     FALSE     FALSE
-4               FALSE            FALSE                     FALSE     FALSE
-5                TRUE             TRUE                     FALSE      TRUE
-...               ...              ...                       ...       ...
-3127            FALSE            FALSE                     FALSE     FALSE
-3128             TRUE             TRUE                      TRUE      TRUE
-3129             TRUE             TRUE                      TRUE      TRUE
-3130             TRUE             TRUE                     FALSE      TRUE
-3131             TRUE             TRUE                      TRUE      TRUE</code></pre>
+<code class="sourceCode R"><span><span class="va">reasons</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span><span class="op">)</span></span></code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb23">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in x[[sum.field]]: object of type 'closure' is not subsettable</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb45">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">reasons</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'reasons' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb47">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span> <span class="op">&lt;-</span> <span class="va">reasons</span><span class="op">$</span><span class="va">discard</span></span></code></pre>
 </div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'reasons' not found</code></pre>
+</div>
 <div id="challenge2" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
@@ -787,7 +837,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
 <p>Maybe our sample preparation was poor and we want the QC to be more
-strict. How could we change the set the QC filtering to use 4 MADs as
+strict. How could we change the set the QC filtering to use 2.5 MADs as
 the threshold for outlier calling?</p>
 </div>
 </div>
@@ -797,14 +847,19 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
-<p>You set <code>nmads = 4</code> like so:</p>
-<div class="codewrapper sourceCode" id="cb24">
+<p>You set <code>nmads = 2.5</code> like so:</p>
+<div class="codewrapper sourceCode" id="cb49">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">reasons_strict</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, nmads <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">reasons_strict</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, nmads <span class="op">=</span> <span class="fl">2.5</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in x[[sum.field]]: object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>You would then need to reassign the <code>discard</code> column as
 well, but we’ll stick with the 3 MADs default for now.
@@ -822,69 +877,87 @@ <h3 id="diagnostic-plots">Diagnostic plots<a class="anchor" aria-label="anchor"
 outliers and with a marked difference from “regular” cells (e.g., a
 bimodal distribution or a long tail). Moreover, if there are too many
 discarded cells, further exploration might be needed.</p>
-<div class="codewrapper sourceCode" id="cb25">
+<div class="codewrapper sourceCode" id="cb51">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Total count"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb26">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb53">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"detected"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Detected features"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb27">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb55">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Mito percent"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-3.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>While the univariate distribution of QC metrics can give some insight
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>While the univariate distribution of QC metrics can give some insight
 on the quality of the sample, often looking at the bivariate
 distribution of QC metrics is useful, e.g., to confirm that there are no
 cells with both large total counts and large mitochondrial counts, to
 ensure that we are not inadvertently removing high-quality cells that
 happen to be highly metabolically active.</p>
-<div class="codewrapper sourceCode" id="cb28">
+<div class="codewrapper sourceCode" id="cb57">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>,  x <span class="op">=</span><span class="st">"sum"</span>, y <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It could also be a good idea to perform a differential expression
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>It could also be a good idea to perform a differential expression
 analysis between retained and discarded cells to check wether we are
 removing an unusual cell population rather than low-quality libraries
 (see <a href="https://bioconductor.org/books/release/OSCA.advanced/quality-control-redux.html#qc-discard-cell-types" class="external-link">Section
 1.5 of OSCA advanced</a>).</p>
 <p>Once we are happy with the results, we can discard the low-quality
 cells by subsetting the original object.</p>
-<div class="codewrapper sourceCode" id="cb29">
+<div class="codewrapper sourceCode" id="cb59">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="op">!</span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span><span class="op">]</span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="op">!</span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb61">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(7): sum detected ... total discard
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 </div>
 </section><section><h2 class="section-heading" id="normalization">Normalization<a class="anchor" aria-label="anchor" href="#normalization"></a></h2>
@@ -917,31 +990,53 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 defined such that the mean size factor across all cells is equal to 1.
 This ensures that the normalized expression values are on the same scale
 as the original counts.</p>
-<div class="codewrapper sourceCode" id="cb31">
+<div class="codewrapper sourceCode" id="cb63">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">lib.sf</span> <span class="op">&lt;-</span> <span class="fu">librarySizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">lib.sf</span> <span class="op">&lt;-</span> <span class="fu">librarySizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.2730  0.7879  0.9600  1.0000  1.1730  2.5598 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'librarySizeFactors': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb33">
+<div class="codewrapper sourceCode" id="cb65">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>size_factor <span class="op">=</span> <span class="va">lib.sf</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'lib.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb67">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>size_factor <span class="op">=</span> <span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'lib.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb69">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_histogram</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-14-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="section level3">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<div class="section level3">
 <h3 id="normalization-by-deconvolution">Normalization by deconvolution<a class="anchor" aria-label="anchor" href="#normalization-by-deconvolution"></a></h3>
 <p>Library size normalization is not optimal, as it assumes that the
 total sum of UMI counts differ between cells only for technical and not
@@ -963,83 +1058,127 @@ <h3 id="normalization-by-deconvolution">Normalization by deconvolution<a class="
 <p>Note that while we focus on normalization by deconvolution here, many
 other methods have been proposed and lead to similar performance (see <a href="reference.html#litref">Borella 2022</a> for a comparative
 review).</p>
-<div class="codewrapper sourceCode" id="cb34">
+<div class="codewrapper sourceCode" id="cb71">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="fu">quickCluster</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> </span>
-<span></span>
-<span><span class="fu">table</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span></span></code></pre>
+<span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="fu">quickCluster</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> </span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>clust
-  1   2   3   4   5   6   7   8   9  10  11  12  13
-273 159 250 122 187 201 154 252 152 169 199 215 104 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'quickCluster': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb36">
+<div class="codewrapper sourceCode" id="cb73">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deconv.sf</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span>, cluster <span class="op">=</span> <span class="va">clust</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.3100  0.8028  0.9626  1.0000  1.1736  2.7858 </code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb38">
+<div class="codewrapper sourceCode" id="cb75">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span>
-<span></span>
-<span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span>, <span class="va">deconv_sf</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="va">deconv.sf</span> <span class="op">&lt;-</span> <span class="fu">pooledSizeFactors</span><span class="op">(</span><span class="va">sce</span>, cluster <span class="op">=</span> <span class="va">clust</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pooledSizeFactors': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb77">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb79">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb81">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb83">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span>, <span class="va">deconv_sf</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_abline</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">clust</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-16-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>Once we have computed the size factors, we compute the normalized
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<p>Once we have computed the size factors, we compute the normalized
 expression values for each cell by dividing the count for each gene with
 the appropriate size factor for that cell. Since we are typically going
 to work with log-transformed counts, the function
 <code>logNormCounts</code> also log-transforms the normalized values,
 creating a new assay called <code>logcounts</code>.</p>
-<div class="codewrapper sourceCode" id="cb39">
+<div class="codewrapper sourceCode" id="cb85">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span>
-<span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">logNormCounts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb87">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">logNormCounts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'logNormCounts': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb89">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <div id="challenge3" class="callout challenge">
 <div class="callout-square">
@@ -1050,16 +1189,16 @@ <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
 <p>Fill in the blanks for normalization that uses simpler library size
 factors instead of deconvolution.</p>
-<div class="codewrapper sourceCode" id="cb41">
+<div class="codewrapper sourceCode" id="cb91">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
-<span id="cb41-2"><a href="#cb41-2" tabindex="-1"></a></span>
-<span id="cb41-3"><a href="#cb41-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
-<span id="cb41-4"><a href="#cb41-4" tabindex="-1"></a></span>
-<span id="cb41-5"><a href="#cb41-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
-<span id="cb41-6"><a href="#cb41-6" tabindex="-1"></a></span>
-<span id="cb41-7"><a href="#cb41-7" tabindex="-1"></a>sce</span></code></pre>
+<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb91-1"><a href="#cb91-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
+<span id="cb91-2"><a href="#cb91-2" tabindex="-1"></a></span>
+<span id="cb91-3"><a href="#cb91-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
+<span id="cb91-4"><a href="#cb91-4" tabindex="-1"></a></span>
+<span id="cb91-5"><a href="#cb91-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
+<span id="cb91-6"><a href="#cb91-6" tabindex="-1"></a></span>
+<span id="cb91-7"><a href="#cb91-7" tabindex="-1"></a>sce</span></code></pre>
 </div>
 </div>
 </div>
@@ -1069,9 +1208,9 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb42">
+<div class="codewrapper sourceCode" id="cb92">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1115,25 +1254,57 @@ <h3 id="quantifying-per-gene-variation">Quantifying per-gene variation<a class="
 its abundance than its underlying biological heterogeneity. To account
 for this, the <code>modelGeneVar</code> function fits a trend to the
 variance with respect to abundance across all genes.</p>
-<div class="codewrapper sourceCode" id="cb43">
+<div class="codewrapper sourceCode" id="cb93">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dec.sce</span> <span class="op">&lt;-</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">fit.sce</span> <span class="op">&lt;-</span> <span class="fu">metadata</span><span class="op">(</span><span class="va">dec.sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">mean_var_df</span> <span class="op">=</span> <span class="fu">data.frame</span><span class="op">(</span>mean <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>                         var  <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">var</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">mean_var_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">mean</span>, <span class="va">var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="va">dec.sce</span> <span class="op">&lt;-</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'modelGeneVar': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb95">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">fit.sce</span> <span class="op">&lt;-</span> <span class="fu">metadata</span><span class="op">(</span><span class="va">dec.sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'metadata': object 'dec.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb97">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">mean_var_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>mean <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">mean</span>,</span>
+<span>                          var  <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">var</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'fit.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb99">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">mean_var_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">mean</span>, <span class="va">var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_function</span><span class="op">(</span>fun <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">trend</span>, </span>
 <span>                  color <span class="op">=</span> <span class="st">"dodgerblue"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>x <span class="op">=</span> <span class="st">"Mean of log-expression"</span>,</span>
 <span>         y <span class="op">=</span> <span class="st">"Variance of log-expression"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The blue line represents the uninteresting “technical” variance for
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'mean_var_df' not found</code></pre>
+</div>
+<p>The blue line represents the uninteresting “technical” variance for
 any given gene abundance. The genes with a lot of additional variance
 exhibit interesting “biological” variation.</p>
 </div>
@@ -1149,19 +1320,27 @@ <h3 id="selecting-highly-variable-genes">Selecting highly variable genes<a class
 differentially expressed across our cell populations and hence select
 10% of genes as highly variable (e.g., by setting
 <code>prop = 0.1</code>).</p>
-<div class="codewrapper sourceCode" id="cb44">
+<div class="codewrapper sourceCode" id="cb101">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">hvg.sce.var</span> <span class="op">&lt;-</span> <span class="fu">getTopHVGs</span><span class="op">(</span><span class="va">dec.sce</span>, n <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">head</span><span class="op">(</span><span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">hvg.sce.var</span> <span class="op">&lt;-</span> <span class="fu">getTopHVGs</span><span class="op">(</span><span class="va">dec.sce</span>, n <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dec.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb103">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">head</span><span class="op">(</span><span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>[1] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919"
-[4] "ENSMUSG00000052187" "ENSMUSG00000048583" "ENSMUSG00000051855"</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'hvg.sce.var' not found</code></pre>
 </div>
 <div id="challenge4" class="callout challenge">
 <div class="callout-square">
@@ -1181,7 +1360,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p><code>modelGeneVar()</code> can take a <code>block</code>
 argument.</p>
@@ -1194,11 +1373,11 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <p>Use the <code>block</code> argument in the call to
 <code>modelGeneVar()</code> like so:</p>
-<div class="codewrapper sourceCode" id="cb46">
+<div class="codewrapper sourceCode" id="cb105">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1238,7 +1417,7 @@ <h3 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a c
 replacing the original variables (genes) by fewer linear combinations of
 these variables, that are orthogonal and have successively maximal
 variance. Such linear combinations seek to “separate out” the
-observations (cells), while loosing as little information as
+observations (cells), while losing as little information as
 possible.</p>
 <p>Without getting into the technical details, one nice feature of PCA
 is that the principal components (PCs) are ordered by how much variance
@@ -1251,70 +1430,91 @@ <h3 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a c
 <p>One simple way to maximize our chance of capturing biological
 variation is by computing the PCs starting from the highly variable
 genes identified before.</p>
-<div class="codewrapper sourceCode" id="cb47">
+<div class="codewrapper sourceCode" id="cb106">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runPCA</span><span class="op">(</span><span class="va">sce</span>, subset_row <span class="op">=</span> <span class="va">hvg.sce.var</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runPCA</span><span class="op">(</span><span class="va">sce</span>, subset_row <span class="op">=</span> <span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'runPCA': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb108">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(1): PCA
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>By default, <code>runPCA</code> computes the first 50 principal
 components. We can check how much original variability they explain.
 These values are stored in the attributes of the <code>percentVar</code>
 reducedDim:</p>
-<div class="codewrapper sourceCode" id="cb49">
+<div class="codewrapper sourceCode" id="cb110">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pct_var_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>PC <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">50</span>,</span>
-<span>                         pct_var <span class="op">=</span> <span class="fu">attr</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="st">"percentVar"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">pct_var_df</span>,</span>
+<span>                         pct_var <span class="op">=</span> <span class="fu">attr</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="st">"percentVar"</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb112">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">pct_var_df</span>,</span>
 <span>       <span class="fu">aes</span><span class="op">(</span><span class="va">PC</span>, <span class="va">pct_var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Variance explained (%)"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>You can see the first two PCs capture the largest amount of
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'pct_var_df' not found</code></pre>
+</div>
+<p>You can see the first two PCs capture the largest amount of
 variation, but in this case you have to take the first 8 PCs before
 you’ve captured 50% of the total.</p>
 <p>And we can of course visualize the first 2-3 components, perhaps
 color-coding each point by an interesting feature, in this case the
 total number of UMIs per cell.</p>
-<div class="codewrapper sourceCode" id="cb50">
+<div class="codewrapper sourceCode" id="cb114">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotPCA</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"sum"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-25-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It can be helpful to compare pairs of PCs. This can be done with the
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'plotPCA': object 'sce' not found</code></pre>
+</div>
+<p>It can be helpful to compare pairs of PCs. This can be done with the
 <code>ncomponents</code> argument to <code>plotReducedDim()</code>. For
 example if one batch or cell type splits off on a particular PC, this
 can help visualize the effect of that.</p>
-<div class="codewrapper sourceCode" id="cb51">
+<div class="codewrapper sourceCode" id="cb116">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotReducedDim</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span>, ncomponents <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-26-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure></div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+</div>
 <div class="section level3">
 <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anchor" href="#non-linear-methods"></a></h3>
 <p>While PCA is a simple and effective way to visualize (and interpret!)
@@ -1324,27 +1524,55 @@ <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anch
 <p>These methods attempt to find a low-dimensional representation of the
 data that attempt to preserve pair-wise distance and structure in
 high-dimensional gene space as best as possible.</p>
-<div class="codewrapper sourceCode" id="cb52">
+<div class="codewrapper sourceCode" id="cb118">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runTSNE</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runTSNE</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb120">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-27-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb53">
+<div class="codewrapper sourceCode" id="cb122">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">111</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runUMAP</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runUMAP</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
+<div class="codewrapper sourceCode" id="cb124">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
 relative sizes and positions of the visual clusters may be misleading,
 as they tend to inflate dense clusters and compress sparse ones, such
 that we cannot use the size as a measure of subpopulation
@@ -1358,28 +1586,16 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 and replotting without the need for recomputing. Note the added
 <code>reducedDimNames</code> row when printing <code>sce</code>
 here:</p>
-<div class="codewrapper sourceCode" id="cb54">
+<div class="codewrapper sourceCode" id="cb126">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(3): PCA TSNE UMAP
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>Despite their shortcomings, t-SNE and UMAP can be useful
 visualization techniques. When using them, it is important to consider
@@ -1405,9 +1621,9 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb56">
+<div class="codewrapper sourceCode" id="cb128">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1471,78 +1687,139 @@ <h3 id="computing-doublet-densities">Computing doublet densities<a class="anchor
 very likely to be a doublet itself.</p>
 <p>This approach is implemented below using the <code>scDblFinder</code>
 library. We then visualize the scores in a t-SNE plot.</p>
-<div class="codewrapper sourceCode" id="cb57">
+<div class="codewrapper sourceCode" id="cb129">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">dbl.dens</span> <span class="op">&lt;-</span> <span class="fu">computeDoubletDensity</span><span class="op">(</span><span class="va">sce</span>, subset.row <span class="op">=</span> <span class="va">hvg.sce.var</span>,</span>
-<span>                                  d <span class="op">=</span> <span class="fu">ncol</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.dens</span><span class="op">)</span></span></code></pre>
+<span>                                  d <span class="op">=</span> <span class="fu">ncol</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
- 0.04874  0.28757  0.46790  0.65614  0.82371 14.88032 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'computeDoubletDensity': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb59">
+<div class="codewrapper sourceCode" id="cb131">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">DoubletScore</span> <span class="op">&lt;-</span> <span class="va">dbl.dens</span></span>
-<span></span>
-<span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.dens</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'dbl.dens' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb133">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">DoubletScore</span> <span class="op">&lt;-</span> <span class="va">dbl.dens</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.dens' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb135">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-31-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>We can explicitly convert this into doublet calls by identifying
+<p>We can explicitly convert this into doublet calls by identifying
 large outliers for the score within each sample. Here we use the
 “griffiths” method to do so.</p>
-<div class="codewrapper sourceCode" id="cb60">
+<div class="codewrapper sourceCode" id="cb137">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dbl.calls</span> <span class="op">&lt;-</span> <span class="fu">doubletThresholding</span><span class="op">(</span><span class="fu">data.frame</span><span class="op">(</span>score <span class="op">=</span> <span class="va">dbl.dens</span><span class="op">)</span>,</span>
 <span>                                 method <span class="op">=</span> <span class="st">"griffiths"</span>,</span>
-<span>                                 returnType <span class="op">=</span> <span class="st">"call"</span><span class="op">)</span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.calls</span><span class="op">)</span></span></code></pre>
+<span>                                 returnType <span class="op">=</span> <span class="st">"call"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>singlet doublet
-   2124     313 </code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.dens' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb62">
+<div class="codewrapper sourceCode" id="cb139">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">doublet</span> <span class="op">&lt;-</span> <span class="va">dbl.calls</span></span>
-<span></span>
-<span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"DoubletScore"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.calls</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'dbl.calls' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb141">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">doublet</span> <span class="op">&lt;-</span> <span class="va">dbl.calls</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.calls' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb143">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"DoubletScore"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-32-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb63">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb145">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-32-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>One way to determine whether a cell is in a real transient state or
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<p>One way to determine whether a cell is in a real transient state or
 it is a doublet is to check the number of detected genes and total UMI
 counts.</p>
-<div class="codewrapper sourceCode" id="cb64">
+<div class="codewrapper sourceCode" id="cb147">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, <span class="st">"detected"</span>, <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-33-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb65">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb149">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, <span class="st">"detected"</span>, <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-33-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>In this case, we only have a few doublets at the periphery of some
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>In this case, we only have a few doublets at the periphery of some
 clusters. It could be fine to keep the doublets in the analysis, but it
 may be safer to discard them to avoid biases in downstream analysis
 (e.g., differential expression).</p>
@@ -1569,38 +1846,69 @@ <h3 class="callout-title">Exercise 1: Normalization</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb66">
+<div class="codewrapper sourceCode" id="cb151">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deconv.sf2</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="co"># dropped `cluster = clust` here</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf2</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">deconv.sf2</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="co"># dropped `cluster = clust` here</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.2985  0.8142  0.9583  1.0000  1.1582  2.7054 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pooledSizeFactors': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb68">
+<div class="codewrapper sourceCode" id="cb153">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf2</span> <span class="op">&lt;-</span> <span class="va">deconv.sf2</span></span>
-<span></span>
-<span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">deconv_sf</span>, <span class="va">deconv_sf2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf2</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'deconv.sf2' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb155">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf2</span> <span class="op">&lt;-</span> <span class="va">deconv.sf2</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf2' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb157">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb159">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">deconv_sf</span>, <span class="va">deconv_sf2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_abline</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">clust</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">facet_wrap</span><span class="op">(</span><span class="fu">vars</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-34-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>You can see that we get largely similar results, though for clusters
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<p>You can see that we get largely similar results, though for clusters
 3 and 9 there’s a slight deviation from the <code>y=x</code>
 relationship because these clusters (which are fairly distinct from the
 other clusters) are now being pooled with cells from other clusters.
@@ -1628,12 +1936,12 @@ <h3 class="callout-title">Exercise 2: PBMC Data</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
   <h4 class="accordion-header" id="headingSolution7"> Show me the solution </h4>
 </button>
-<div id="collapseSolution7" class="accordion-collapse collapse" aria-labelledby="headingSolution7" data-bs-parent="#accordionSolution7">
+<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
 <div class="accordion-body">
 <p>The first few lines here read the data from ExperimentHub and the
 mitochondrial genes are identified by gene symbols in the row data.
 Otherwise the steps are the same:</p>
-<div class="codewrapper sourceCode" id="cb69">
+<div class="codewrapper sourceCode" id="cb161">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1736,7 +2044,7 @@ <h3 class="callout-title">Extension challenge 1: Spike-ins</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
   <h4 class="accordion-header" id="headingSolution8"> Show me the solution </h4>
 </button>
-<div id="collapseSolution8" class="accordion-collapse collapse" aria-labelledby="headingSolution8" data-bs-parent="#accordionSolution8">
+<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
 <div class="accordion-body">
 <p>Spike-ins are deliberately-introduced exogeneous RNA from an exotic
 or synthetic source at a known concentration. This provides a known
@@ -1780,7 +2088,7 @@ <h3 class="callout-title">Extension challenge 3: Reduced dimensionality represen
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
   <h4 class="accordion-header" id="headingSolution9"> Show me the solution </h4>
 </button>
-<div id="collapseSolution9" class="accordion-collapse collapse" aria-labelledby="headingSolution9" data-bs-parent="#accordionSolution9">
+<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
 <div class="accordion-body">
 <p>Mathematically, this would require the data to fall on a
 two-dimensional plane (for linear methods like PCA) or a smooth 2D
@@ -1918,8 +2226,8 @@ <h3 class="callout-title">Further Reading</h3>
   "url": "https://ccb-hms.github.io/osca-carpentries/eda_qc.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/eda_qc.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-03",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/hca.html b/hca.html
index 09667c1..1a58515 100644
--- a/hca.html
+++ b/hca.html
@@ -758,7 +758,7 @@ <h3 class="callout-title">Exercise 1: Basic counting + piping</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb23">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -809,7 +809,7 @@ <h3 class="callout-title">Exercise 2: Tissue &amp; type counting</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -850,7 +850,7 @@ <h3 class="callout-title">Exercise 3: Comparing metadata categories</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb27">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -942,7 +942,7 @@ <h3 class="callout-title">Exercise 4: Highly specific cell groups</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb31">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1064,7 +1064,7 @@ <h3 class="callout-title">Key Points</h3>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/hca.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-04",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/images.html b/images.html
index ac8a7a4..2366057 100644
--- a/images.html
+++ b/images.html
@@ -374,45 +374,7 @@ <h3 id="intro-sce-figure-2">Figure 2</h3>
 <figure><img src="fig/intro-sce-rendered-unnamed-chunk-14-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr></section><section id="eda_qc"><h2 class="section-heading"><a href="eda_qc.html">Exploratory data analysis and quality control</a></h2>
 <hr class="half-width">
 <h3 id="eda_qc-figure-1">Figure 1</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-2-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-2">Figure 2</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-3">Figure 3</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-4">Figure 4</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-11-3.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-5">Figure 5</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-6">Figure 6</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-14-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-7">Figure 7</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-16-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-8">Figure 8</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-9">Figure 9</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-10">Figure 10</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-25-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-11">Figure 11</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-26-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-12">Figure 12</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-27-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-13">Figure 13</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-14">Figure 14</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-30-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-15">Figure 15</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-31-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-16">Figure 16</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-32-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-17">Figure 17</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-32-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-18">Figure 18</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-33-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-19">Figure 19</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-33-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-20">Figure 20</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-34-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr></section><section id="cell_type_annotation"><h2 class="section-heading"><a href="cell_type_annotation.html">Cell type annotation</a></h2>
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-30-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr></section><section id="cell_type_annotation"><h2 class="section-heading"><a href="cell_type_annotation.html">Cell type annotation</a></h2>
 <hr class="half-width">
 <h3 id="cell_type_annotation-figure-1">Figure 1</h3>
 <figure><img src="fig/cell_type_annotation-rendered-cluster-viz-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
@@ -510,8 +472,8 @@ <h3 id="hca-figure-1">Figure 1</h3>
   "url": "https://ccb-hms.github.io/osca-carpentries/images.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/images.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/index.html b/index.html
index efe1a0c..cbbbfed 100644
--- a/index.html
+++ b/index.html
@@ -448,8 +448,8 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
   "url": "https://ccb-hms.github.io/osca-carpentries/index.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/index.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor-notes.html b/instructor-notes.html
index 482fe25..c30102c 100644
--- a/instructor-notes.html
+++ b/instructor-notes.html
@@ -433,8 +433,8 @@ <h1>Instructor Notes</h1>
   "url": "https://ccb-hms.github.io/osca-carpentries/instructor-notes.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor-notes.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/404.html b/instructor/404.html
index 5823e89..3606a24 100644
--- a/instructor/404.html
+++ b/instructor/404.html
@@ -376,8 +376,8 @@ <h1>Page not found</h1>
   "url": "https://ccb-hms.github.io/osca-carpentries/instructor/404.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/404.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/CODE_OF_CONDUCT.html b/instructor/CODE_OF_CONDUCT.html
index 61f200a..a9a43ef 100644
--- a/instructor/CODE_OF_CONDUCT.html
+++ b/instructor/CODE_OF_CONDUCT.html
@@ -401,7 +401,7 @@ <h1>Contributor Code of Conduct</h1>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/CODE_OF_CONDUCT.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-09-24",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/LICENSE.html b/instructor/LICENSE.html
index 5b450e6..dc37a13 100644
--- a/instructor/LICENSE.html
+++ b/instructor/LICENSE.html
@@ -449,7 +449,7 @@ <h1>Licenses</h1>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/LICENSE.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-09-24",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/aio.html b/instructor/aio.html
index 3d4f0db..58fed5f 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -1006,7 +1006,7 @@ <h3 class="callout-title">Key Points</h3>
 any links that you are not going to use.
  --></section></section><section id="aio-eda_qc"><p>Content from <a href="eda_qc.html">Exploratory data analysis and quality control</a></p>
 <hr>
-<p>Last updated on 2024-10-03 |
+<p>Last updated on 2024-10-10 |
 
         <a href="https://github.com/ccb-hms/osca-carpentries/edit/main/episodes/eda_qc.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
 <p>Estimated time: <i aria-hidden="true" data-feather="clock"></i> 45 minutes</p>
@@ -1077,28 +1077,37 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span><span class="kw">library</span><span class="op">(</span><span class="va">scran</span><span class="op">)</span></span>
 <span><span class="kw">library</span><span class="op">(</span><span class="va">scDblFinder</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">WTChimeraData</span><span class="op">(</span>samples <span class="op">=</span> <span class="fl">5</span>, type <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">WTChimeraData</span><span class="op">(</span>samples <span class="op">=</span> <span class="fl">5</span>, type <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 522554
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(522554): AAACCTGAGAAACCAT AAACCTGAGAAACCGC ...
-  TTTGTCATCTTTACGT TTTGTCATCTTTCCTC
-colData names(0):
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error: failed to load resource
+  name: EH3019
+  title: WT chimera raw counts (sample 5)
+  reason: 1 resources failed to download</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb3">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb5">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>This is the same data we examined in the previous lesson.</p>
 </section><section><h2 class="section-heading" id="droplet-processing">Droplet processing<a class="anchor" aria-label="anchor" href="#droplet-processing"></a>
@@ -1108,36 +1117,6 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 while we can see that we have data for more than 500,000 droplets. It is
 likely that most of these droplets are empty and are capturing only
 ambient or background RNA.</p>
-<div class="codewrapper sourceCode" id="cb3">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">bcrank</span> <span class="op">&lt;-</span> <span class="fu">barcodeRanks</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Only showing unique points for plotting speed.</span></span>
-<span><span class="va">uniq</span> <span class="op">&lt;-</span> <span class="op">!</span><span class="fu">duplicated</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">rank</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">line_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>cutoff <span class="op">=</span> <span class="fu">names</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>                      value  <span class="op">=</span> <span class="fu">unlist</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">bcrank</span><span class="op">[</span><span class="va">uniq</span>,<span class="op">]</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">rank</span>, <span class="va">total</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">geom_hline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">line_df</span>,</span>
-<span>               <span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">cutoff</span>,</span>
-<span>                   yintercept <span class="op">=</span> <span class="va">value</span><span class="op">)</span>,</span>
-<span>               lty <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Total UMI count"</span><span class="op">)</span></span></code></pre>
-</div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-2-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The distribution of total counts (called the unique molecular
-identifier or UMI count) exhibits a sharp transition between barcodes
-with large and small total counts, probably corresponding to
-cell-containing and empty droplets respectively.</p>
-<p>A simple approach would be to apply a threshold on the total count to
-only retain those barcodes with large totals. However, this may
-unnecessarily discard libraries derived from cell types with low RNA
-content.</p>
 <div id="callout1" class="callout">
 <div class="callout-square">
 <i class="callout-icon" data-feather="bell"></i>
@@ -1159,6 +1138,71 @@ <h3 class="callout-title">Callout</h3>
 </div>
 </div>
 </div>
+<p>We can visualize barcode read totals to visualize the distinction
+between empty droplets and properly profiled single cells in a so-called
+“knee plot”:</p>
+<div class="codewrapper sourceCode" id="cb7">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">bcrank</span> <span class="op">&lt;-</span> <span class="fu">barcodeRanks</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'm' in selecting a method for function 'barcodeRanks': error in evaluating the argument 'object' in selecting a method for function 'counts': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb9">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Only showing unique points for plotting speed.</span></span>
+<span><span class="va">uniq</span> <span class="op">&lt;-</span> <span class="op">!</span><span class="fu">duplicated</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">rank</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'duplicated': object 'bcrank' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb11">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">line_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>cutoff <span class="op">=</span> <span class="fu">names</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>                      value  <span class="op">=</span> <span class="fu">unlist</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'metadata': object 'bcrank' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb13">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">bcrank</span><span class="op">[</span><span class="va">uniq</span>,<span class="op">]</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">rank</span>, <span class="va">total</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">geom_hline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">line_df</span>,</span>
+<span>               <span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">cutoff</span>,</span>
+<span>                   yintercept <span class="op">=</span> <span class="va">value</span><span class="op">)</span>,</span>
+<span>               lty <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Total UMI count"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'bcrank' not found</code></pre>
+</div>
+<p>The distribution of total counts (called the unique molecular
+identifier or UMI count) exhibits a sharp transition between barcodes
+with large and small total counts, probably corresponding to
+cell-containing and empty droplets respectively.</p>
+<p>A simple approach would be to apply a threshold on the total count to
+only retain those barcodes with large totals. However, this may
+unnecessarily discard libraries derived from cell types with low RNA
+content.</p>
 <div id="challenge1" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
@@ -1175,18 +1219,18 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb4">
+<div class="codewrapper sourceCode" id="cb15">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">median</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">total</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>[1] 2</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'median': object 'bcrank' not found</code></pre>
 </div>
 <p>Just 2! Clearly many barcodes produce practically no output.</p>
 <!-- This is a direct application challenge of of  "It is likely that most of these droplets are empty and are capturing only ambient or background RNA" and a synthesis challenge of the previous episode where they learned to access columns of DataFrames. -->
@@ -1206,7 +1250,7 @@ <h3 id="testing-for-empty-droplets">Testing for empty droplets<a class="anchor"
 <p>We call cells at a false discovery rate (FDR) of 0.1%, meaning that
 no more than 0.1% of our called barcodes should be empty droplets on
 average.</p>
-<div class="codewrapper sourceCode" id="cb6">
+<div class="codewrapper sourceCode" id="cb17">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1215,40 +1259,45 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># this may take a few minutes</span></span>
-<span><span class="va">e.out</span> <span class="op">&lt;-</span> <span class="fu">emptyDrops</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span></span></code></pre>
+<span><span class="va">e.out</span> <span class="op">&lt;-</span> <span class="fu">emptyDrops</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Mode   FALSE    TRUE    NA's
-logical    6184    3131  513239 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'm' in selecting a method for function 'emptyDrops': error in evaluating the argument 'object' in selecting a method for function 'counts': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb8">
+<div class="codewrapper sourceCode" id="cb19">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="fu">which</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 3131
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(3131): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGTCAGTCTGATTG
-  TTTGTCATCTGAGTGT
-colData names(0):
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'e.out' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb21">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="fu">which</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb23">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>The result confirms our expectation: only 3,131 droplets contain a
 cell, while the large majority of droplets are empty.</p>
@@ -1257,7 +1306,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler1" aria-expanded="false" aria-controls="collapseSpoiler1">
   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> Setting the Random Seed </h3>
 </button>
-<div id="collapseSpoiler1" class="accordion-collapse collapse" aria-labelledby="headingSpoiler1" data-bs-parent="#accordionSpoiler1">
+<div id="collapseSpoiler1" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler1" aria-labelledby="headingSpoiler1">
 <div class="accordion-body">
 <p>Whenever your code involves the generation of random numbers, it’s a
 good practice to set the random seed in R with
@@ -1317,115 +1366,115 @@ <h3 id="choice-of-quality-control-metrics">Choice of quality control metrics<a c
 <code>EnsDb</code> mouse package available in Bioconductor, but a more
 updated version of Ensembl can be used through the
 <code>AnnotationHub</code> or <code>biomaRt</code> packages.</p>
-<div class="codewrapper sourceCode" id="cb10">
+<div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">chr.loc</span> <span class="op">&lt;-</span> <span class="fu">mapIds</span><span class="op">(</span><span class="va">EnsDb.Mmusculus.v79</span>,</span>
 <span>                  keys    <span class="op">=</span> <span class="fu">rownames</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>,</span>
 <span>                  keytype <span class="op">=</span> <span class="st">"GENEID"</span>, </span>
-<span>                  column  <span class="op">=</span> <span class="st">"SEQNAME"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">is.mito</span> <span class="op">&lt;-</span> <span class="fu">which</span><span class="op">(</span><span class="va">chr.loc</span> <span class="op">==</span> <span class="st">"MT"</span><span class="op">)</span></span></code></pre>
+<span>                  column  <span class="op">=</span> <span class="st">"SEQNAME"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb27">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">is.mito</span> <span class="op">&lt;-</span> <span class="fu">which</span><span class="op">(</span><span class="va">chr.loc</span> <span class="op">==</span> <span class="st">"MT"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'which': object 'chr.loc' not found</code></pre>
 </div>
 <p>We can use the <code>scuttle</code> package to compute a set of
 quality control metrics, specifying that we want to use the
 mitochondrial genes as a special set of features.</p>
-<div class="codewrapper sourceCode" id="cb11">
+<div class="codewrapper sourceCode" id="cb29">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu">perCellQCMetrics</span><span class="op">(</span><span class="va">sce</span>, subsets <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>Mito <span class="op">=</span> <span class="va">is.mito</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">cbind</span><span class="op">(</span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="va">df</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu">perCellQCMetrics</span><span class="op">(</span><span class="va">sce</span>, subsets <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>Mito <span class="op">=</span> <span class="va">is.mito</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'perCellQCMetrics': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb31">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">cbind</span><span class="op">(</span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="va">df</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb33">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>DataFrame with 3131 rows and 6 columns
-                       sum  detected subsets_Mito_sum subsets_Mito_detected
-                 &lt;numeric&gt; &lt;integer&gt;        &lt;numeric&gt;             &lt;integer&gt;
-AAACCTGAGACTGTAA     27577      5418              471                    10
-AAACCTGAGATGCCTT     29309      5405              679                    10
-AAACCTGAGCAGCCTC     28795      5218              480                    12
-AAACCTGCATACTCTT     34794      4781              496                    12
-AAACCTGGTGGTACAG       262       229                0                     0
-...                    ...       ...              ...                   ...
-TTTGGTTTCGCCATAA     38398      6020              252                    12
-TTTGTCACACCCTATC      3013      1451              123                     9
-TTTGTCACATTCTCAT      1472       675              599                    11
-TTTGTCAGTCTGATTG       361       293                0                     0
-TTTGTCATCTGAGTGT       267       233               16                     6
-                 subsets_Mito_percent     total
-                            &lt;numeric&gt; &lt;numeric&gt;
-AAACCTGAGACTGTAA              1.70795     27577
-AAACCTGAGATGCCTT              2.31669     29309
-AAACCTGAGCAGCCTC              1.66696     28795
-AAACCTGCATACTCTT              1.42553     34794
-AAACCTGGTGGTACAG              0.00000       262
-...                               ...       ...
-TTTGGTTTCGCCATAA             0.656284     38398
-TTTGTCACACCCTATC             4.082310      3013
-TTTGTCACATTCTCAT            40.692935      1472
-TTTGTCAGTCTGATTG             0.000000       361
-TTTGTCATCTGAGTGT             5.992509       267</code></pre>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'sce' not found</code></pre>
 </div>
 <p>Now that we have computed the metrics, we have to decide on
 thresholds to define high- and low-quality samples. We could check how
 many cells are above/below a certain fixed threshold. For instance,</p>
-<div class="codewrapper sourceCode" id="cb13">
+<div class="codewrapper sourceCode" id="cb35">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">sum</span> <span class="op">&lt;</span> <span class="fl">10000</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>
-FALSE  TRUE
- 2477   654 </code></pre>
+<pre class="error" tabindex="0"><code>Error in df$sum: object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb15">
+<div class="codewrapper sourceCode" id="cb37">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">subsets_Mito_percent</span> <span class="op">&gt;</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>
-FALSE  TRUE
- 2761   370 </code></pre>
+<pre class="error" tabindex="0"><code>Error in df$subsets_Mito_percent: object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>or we could look at the distribution of such metrics and use a data
 adaptive threshold.</p>
-<div class="codewrapper sourceCode" id="cb17">
+<div class="codewrapper sourceCode" id="cb39">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">detected</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-     98    4126    5168    4455    5670    7908 </code></pre>
+<pre class="error" tabindex="0"><code>Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'summary': object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb19">
+<div class="codewrapper sourceCode" id="cb41">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">subsets_Mito_percent</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-  0.000   1.155   1.608   5.079   2.182  66.968 </code></pre>
+<pre class="error" tabindex="0"><code>Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'summary': object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>We can use the <code>perCellQCFilters</code> function to apply a set
 of common adaptive filters to identify low-quality cells. By default, we
@@ -1433,38 +1482,39 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 deviations (MADs) from the median in the “problematic” direction. This
 is loosely motivated by the fact that such a filter will retain 99% of
 non-outlier values that follow a normal distribution.</p>
-<div class="codewrapper sourceCode" id="cb21">
+<div class="codewrapper sourceCode" id="cb43">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">reasons</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">reasons</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">reasons</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>DataFrame with 3131 rows and 4 columns
-         low_lib_size   low_n_features high_subsets_Mito_percent   discard
-     &lt;outlier.filter&gt; &lt;outlier.filter&gt;          &lt;outlier.filter&gt; &lt;logical&gt;
-1               FALSE            FALSE                     FALSE     FALSE
-2               FALSE            FALSE                     FALSE     FALSE
-3               FALSE            FALSE                     FALSE     FALSE
-4               FALSE            FALSE                     FALSE     FALSE
-5                TRUE             TRUE                     FALSE      TRUE
-...               ...              ...                       ...       ...
-3127            FALSE            FALSE                     FALSE     FALSE
-3128             TRUE             TRUE                      TRUE      TRUE
-3129             TRUE             TRUE                      TRUE      TRUE
-3130             TRUE             TRUE                     FALSE      TRUE
-3131             TRUE             TRUE                      TRUE      TRUE</code></pre>
+<pre class="error" tabindex="0"><code>Error in x[[sum.field]]: object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb23">
+<div class="codewrapper sourceCode" id="cb45">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">reasons</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'reasons' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb47">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span> <span class="op">&lt;-</span> <span class="va">reasons</span><span class="op">$</span><span class="va">discard</span></span></code></pre>
 </div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'reasons' not found</code></pre>
+</div>
 <div id="challenge2" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
@@ -1473,7 +1523,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
 <p>Maybe our sample preparation was poor and we want the QC to be more
-strict. How could we change the set the QC filtering to use 4 MADs as
+strict. How could we change the set the QC filtering to use 2.5 MADs as
 the threshold for outlier calling?</p>
 </div>
 </div>
@@ -1483,14 +1533,19 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
-<p>You set <code>nmads = 4</code> like so:</p>
-<div class="codewrapper sourceCode" id="cb24">
+<p>You set <code>nmads = 2.5</code> like so:</p>
+<div class="codewrapper sourceCode" id="cb49">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">reasons_strict</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, nmads <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">reasons_strict</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, nmads <span class="op">=</span> <span class="fl">2.5</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in x[[sum.field]]: object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>You would then need to reassign the <code>discard</code> column as
 well, but we’ll stick with the 3 MADs default for now.
@@ -1509,69 +1564,87 @@ <h3 id="diagnostic-plots">Diagnostic plots<a class="anchor" aria-label="anchor"
 outliers and with a marked difference from “regular” cells (e.g., a
 bimodal distribution or a long tail). Moreover, if there are too many
 discarded cells, further exploration might be needed.</p>
-<div class="codewrapper sourceCode" id="cb25">
+<div class="codewrapper sourceCode" id="cb51">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Total count"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb26">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb53">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"detected"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Detected features"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb27">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb55">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Mito percent"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-3.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>While the univariate distribution of QC metrics can give some insight
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>While the univariate distribution of QC metrics can give some insight
 on the quality of the sample, often looking at the bivariate
 distribution of QC metrics is useful, e.g., to confirm that there are no
 cells with both large total counts and large mitochondrial counts, to
 ensure that we are not inadvertently removing high-quality cells that
 happen to be highly metabolically active.</p>
-<div class="codewrapper sourceCode" id="cb28">
+<div class="codewrapper sourceCode" id="cb57">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>,  x <span class="op">=</span><span class="st">"sum"</span>, y <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It could also be a good idea to perform a differential expression
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>It could also be a good idea to perform a differential expression
 analysis between retained and discarded cells to check wether we are
 removing an unusual cell population rather than low-quality libraries
 (see <a href="https://bioconductor.org/books/release/OSCA.advanced/quality-control-redux.html#qc-discard-cell-types" class="external-link">Section
 1.5 of OSCA advanced</a>).</p>
 <p>Once we are happy with the results, we can discard the low-quality
 cells by subsetting the original object.</p>
-<div class="codewrapper sourceCode" id="cb29">
+<div class="codewrapper sourceCode" id="cb59">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="op">!</span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span><span class="op">]</span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="op">!</span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span><span class="op">]</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(7): sum detected ... total discard
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb61">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 </div>
 </section><section><h2 class="section-heading" id="normalization">Normalization<a class="anchor" aria-label="anchor" href="#normalization"></a>
@@ -1606,31 +1679,53 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 defined such that the mean size factor across all cells is equal to 1.
 This ensures that the normalized expression values are on the same scale
 as the original counts.</p>
-<div class="codewrapper sourceCode" id="cb31">
+<div class="codewrapper sourceCode" id="cb63">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">lib.sf</span> <span class="op">&lt;-</span> <span class="fu">librarySizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">lib.sf</span> <span class="op">&lt;-</span> <span class="fu">librarySizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.2730  0.7879  0.9600  1.0000  1.1730  2.5598 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'librarySizeFactors': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb33">
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 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>size_factor <span class="op">=</span> <span class="va">lib.sf</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'lib.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb67">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>size_factor <span class="op">=</span> <span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'lib.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb69">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_histogram</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-14-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="section level3">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<div class="section level3">
 <h3 id="normalization-by-deconvolution">Normalization by deconvolution<a class="anchor" aria-label="anchor" href="#normalization-by-deconvolution"></a>
 </h3>
 <p>Library size normalization is not optimal, as it assumes that the
@@ -1653,83 +1748,127 @@ <h3 id="normalization-by-deconvolution">Normalization by deconvolution<a class="
 <p>Note that while we focus on normalization by deconvolution here, many
 other methods have been proposed and lead to similar performance (see <a href="reference.html#litref">Borella 2022</a> for a comparative
 review).</p>
-<div class="codewrapper sourceCode" id="cb34">
+<div class="codewrapper sourceCode" id="cb71">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="fu">quickCluster</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> </span>
-<span></span>
-<span><span class="fu">table</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span></span></code></pre>
+<span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="fu">quickCluster</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> </span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>clust
-  1   2   3   4   5   6   7   8   9  10  11  12  13
-273 159 250 122 187 201 154 252 152 169 199 215 104 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'quickCluster': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb36">
+<div class="codewrapper sourceCode" id="cb73">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb75">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">deconv.sf</span> <span class="op">&lt;-</span> <span class="fu">pooledSizeFactors</span><span class="op">(</span><span class="va">sce</span>, cluster <span class="op">=</span> <span class="va">clust</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pooledSizeFactors': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb77">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb79">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb81">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deconv.sf</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span>, cluster <span class="op">=</span> <span class="va">clust</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.3100  0.8028  0.9626  1.0000  1.1736  2.7858 </code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb38">
+<div class="codewrapper sourceCode" id="cb83">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span>
-<span></span>
-<span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span>, <span class="va">deconv_sf</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span>, <span class="va">deconv_sf</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_abline</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">clust</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-16-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>Once we have computed the size factors, we compute the normalized
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<p>Once we have computed the size factors, we compute the normalized
 expression values for each cell by dividing the count for each gene with
 the appropriate size factor for that cell. Since we are typically going
 to work with log-transformed counts, the function
 <code>logNormCounts</code> also log-transforms the normalized values,
 creating a new assay called <code>logcounts</code>.</p>
-<div class="codewrapper sourceCode" id="cb39">
+<div class="codewrapper sourceCode" id="cb85">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span>
-<span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">logNormCounts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb87">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">logNormCounts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'logNormCounts': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb89">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <div id="challenge3" class="callout challenge">
 <div class="callout-square">
@@ -1740,16 +1879,16 @@ <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
 <p>Fill in the blanks for normalization that uses simpler library size
 factors instead of deconvolution.</p>
-<div class="codewrapper sourceCode" id="cb41">
+<div class="codewrapper sourceCode" id="cb91">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
-<span id="cb41-2"><a href="#cb41-2" tabindex="-1"></a></span>
-<span id="cb41-3"><a href="#cb41-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
-<span id="cb41-4"><a href="#cb41-4" tabindex="-1"></a></span>
-<span id="cb41-5"><a href="#cb41-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
-<span id="cb41-6"><a href="#cb41-6" tabindex="-1"></a></span>
-<span id="cb41-7"><a href="#cb41-7" tabindex="-1"></a>sce</span></code></pre>
+<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb91-1"><a href="#cb91-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
+<span id="cb91-2"><a href="#cb91-2" tabindex="-1"></a></span>
+<span id="cb91-3"><a href="#cb91-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
+<span id="cb91-4"><a href="#cb91-4" tabindex="-1"></a></span>
+<span id="cb91-5"><a href="#cb91-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
+<span id="cb91-6"><a href="#cb91-6" tabindex="-1"></a></span>
+<span id="cb91-7"><a href="#cb91-7" tabindex="-1"></a>sce</span></code></pre>
 </div>
 </div>
 </div>
@@ -1759,9 +1898,9 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb42">
+<div class="codewrapper sourceCode" id="cb92">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1808,25 +1947,57 @@ <h3 id="quantifying-per-gene-variation">Quantifying per-gene variation<a class="
 its abundance than its underlying biological heterogeneity. To account
 for this, the <code>modelGeneVar</code> function fits a trend to the
 variance with respect to abundance across all genes.</p>
-<div class="codewrapper sourceCode" id="cb43">
+<div class="codewrapper sourceCode" id="cb93">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dec.sce</span> <span class="op">&lt;-</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">fit.sce</span> <span class="op">&lt;-</span> <span class="fu">metadata</span><span class="op">(</span><span class="va">dec.sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">mean_var_df</span> <span class="op">=</span> <span class="fu">data.frame</span><span class="op">(</span>mean <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>                         var  <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">var</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">mean_var_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">mean</span>, <span class="va">var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="va">dec.sce</span> <span class="op">&lt;-</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'modelGeneVar': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb95">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">fit.sce</span> <span class="op">&lt;-</span> <span class="fu">metadata</span><span class="op">(</span><span class="va">dec.sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'metadata': object 'dec.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb97">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">mean_var_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>mean <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">mean</span>,</span>
+<span>                          var  <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">var</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'fit.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb99">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">mean_var_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">mean</span>, <span class="va">var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_function</span><span class="op">(</span>fun <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">trend</span>, </span>
 <span>                  color <span class="op">=</span> <span class="st">"dodgerblue"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>x <span class="op">=</span> <span class="st">"Mean of log-expression"</span>,</span>
 <span>         y <span class="op">=</span> <span class="st">"Variance of log-expression"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The blue line represents the uninteresting “technical” variance for
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'mean_var_df' not found</code></pre>
+</div>
+<p>The blue line represents the uninteresting “technical” variance for
 any given gene abundance. The genes with a lot of additional variance
 exhibit interesting “biological” variation.</p>
 </div>
@@ -1843,19 +2014,27 @@ <h3 id="selecting-highly-variable-genes">Selecting highly variable genes<a class
 differentially expressed across our cell populations and hence select
 10% of genes as highly variable (e.g., by setting
 <code>prop = 0.1</code>).</p>
-<div class="codewrapper sourceCode" id="cb44">
+<div class="codewrapper sourceCode" id="cb101">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">hvg.sce.var</span> <span class="op">&lt;-</span> <span class="fu">getTopHVGs</span><span class="op">(</span><span class="va">dec.sce</span>, n <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">head</span><span class="op">(</span><span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">hvg.sce.var</span> <span class="op">&lt;-</span> <span class="fu">getTopHVGs</span><span class="op">(</span><span class="va">dec.sce</span>, n <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dec.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb103">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">head</span><span class="op">(</span><span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>[1] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919"
-[4] "ENSMUSG00000052187" "ENSMUSG00000048583" "ENSMUSG00000051855"</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'hvg.sce.var' not found</code></pre>
 </div>
 <div id="challenge4" class="callout challenge">
 <div class="callout-square">
@@ -1875,7 +2054,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p><code>modelGeneVar()</code> can take a <code>block</code>
 argument.</p>
@@ -1888,11 +2067,11 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <p>Use the <code>block</code> argument in the call to
 <code>modelGeneVar()</code> like so:</p>
-<div class="codewrapper sourceCode" id="cb46">
+<div class="codewrapper sourceCode" id="cb105">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1935,7 +2114,7 @@ <h3 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a c
 replacing the original variables (genes) by fewer linear combinations of
 these variables, that are orthogonal and have successively maximal
 variance. Such linear combinations seek to “separate out” the
-observations (cells), while loosing as little information as
+observations (cells), while losing as little information as
 possible.</p>
 <p>Without getting into the technical details, one nice feature of PCA
 is that the principal components (PCs) are ordered by how much variance
@@ -1948,70 +2127,90 @@ <h3 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a c
 <p>One simple way to maximize our chance of capturing biological
 variation is by computing the PCs starting from the highly variable
 genes identified before.</p>
-<div class="codewrapper sourceCode" id="cb47">
+<div class="codewrapper sourceCode" id="cb106">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runPCA</span><span class="op">(</span><span class="va">sce</span>, subset_row <span class="op">=</span> <span class="va">hvg.sce.var</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runPCA</span><span class="op">(</span><span class="va">sce</span>, subset_row <span class="op">=</span> <span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(1): PCA
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'runPCA': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb108">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>By default, <code>runPCA</code> computes the first 50 principal
 components. We can check how much original variability they explain.
 These values are stored in the attributes of the <code>percentVar</code>
 reducedDim:</p>
-<div class="codewrapper sourceCode" id="cb49">
+<div class="codewrapper sourceCode" id="cb110">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pct_var_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>PC <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">50</span>,</span>
-<span>                         pct_var <span class="op">=</span> <span class="fu">attr</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="st">"percentVar"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">pct_var_df</span>,</span>
+<span>                         pct_var <span class="op">=</span> <span class="fu">attr</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="st">"percentVar"</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb112">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">pct_var_df</span>,</span>
 <span>       <span class="fu">aes</span><span class="op">(</span><span class="va">PC</span>, <span class="va">pct_var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Variance explained (%)"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>You can see the first two PCs capture the largest amount of
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'pct_var_df' not found</code></pre>
+</div>
+<p>You can see the first two PCs capture the largest amount of
 variation, but in this case you have to take the first 8 PCs before
 you’ve captured 50% of the total.</p>
 <p>And we can of course visualize the first 2-3 components, perhaps
 color-coding each point by an interesting feature, in this case the
 total number of UMIs per cell.</p>
-<div class="codewrapper sourceCode" id="cb50">
+<div class="codewrapper sourceCode" id="cb114">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotPCA</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"sum"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-25-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It can be helpful to compare pairs of PCs. This can be done with the
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'plotPCA': object 'sce' not found</code></pre>
+</div>
+<p>It can be helpful to compare pairs of PCs. This can be done with the
 <code>ncomponents</code> argument to <code>plotReducedDim()</code>. For
 example if one batch or cell type splits off on a particular PC, this
 can help visualize the effect of that.</p>
-<div class="codewrapper sourceCode" id="cb51">
+<div class="codewrapper sourceCode" id="cb116">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotReducedDim</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span>, ncomponents <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-26-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
 </div>
 <div class="section level3">
 <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anchor" href="#non-linear-methods"></a>
@@ -2023,27 +2222,55 @@ <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anch
 <p>These methods attempt to find a low-dimensional representation of the
 data that attempt to preserve pair-wise distance and structure in
 high-dimensional gene space as best as possible.</p>
-<div class="codewrapper sourceCode" id="cb52">
+<div class="codewrapper sourceCode" id="cb118">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runTSNE</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runTSNE</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb120">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-27-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb53">
+<div class="codewrapper sourceCode" id="cb122">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">111</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runUMAP</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runUMAP</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb124">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
+<p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
 relative sizes and positions of the visual clusters may be misleading,
 as they tend to inflate dense clusters and compress sparse ones, such
 that we cannot use the size as a measure of subpopulation
@@ -2057,28 +2284,16 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 and replotting without the need for recomputing. Note the added
 <code>reducedDimNames</code> row when printing <code>sce</code>
 here:</p>
-<div class="codewrapper sourceCode" id="cb54">
+<div class="codewrapper sourceCode" id="cb126">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(3): PCA TSNE UMAP
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>Despite their shortcomings, t-SNE and UMAP can be useful
 visualization techniques. When using them, it is important to consider
@@ -2104,9 +2319,9 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb56">
+<div class="codewrapper sourceCode" id="cb128">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -2175,78 +2390,139 @@ <h3 id="computing-doublet-densities">Computing doublet densities<a class="anchor
 very likely to be a doublet itself.</p>
 <p>This approach is implemented below using the <code>scDblFinder</code>
 library. We then visualize the scores in a t-SNE plot.</p>
-<div class="codewrapper sourceCode" id="cb57">
+<div class="codewrapper sourceCode" id="cb129">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">dbl.dens</span> <span class="op">&lt;-</span> <span class="fu">computeDoubletDensity</span><span class="op">(</span><span class="va">sce</span>, subset.row <span class="op">=</span> <span class="va">hvg.sce.var</span>,</span>
-<span>                                  d <span class="op">=</span> <span class="fu">ncol</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.dens</span><span class="op">)</span></span></code></pre>
+<span>                                  d <span class="op">=</span> <span class="fu">ncol</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
- 0.04874  0.28757  0.46790  0.65614  0.82371 14.88032 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'computeDoubletDensity': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb59">
+<div class="codewrapper sourceCode" id="cb131">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">DoubletScore</span> <span class="op">&lt;-</span> <span class="va">dbl.dens</span></span>
-<span></span>
-<span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.dens</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'dbl.dens' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb133">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">DoubletScore</span> <span class="op">&lt;-</span> <span class="va">dbl.dens</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.dens' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb135">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-31-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>We can explicitly convert this into doublet calls by identifying
+<p>We can explicitly convert this into doublet calls by identifying
 large outliers for the score within each sample. Here we use the
 “griffiths” method to do so.</p>
-<div class="codewrapper sourceCode" id="cb60">
+<div class="codewrapper sourceCode" id="cb137">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dbl.calls</span> <span class="op">&lt;-</span> <span class="fu">doubletThresholding</span><span class="op">(</span><span class="fu">data.frame</span><span class="op">(</span>score <span class="op">=</span> <span class="va">dbl.dens</span><span class="op">)</span>,</span>
 <span>                                 method <span class="op">=</span> <span class="st">"griffiths"</span>,</span>
-<span>                                 returnType <span class="op">=</span> <span class="st">"call"</span><span class="op">)</span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.calls</span><span class="op">)</span></span></code></pre>
+<span>                                 returnType <span class="op">=</span> <span class="st">"call"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>singlet doublet
-   2124     313 </code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.dens' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb62">
+<div class="codewrapper sourceCode" id="cb139">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">doublet</span> <span class="op">&lt;-</span> <span class="va">dbl.calls</span></span>
-<span></span>
-<span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"DoubletScore"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.calls</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'dbl.calls' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb141">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">doublet</span> <span class="op">&lt;-</span> <span class="va">dbl.calls</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-32-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb63">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.calls' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb143">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"DoubletScore"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb145">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-32-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>One way to determine whether a cell is in a real transient state or
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<p>One way to determine whether a cell is in a real transient state or
 it is a doublet is to check the number of detected genes and total UMI
 counts.</p>
-<div class="codewrapper sourceCode" id="cb64">
+<div class="codewrapper sourceCode" id="cb147">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, <span class="st">"detected"</span>, <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-33-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb65">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb149">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, <span class="st">"detected"</span>, <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-33-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>In this case, we only have a few doublets at the periphery of some
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>In this case, we only have a few doublets at the periphery of some
 clusters. It could be fine to keep the doublets in the analysis, but it
 may be safer to discard them to avoid biases in downstream analysis
 (e.g., differential expression).</p>
@@ -2275,38 +2551,69 @@ <h3 class="callout-title">Exercise 1: Normalization</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb66">
+<div class="codewrapper sourceCode" id="cb151">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deconv.sf2</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="co"># dropped `cluster = clust` here</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf2</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">deconv.sf2</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="co"># dropped `cluster = clust` here</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.2985  0.8142  0.9583  1.0000  1.1582  2.7054 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pooledSizeFactors': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb68">
+<div class="codewrapper sourceCode" id="cb153">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf2</span> <span class="op">&lt;-</span> <span class="va">deconv.sf2</span></span>
-<span></span>
-<span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">deconv_sf</span>, <span class="va">deconv_sf2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf2</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'deconv.sf2' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb155">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf2</span> <span class="op">&lt;-</span> <span class="va">deconv.sf2</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf2' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb157">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb159">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">deconv_sf</span>, <span class="va">deconv_sf2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_abline</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">clust</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">facet_wrap</span><span class="op">(</span><span class="fu">vars</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-34-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>You can see that we get largely similar results, though for clusters
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<p>You can see that we get largely similar results, though for clusters
 3 and 9 there’s a slight deviation from the <code>y=x</code>
 relationship because these clusters (which are fairly distinct from the
 other clusters) are now being pooled with cells from other clusters.
@@ -2334,12 +2641,12 @@ <h3 class="callout-title">Exercise 2: PBMC Data</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
   <h4 class="accordion-header" id="headingSolution7"> Show me the solution </h4>
 </button>
-<div id="collapseSolution7" class="accordion-collapse collapse" aria-labelledby="headingSolution7" data-bs-parent="#accordionSolution7">
+<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
 <div class="accordion-body">
 <p>The first few lines here read the data from ExperimentHub and the
 mitochondrial genes are identified by gene symbols in the row data.
 Otherwise the steps are the same:</p>
-<div class="codewrapper sourceCode" id="cb69">
+<div class="codewrapper sourceCode" id="cb161">
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@@ -2442,7 +2749,7 @@ <h3 class="callout-title">Extension challenge 1: Spike-ins</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
   <h4 class="accordion-header" id="headingSolution8"> Show me the solution </h4>
 </button>
-<div id="collapseSolution8" class="accordion-collapse collapse" aria-labelledby="headingSolution8" data-bs-parent="#accordionSolution8">
+<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
 <div class="accordion-body">
 <p>Spike-ins are deliberately-introduced exogeneous RNA from an exotic
 or synthetic source at a known concentration. This provides a known
@@ -2486,7 +2793,7 @@ <h3 class="callout-title">Extension challenge 3: Reduced dimensionality represen
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
   <h4 class="accordion-header" id="headingSolution9"> Show me the solution </h4>
 </button>
-<div id="collapseSolution9" class="accordion-collapse collapse" aria-labelledby="headingSolution9" data-bs-parent="#accordionSolution9">
+<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
 <div class="accordion-body">
 <p>Mathematically, this would require the data to fall on a
 two-dimensional plane (for linear methods like PCA) or a smooth 2D
@@ -2761,7 +3068,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <p>We see in the help documentation for <code>?clusterCells</code> that
 all of the clustering algorithm details are handled through the
@@ -2958,7 +3265,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <p>You can see that at least among the top markers, cluster 6 (pale
 green) tends to have the least separation from cluster 1.</p>
@@ -3256,7 +3563,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
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 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -3520,7 +3827,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
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@@ -3670,7 +3977,7 @@ <h3 class="callout-title">Exercise 1: Clustering</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>The <code>NNGraphParam</code> constructor has an argument
 <code>cluster.args</code>. This allows to specify arguments passed on to
@@ -3687,7 +3994,7 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
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@@ -3726,7 +4033,7 @@ <h3 class="callout-title">Exercise 2: Reference marker genes</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb47">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -3799,7 +4106,7 @@ <h3 class="callout-title">Extension Challenge 1: Group pair comparisons</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
   <h4 class="accordion-header" id="headingSolution7"> Show me the solution </h4>
 </button>
-<div id="collapseSolution7" class="accordion-collapse collapse" aria-labelledby="headingSolution7" data-bs-parent="#accordionSolution7">
+<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
 <div class="accordion-body">
 <p>One important reason why is because averages over all other clusters
 can be sensitive to the cell type composition. If a rare cell type shows
@@ -3833,7 +4140,7 @@ <h3 class="callout-title">Extension Challenge 2: Parallelizing SingleR</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
   <h4 class="accordion-header" id="headingSolution8"> Show me the solution </h4>
 </button>
-<div id="collapseSolution8" class="accordion-collapse collapse" aria-labelledby="headingSolution8" data-bs-parent="#accordionSolution8">
+<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
 <div class="accordion-body">
 <p>Use <code>BiocParallel</code> and the <code>BPPARAM</code> argument!
 This example will set it to use four cores on your laptop, but you can
@@ -3875,7 +4182,7 @@ <h3 class="callout-title">Extension Challenge 3: Critical inspection of diagnost
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
   <h4 class="accordion-header" id="headingSolution9"> Show me the solution </h4>
 </button>
-<div id="collapseSolution9" class="accordion-collapse collapse" aria-labelledby="headingSolution9" data-bs-parent="#accordionSolution9">
+<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
 <div class="accordion-body">
 <p>The example that jumps out most strongly to the eye is ExE endoderm,
 which doesn’t show clear separate modes. Simultaneously, Endothelium
@@ -4169,7 +4476,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <p>Samples 5 and 6 were from the same “pool” of cells. Looking at the
 documentation for the dataset under <code>?WTChimeraData</code> we see
@@ -4234,7 +4541,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <p>False. Batch-level data can be retained through confounding with
 experimental factors or poor ability to distinguish experimental effects
@@ -4687,7 +4994,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
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 <div class="accordion-body">
 <p>“logFC” stands for log fold-change. <code>edgeR</code> uses a log2
 convention. Rather than reporting e.g. a 5-fold increase, it’s better to
@@ -5013,7 +5320,7 @@ <h3 class="callout-title">Exercise 1: Heatmaps</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>You can simply hand <code>pheatmap()</code> a matrix as its only
 argument. <code>pheatmap()</code> has a million options you can tweak,
@@ -5027,7 +5334,7 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
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 <div class="accordion-body">
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@@ -5062,7 +5369,7 @@ <h3 class="callout-title">Exercise 2: Model specification and comparison</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint2" aria-expanded="false" aria-controls="collapseHint2">
   <h4 class="accordion-header" id="headingHint2"> Give me a hint </h4>
 </button>
-<div id="collapseHint2" class="accordion-collapse collapse" aria-labelledby="headingHint2" data-bs-parent="#accordionHint2">
+<div id="collapseHint2" class="accordion-collapse collapse" data-bs-parent="#accordionHint2" aria-labelledby="headingHint2">
 <div class="accordion-body">
 <p>After running the second pseudobulk DGE, you can join the two
 <code>DataFrame</code>s of <code>Erythroid3</code> statistics using the
@@ -5077,7 +5384,7 @@ <h4 class="accordion-header" id="headingHint2"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb53">
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@@ -5160,7 +5467,7 @@ <h3 class="callout-title">Extension challenge 1: Group effects</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
 <p>It’s important to have multiple samples within each experimental
 group because it helps the batch effect correction algorithm distinguish
@@ -6950,7 +7257,7 @@ <h3 class="callout-title">Exercise 1: Basic counting + piping</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb23">
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@@ -7001,7 +7308,7 @@ <h3 class="callout-title">Exercise 2: Tissue &amp; type counting</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -7042,7 +7349,7 @@ <h3 class="callout-title">Exercise 3: Comparing metadata categories</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
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@@ -7134,7 +7441,7 @@ <h3 class="callout-title">Exercise 4: Highly specific cell groups</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb31">
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@@ -7243,8 +7550,8 @@ <h3 class="callout-title">Key Points</h3>
   "url": "https://ccb-hms.github.io/osca-carpentries/instructor/aio.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/aio.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/cell_type_annotation.html b/instructor/cell_type_annotation.html
index 180e36b..56bf4d0 100644
--- a/instructor/cell_type_annotation.html
+++ b/instructor/cell_type_annotation.html
@@ -518,7 +518,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <p>We see in the help documentation for <code>?clusterCells</code> that
 all of the clustering algorithm details are handled through the
@@ -712,7 +712,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <p>You can see that at least among the top markers, cluster 6 (pale
 green) tends to have the least separation from cluster 1.</p>
@@ -1007,7 +1007,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb31">
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@@ -1270,7 +1270,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
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@@ -1415,7 +1415,7 @@ <h3 class="callout-title">Exercise 1: Clustering</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>The <code>NNGraphParam</code> constructor has an argument
 <code>cluster.args</code>. This allows to specify arguments passed on to
@@ -1432,7 +1432,7 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb46">
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@@ -1470,7 +1470,7 @@ <h3 class="callout-title">Exercise 2: Reference marker genes</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb47">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1543,7 +1543,7 @@ <h3 class="callout-title">Extension Challenge 1: Group pair comparisons</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
   <h4 class="accordion-header" id="headingSolution7"> Show me the solution </h4>
 </button>
-<div id="collapseSolution7" class="accordion-collapse collapse" aria-labelledby="headingSolution7" data-bs-parent="#accordionSolution7">
+<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
 <div class="accordion-body">
 <p>One important reason why is because averages over all other clusters
 can be sensitive to the cell type composition. If a rare cell type shows
@@ -1577,7 +1577,7 @@ <h3 class="callout-title">Extension Challenge 2: Parallelizing SingleR</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
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 </button>
-<div id="collapseSolution8" class="accordion-collapse collapse" aria-labelledby="headingSolution8" data-bs-parent="#accordionSolution8">
+<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
 <div class="accordion-body">
 <p>Use <code>BiocParallel</code> and the <code>BPPARAM</code> argument!
 This example will set it to use four cores on your laptop, but you can
@@ -1619,7 +1619,7 @@ <h3 class="callout-title">Extension Challenge 3: Critical inspection of diagnost
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
   <h4 class="accordion-header" id="headingSolution9"> Show me the solution </h4>
 </button>
-<div id="collapseSolution9" class="accordion-collapse collapse" aria-labelledby="headingSolution9" data-bs-parent="#accordionSolution9">
+<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
 <div class="accordion-body">
 <p>The example that jumps out most strongly to the eye is ExE endoderm,
 which doesn’t show clear separate modes. Simultaneously, Endothelium
@@ -1751,7 +1751,7 @@ <h3 class="callout-title">Key Points</h3>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/cell_type_annotation.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-02",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/eda_qc.html b/instructor/eda_qc.html
index e97adab..bba580e 100644
--- a/instructor/eda_qc.html
+++ b/instructor/eda_qc.html
@@ -333,7 +333,7 @@ <h2 class="accordion-header" id="flush-headingTwelve">
       </div>
       <hr></nav><main id="main-content" class="main-content"><div class="container lesson-content">
         <h1>Exploratory data analysis and quality control</h1>
-        <p>Last updated on 2024-10-03 |
+        <p>Last updated on 2024-10-10 |
 
         <a href="https://github.com/ccb-hms/osca-carpentries/edit/main/episodes/eda_qc.Rmd" class="external-link">Edit this page <i aria-hidden="true" data-feather="edit"></i></a></p>
 
@@ -403,28 +403,37 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span><span class="kw">library</span><span class="op">(</span><span class="va">scran</span><span class="op">)</span></span>
 <span><span class="kw">library</span><span class="op">(</span><span class="va">scDblFinder</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">WTChimeraData</span><span class="op">(</span>samples <span class="op">=</span> <span class="fl">5</span>, type <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">WTChimeraData</span><span class="op">(</span>samples <span class="op">=</span> <span class="fl">5</span>, type <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error: failed to load resource
+  name: EH3019
+  title: WT chimera raw counts (sample 5)
+  reason: 1 resources failed to download</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb3">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 522554
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(522554): AAACCTGAGAAACCAT AAACCTGAGAAACCGC ...
-  TTTGTCATCTTTACGT TTTGTCATCTTTCCTC
-colData names(0):
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb5">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>This is the same data we examined in the previous lesson.</p>
 </section><section><h2 class="section-heading" id="droplet-processing">Droplet processing<a class="anchor" aria-label="anchor" href="#droplet-processing"></a></h2>
@@ -432,36 +441,6 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 while we can see that we have data for more than 500,000 droplets. It is
 likely that most of these droplets are empty and are capturing only
 ambient or background RNA.</p>
-<div class="codewrapper sourceCode" id="cb3">
-<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">bcrank</span> <span class="op">&lt;-</span> <span class="fu">barcodeRanks</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co"># Only showing unique points for plotting speed.</span></span>
-<span><span class="va">uniq</span> <span class="op">&lt;-</span> <span class="op">!</span><span class="fu">duplicated</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">rank</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">line_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>cutoff <span class="op">=</span> <span class="fu">names</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span>,</span>
-<span>                      value  <span class="op">=</span> <span class="fu">unlist</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">bcrank</span><span class="op">[</span><span class="va">uniq</span>,<span class="op">]</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">rank</span>, <span class="va">total</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">geom_hline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">line_df</span>,</span>
-<span>               <span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">cutoff</span>,</span>
-<span>                   yintercept <span class="op">=</span> <span class="va">value</span><span class="op">)</span>,</span>
-<span>               lty <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
-<span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Total UMI count"</span><span class="op">)</span></span></code></pre>
-</div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-2-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The distribution of total counts (called the unique molecular
-identifier or UMI count) exhibits a sharp transition between barcodes
-with large and small total counts, probably corresponding to
-cell-containing and empty droplets respectively.</p>
-<p>A simple approach would be to apply a threshold on the total count to
-only retain those barcodes with large totals. However, this may
-unnecessarily discard libraries derived from cell types with low RNA
-content.</p>
 <div id="callout1" class="callout">
 <div class="callout-square">
 <i class="callout-icon" data-feather="bell"></i>
@@ -483,6 +462,71 @@ <h3 class="callout-title">Callout</h3>
 </div>
 </div>
 </div>
+<p>We can visualize barcode read totals to visualize the distinction
+between empty droplets and properly profiled single cells in a so-called
+“knee plot”:</p>
+<div class="codewrapper sourceCode" id="cb7">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">bcrank</span> <span class="op">&lt;-</span> <span class="fu">barcodeRanks</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'm' in selecting a method for function 'barcodeRanks': error in evaluating the argument 'object' in selecting a method for function 'counts': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb9">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Only showing unique points for plotting speed.</span></span>
+<span><span class="va">uniq</span> <span class="op">&lt;-</span> <span class="op">!</span><span class="fu">duplicated</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">rank</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'duplicated': object 'bcrank' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb11">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">line_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>cutoff <span class="op">=</span> <span class="fu">names</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>                      value  <span class="op">=</span> <span class="fu">unlist</span><span class="op">(</span><span class="fu">metadata</span><span class="op">(</span><span class="va">bcrank</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'metadata': object 'bcrank' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb13">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">bcrank</span><span class="op">[</span><span class="va">uniq</span>,<span class="op">]</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">rank</span>, <span class="va">total</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">geom_hline</span><span class="op">(</span>data <span class="op">=</span> <span class="va">line_df</span>,</span>
+<span>               <span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">cutoff</span>,</span>
+<span>                   yintercept <span class="op">=</span> <span class="va">value</span><span class="op">)</span>,</span>
+<span>               lty <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
+<span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Total UMI count"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'bcrank' not found</code></pre>
+</div>
+<p>The distribution of total counts (called the unique molecular
+identifier or UMI count) exhibits a sharp transition between barcodes
+with large and small total counts, probably corresponding to
+cell-containing and empty droplets respectively.</p>
+<p>A simple approach would be to apply a threshold on the total count to
+only retain those barcodes with large totals. However, this may
+unnecessarily discard libraries derived from cell types with low RNA
+content.</p>
 <div id="challenge1" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
@@ -499,18 +543,18 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb4">
+<div class="codewrapper sourceCode" id="cb15">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">median</span><span class="op">(</span><span class="va">bcrank</span><span class="op">$</span><span class="va">total</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>[1] 2</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'median': object 'bcrank' not found</code></pre>
 </div>
 <p>Just 2! Clearly many barcodes produce practically no output.</p>
 <!-- This is a direct application challenge of of  "It is likely that most of these droplets are empty and are capturing only ambient or background RNA" and a synthesis challenge of the previous episode where they learned to access columns of DataFrames. -->
@@ -529,7 +573,7 @@ <h3 id="testing-for-empty-droplets">Testing for empty droplets<a class="anchor"
 <p>We call cells at a false discovery rate (FDR) of 0.1%, meaning that
 no more than 0.1% of our called barcodes should be empty droplets on
 average.</p>
-<div class="codewrapper sourceCode" id="cb6">
+<div class="codewrapper sourceCode" id="cb17">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -538,40 +582,45 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># this may take a few minutes</span></span>
-<span><span class="va">e.out</span> <span class="op">&lt;-</span> <span class="fu">emptyDrops</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span></span></code></pre>
+<span><span class="va">e.out</span> <span class="op">&lt;-</span> <span class="fu">emptyDrops</span><span class="op">(</span><span class="fu">counts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Mode   FALSE    TRUE    NA's
-logical    6184    3131  513239 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'm' in selecting a method for function 'emptyDrops': error in evaluating the argument 'object' in selecting a method for function 'counts': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb8">
+<div class="codewrapper sourceCode" id="cb19">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="fu">which</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span><span class="op">]</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'e.out' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb21">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="fu">which</span><span class="op">(</span><span class="va">e.out</span><span class="op">$</span><span class="va">FDR</span> <span class="op">&lt;=</span> <span class="fl">0.001</span><span class="op">)</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb23">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 3131
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(3131): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGTCAGTCTGATTG
-  TTTGTCATCTGAGTGT
-colData names(0):
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>The result confirms our expectation: only 3,131 droplets contain a
 cell, while the large majority of droplets are empty.</p>
@@ -580,7 +629,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler1" aria-expanded="false" aria-controls="collapseSpoiler1">
   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> Setting the Random Seed </h3>
 </button>
-<div id="collapseSpoiler1" class="accordion-collapse collapse" aria-labelledby="headingSpoiler1" data-bs-parent="#accordionSpoiler1">
+<div id="collapseSpoiler1" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler1" aria-labelledby="headingSpoiler1">
 <div class="accordion-body">
 <p>Whenever your code involves the generation of random numbers, it’s a
 good practice to set the random seed in R with
@@ -633,115 +682,115 @@ <h3 id="choice-of-quality-control-metrics">Choice of quality control metrics<a c
 <code>EnsDb</code> mouse package available in Bioconductor, but a more
 updated version of Ensembl can be used through the
 <code>AnnotationHub</code> or <code>biomaRt</code> packages.</p>
-<div class="codewrapper sourceCode" id="cb10">
+<div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">chr.loc</span> <span class="op">&lt;-</span> <span class="fu">mapIds</span><span class="op">(</span><span class="va">EnsDb.Mmusculus.v79</span>,</span>
 <span>                  keys    <span class="op">=</span> <span class="fu">rownames</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>,</span>
 <span>                  keytype <span class="op">=</span> <span class="st">"GENEID"</span>, </span>
-<span>                  column  <span class="op">=</span> <span class="st">"SEQNAME"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">is.mito</span> <span class="op">&lt;-</span> <span class="fu">which</span><span class="op">(</span><span class="va">chr.loc</span> <span class="op">==</span> <span class="st">"MT"</span><span class="op">)</span></span></code></pre>
+<span>                  column  <span class="op">=</span> <span class="st">"SEQNAME"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb27">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">is.mito</span> <span class="op">&lt;-</span> <span class="fu">which</span><span class="op">(</span><span class="va">chr.loc</span> <span class="op">==</span> <span class="st">"MT"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'which': object 'chr.loc' not found</code></pre>
 </div>
 <p>We can use the <code>scuttle</code> package to compute a set of
 quality control metrics, specifying that we want to use the
 mitochondrial genes as a special set of features.</p>
-<div class="codewrapper sourceCode" id="cb11">
+<div class="codewrapper sourceCode" id="cb29">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu">perCellQCMetrics</span><span class="op">(</span><span class="va">sce</span>, subsets <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>Mito <span class="op">=</span> <span class="va">is.mito</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">cbind</span><span class="op">(</span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="va">df</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>DataFrame with 3131 rows and 6 columns
-                       sum  detected subsets_Mito_sum subsets_Mito_detected
-                 &lt;numeric&gt; &lt;integer&gt;        &lt;numeric&gt;             &lt;integer&gt;
-AAACCTGAGACTGTAA     27577      5418              471                    10
-AAACCTGAGATGCCTT     29309      5405              679                    10
-AAACCTGAGCAGCCTC     28795      5218              480                    12
-AAACCTGCATACTCTT     34794      4781              496                    12
-AAACCTGGTGGTACAG       262       229                0                     0
-...                    ...       ...              ...                   ...
-TTTGGTTTCGCCATAA     38398      6020              252                    12
-TTTGTCACACCCTATC      3013      1451              123                     9
-TTTGTCACATTCTCAT      1472       675              599                    11
-TTTGTCAGTCTGATTG       361       293                0                     0
-TTTGTCATCTGAGTGT       267       233               16                     6
-                 subsets_Mito_percent     total
-                            &lt;numeric&gt; &lt;numeric&gt;
-AAACCTGAGACTGTAA              1.70795     27577
-AAACCTGAGATGCCTT              2.31669     29309
-AAACCTGAGCAGCCTC              1.66696     28795
-AAACCTGCATACTCTT              1.42553     34794
-AAACCTGGTGGTACAG              0.00000       262
-...                               ...       ...
-TTTGGTTTCGCCATAA             0.656284     38398
-TTTGTCACACCCTATC             4.082310      3013
-TTTGTCACATTCTCAT            40.692935      1472
-TTTGTCAGTCTGATTG             0.000000       361
-TTTGTCATCTGAGTGT             5.992509       267</code></pre>
+<code class="sourceCode R"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu">perCellQCMetrics</span><span class="op">(</span><span class="va">sce</span>, subsets <span class="op">=</span> <span class="fu">list</span><span class="op">(</span>Mito <span class="op">=</span> <span class="va">is.mito</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'perCellQCMetrics': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb31">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">cbind</span><span class="op">(</span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="va">df</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb33">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'sce' not found</code></pre>
 </div>
 <p>Now that we have computed the metrics, we have to decide on
 thresholds to define high- and low-quality samples. We could check how
 many cells are above/below a certain fixed threshold. For instance,</p>
-<div class="codewrapper sourceCode" id="cb13">
+<div class="codewrapper sourceCode" id="cb35">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">sum</span> <span class="op">&lt;</span> <span class="fl">10000</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>
-FALSE  TRUE
- 2477   654 </code></pre>
+<pre class="error" tabindex="0"><code>Error in df$sum: object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb15">
+<div class="codewrapper sourceCode" id="cb37">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">subsets_Mito_percent</span> <span class="op">&gt;</span> <span class="fl">10</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>
-FALSE  TRUE
- 2761   370 </code></pre>
+<pre class="error" tabindex="0"><code>Error in df$subsets_Mito_percent: object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>or we could look at the distribution of such metrics and use a data
 adaptive threshold.</p>
-<div class="codewrapper sourceCode" id="cb17">
+<div class="codewrapper sourceCode" id="cb39">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">detected</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-     98    4126    5168    4455    5670    7908 </code></pre>
+<pre class="error" tabindex="0"><code>Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'summary': object of type 'closure' is not subsettable</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb19">
+<div class="codewrapper sourceCode" id="cb41">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">subsets_Mito_percent</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
-  0.000   1.155   1.608   5.079   2.182  66.968 </code></pre>
+<pre class="error" tabindex="0"><code>Error in (function (cond) : error in evaluating the argument 'object' in selecting a method for function 'summary': object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>We can use the <code>perCellQCFilters</code> function to apply a set
 of common adaptive filters to identify low-quality cells. By default, we
@@ -749,38 +798,39 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 deviations (MADs) from the median in the “problematic” direction. This
 is loosely motivated by the fact that such a filter will retain 99% of
 non-outlier values that follow a normal distribution.</p>
-<div class="codewrapper sourceCode" id="cb21">
+<div class="codewrapper sourceCode" id="cb43">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">reasons</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">reasons</span></span></code></pre>
-</div>
-<div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
-</h3>
-<pre class="output" tabindex="0"><code>DataFrame with 3131 rows and 4 columns
-         low_lib_size   low_n_features high_subsets_Mito_percent   discard
-     &lt;outlier.filter&gt; &lt;outlier.filter&gt;          &lt;outlier.filter&gt; &lt;logical&gt;
-1               FALSE            FALSE                     FALSE     FALSE
-2               FALSE            FALSE                     FALSE     FALSE
-3               FALSE            FALSE                     FALSE     FALSE
-4               FALSE            FALSE                     FALSE     FALSE
-5                TRUE             TRUE                     FALSE      TRUE
-...               ...              ...                       ...       ...
-3127            FALSE            FALSE                     FALSE     FALSE
-3128             TRUE             TRUE                      TRUE      TRUE
-3129             TRUE             TRUE                      TRUE      TRUE
-3130             TRUE             TRUE                     FALSE      TRUE
-3131             TRUE             TRUE                      TRUE      TRUE</code></pre>
+<code class="sourceCode R"><span><span class="va">reasons</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span><span class="op">)</span></span></code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb23">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in x[[sum.field]]: object of type 'closure' is not subsettable</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb45">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">reasons</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'reasons' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb47">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span> <span class="op">&lt;-</span> <span class="va">reasons</span><span class="op">$</span><span class="va">discard</span></span></code></pre>
 </div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'reasons' not found</code></pre>
+</div>
 <div id="challenge2" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
@@ -789,7 +839,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
 <p>Maybe our sample preparation was poor and we want the QC to be more
-strict. How could we change the set the QC filtering to use 4 MADs as
+strict. How could we change the set the QC filtering to use 2.5 MADs as
 the threshold for outlier calling?</p>
 </div>
 </div>
@@ -799,14 +849,19 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
-<p>You set <code>nmads = 4</code> like so:</p>
-<div class="codewrapper sourceCode" id="cb24">
+<p>You set <code>nmads = 2.5</code> like so:</p>
+<div class="codewrapper sourceCode" id="cb49">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">reasons_strict</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, nmads <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">reasons_strict</span> <span class="op">&lt;-</span> <span class="fu">perCellQCFilters</span><span class="op">(</span><span class="va">df</span>, sub.fields <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, nmads <span class="op">=</span> <span class="fl">2.5</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in x[[sum.field]]: object of type 'closure' is not subsettable</code></pre>
 </div>
 <p>You would then need to reassign the <code>discard</code> column as
 well, but we’ll stick with the 3 MADs default for now.
@@ -824,69 +879,87 @@ <h3 id="diagnostic-plots">Diagnostic plots<a class="anchor" aria-label="anchor"
 outliers and with a marked difference from “regular” cells (e.g., a
 bimodal distribution or a long tail). Moreover, if there are too many
 discarded cells, further exploration might be needed.</p>
-<div class="codewrapper sourceCode" id="cb25">
+<div class="codewrapper sourceCode" id="cb51">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Total count"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb26">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb53">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"detected"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Detected features"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb27">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb55">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Mito percent"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-3.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>While the univariate distribution of QC metrics can give some insight
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>While the univariate distribution of QC metrics can give some insight
 on the quality of the sample, often looking at the bivariate
 distribution of QC metrics is useful, e.g., to confirm that there are no
 cells with both large total counts and large mitochondrial counts, to
 ensure that we are not inadvertently removing high-quality cells that
 happen to be highly metabolically active.</p>
-<div class="codewrapper sourceCode" id="cb28">
+<div class="codewrapper sourceCode" id="cb57">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>,  x <span class="op">=</span><span class="st">"sum"</span>, y <span class="op">=</span> <span class="st">"subsets_Mito_percent"</span>, colour_by <span class="op">=</span> <span class="st">"discard"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It could also be a good idea to perform a differential expression
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>It could also be a good idea to perform a differential expression
 analysis between retained and discarded cells to check wether we are
 removing an unusual cell population rather than low-quality libraries
 (see <a href="https://bioconductor.org/books/release/OSCA.advanced/quality-control-redux.html#qc-discard-cell-types" class="external-link">Section
 1.5 of OSCA advanced</a>).</p>
 <p>Once we are happy with the results, we can discard the low-quality
 cells by subsetting the original object.</p>
-<div class="codewrapper sourceCode" id="cb29">
+<div class="codewrapper sourceCode" id="cb59">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="op">!</span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span><span class="op">]</span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="va">sce</span><span class="op">[</span>,<span class="op">!</span><span class="va">sce</span><span class="op">$</span><span class="va">discard</span><span class="op">]</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb61">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(1): counts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(7): sum detected ... total discard
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 </div>
 </section><section><h2 class="section-heading" id="normalization">Normalization<a class="anchor" aria-label="anchor" href="#normalization"></a></h2>
@@ -919,31 +992,53 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 defined such that the mean size factor across all cells is equal to 1.
 This ensures that the normalized expression values are on the same scale
 as the original counts.</p>
-<div class="codewrapper sourceCode" id="cb31">
+<div class="codewrapper sourceCode" id="cb63">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">lib.sf</span> <span class="op">&lt;-</span> <span class="fu">librarySizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">lib.sf</span> <span class="op">&lt;-</span> <span class="fu">librarySizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.2730  0.7879  0.9600  1.0000  1.1730  2.5598 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'librarySizeFactors': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb33">
+<div class="codewrapper sourceCode" id="cb65">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>size_factor <span class="op">=</span> <span class="va">lib.sf</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'lib.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb67">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>size_factor <span class="op">=</span> <span class="va">lib.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'lib.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb69">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_histogram</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-14-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="section level3">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<div class="section level3">
 <h3 id="normalization-by-deconvolution">Normalization by deconvolution<a class="anchor" aria-label="anchor" href="#normalization-by-deconvolution"></a></h3>
 <p>Library size normalization is not optimal, as it assumes that the
 total sum of UMI counts differ between cells only for technical and not
@@ -965,83 +1060,127 @@ <h3 id="normalization-by-deconvolution">Normalization by deconvolution<a class="
 <p>Note that while we focus on normalization by deconvolution here, many
 other methods have been proposed and lead to similar performance (see <a href="reference.html#litref">Borella 2022</a> for a comparative
 review).</p>
-<div class="codewrapper sourceCode" id="cb34">
+<div class="codewrapper sourceCode" id="cb71">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="fu">quickCluster</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> </span>
-<span></span>
-<span><span class="fu">table</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span></span></code></pre>
+<span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="fu">quickCluster</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> </span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>clust
-  1   2   3   4   5   6   7   8   9  10  11  12  13
-273 159 250 122 187 201 154 252 152 169 199 215 104 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'quickCluster': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb36">
+<div class="codewrapper sourceCode" id="cb73">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deconv.sf</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span>, cluster <span class="op">=</span> <span class="va">clust</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">table</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.3100  0.8028  0.9626  1.0000  1.1736  2.7858 </code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb38">
+<div class="codewrapper sourceCode" id="cb75">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span>
-<span></span>
-<span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span>, <span class="va">deconv_sf</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="va">deconv.sf</span> <span class="op">&lt;-</span> <span class="fu">pooledSizeFactors</span><span class="op">(</span><span class="va">sce</span>, cluster <span class="op">=</span> <span class="va">clust</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pooledSizeFactors': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb77">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb79">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb81">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb83">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">size_factor</span>, <span class="va">deconv_sf</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_abline</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">clust</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-16-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>Once we have computed the size factors, we compute the normalized
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<p>Once we have computed the size factors, we compute the normalized
 expression values for each cell by dividing the count for each gene with
 the appropriate size factor for that cell. Since we are typically going
 to work with log-transformed counts, the function
 <code>logNormCounts</code> also log-transforms the normalized values,
 creating a new assay called <code>logcounts</code>.</p>
-<div class="codewrapper sourceCode" id="cb39">
+<div class="codewrapper sourceCode" id="cb85">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span>
-<span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">logNormCounts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">deconv.sf</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb87">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">logNormCounts</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'logNormCounts': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb89">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(0):
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <div id="challenge3" class="callout challenge">
 <div class="callout-square">
@@ -1052,16 +1191,16 @@ <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
 <p>Fill in the blanks for normalization that uses simpler library size
 factors instead of deconvolution.</p>
-<div class="codewrapper sourceCode" id="cb41">
+<div class="codewrapper sourceCode" id="cb91">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
-<span id="cb41-2"><a href="#cb41-2" tabindex="-1"></a></span>
-<span id="cb41-3"><a href="#cb41-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
-<span id="cb41-4"><a href="#cb41-4" tabindex="-1"></a></span>
-<span id="cb41-5"><a href="#cb41-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
-<span id="cb41-6"><a href="#cb41-6" tabindex="-1"></a></span>
-<span id="cb41-7"><a href="#cb41-7" tabindex="-1"></a>sce</span></code></pre>
+<pre class="sourceCode r" tabindex="0"><code class="sourceCode r"><span id="cb91-1"><a href="#cb91-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
+<span id="cb91-2"><a href="#cb91-2" tabindex="-1"></a></span>
+<span id="cb91-3"><a href="#cb91-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
+<span id="cb91-4"><a href="#cb91-4" tabindex="-1"></a></span>
+<span id="cb91-5"><a href="#cb91-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
+<span id="cb91-6"><a href="#cb91-6" tabindex="-1"></a></span>
+<span id="cb91-7"><a href="#cb91-7" tabindex="-1"></a>sce</span></code></pre>
 </div>
 </div>
 </div>
@@ -1071,9 +1210,9 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb42">
+<div class="codewrapper sourceCode" id="cb92">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1117,25 +1256,57 @@ <h3 id="quantifying-per-gene-variation">Quantifying per-gene variation<a class="
 its abundance than its underlying biological heterogeneity. To account
 for this, the <code>modelGeneVar</code> function fits a trend to the
 variance with respect to abundance across all genes.</p>
-<div class="codewrapper sourceCode" id="cb43">
+<div class="codewrapper sourceCode" id="cb93">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">dec.sce</span> <span class="op">&lt;-</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">fit.sce</span> <span class="op">&lt;-</span> <span class="fu">metadata</span><span class="op">(</span><span class="va">dec.sce</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">mean_var_df</span> <span class="op">=</span> <span class="fu">data.frame</span><span class="op">(</span>mean <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">mean</span>,</span>
-<span>                         var  <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">var</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">mean_var_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">mean</span>, <span class="va">var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="va">dec.sce</span> <span class="op">&lt;-</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'modelGeneVar': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb95">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">fit.sce</span> <span class="op">&lt;-</span> <span class="fu">metadata</span><span class="op">(</span><span class="va">dec.sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'metadata': object 'dec.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb97">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">mean_var_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>mean <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">mean</span>,</span>
+<span>                          var  <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">var</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'fit.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb99">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">mean_var_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">mean</span>, <span class="va">var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_function</span><span class="op">(</span>fun <span class="op">=</span> <span class="va">fit.sce</span><span class="op">$</span><span class="va">trend</span>, </span>
 <span>                  color <span class="op">=</span> <span class="st">"dodgerblue"</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>x <span class="op">=</span> <span class="st">"Mean of log-expression"</span>,</span>
 <span>         y <span class="op">=</span> <span class="st">"Variance of log-expression"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The blue line represents the uninteresting “technical” variance for
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'mean_var_df' not found</code></pre>
+</div>
+<p>The blue line represents the uninteresting “technical” variance for
 any given gene abundance. The genes with a lot of additional variance
 exhibit interesting “biological” variation.</p>
 </div>
@@ -1151,19 +1322,27 @@ <h3 id="selecting-highly-variable-genes">Selecting highly variable genes<a class
 differentially expressed across our cell populations and hence select
 10% of genes as highly variable (e.g., by setting
 <code>prop = 0.1</code>).</p>
-<div class="codewrapper sourceCode" id="cb44">
+<div class="codewrapper sourceCode" id="cb101">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">hvg.sce.var</span> <span class="op">&lt;-</span> <span class="fu">getTopHVGs</span><span class="op">(</span><span class="va">dec.sce</span>, n <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">head</span><span class="op">(</span><span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">hvg.sce.var</span> <span class="op">&lt;-</span> <span class="fu">getTopHVGs</span><span class="op">(</span><span class="va">dec.sce</span>, n <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dec.sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb103">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">head</span><span class="op">(</span><span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>[1] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919"
-[4] "ENSMUSG00000052187" "ENSMUSG00000048583" "ENSMUSG00000051855"</code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'hvg.sce.var' not found</code></pre>
 </div>
 <div id="challenge4" class="callout challenge">
 <div class="callout-square">
@@ -1183,7 +1362,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p><code>modelGeneVar()</code> can take a <code>block</code>
 argument.</p>
@@ -1196,11 +1375,11 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <p>Use the <code>block</code> argument in the call to
 <code>modelGeneVar()</code> like so:</p>
-<div class="codewrapper sourceCode" id="cb46">
+<div class="codewrapper sourceCode" id="cb105">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1240,7 +1419,7 @@ <h3 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a c
 replacing the original variables (genes) by fewer linear combinations of
 these variables, that are orthogonal and have successively maximal
 variance. Such linear combinations seek to “separate out” the
-observations (cells), while loosing as little information as
+observations (cells), while losing as little information as
 possible.</p>
 <p>Without getting into the technical details, one nice feature of PCA
 is that the principal components (PCs) are ordered by how much variance
@@ -1253,70 +1432,91 @@ <h3 id="principal-component-analysis-pca">Principal Component Analysis (PCA)<a c
 <p>One simple way to maximize our chance of capturing biological
 variation is by computing the PCs starting from the highly variable
 genes identified before.</p>
-<div class="codewrapper sourceCode" id="cb47">
+<div class="codewrapper sourceCode" id="cb106">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runPCA</span><span class="op">(</span><span class="va">sce</span>, subset_row <span class="op">=</span> <span class="va">hvg.sce.var</span><span class="op">)</span></span>
-<span></span>
-<span><span class="va">sce</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runPCA</span><span class="op">(</span><span class="va">sce</span>, subset_row <span class="op">=</span> <span class="va">hvg.sce.var</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'runPCA': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb108">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(1): PCA
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>By default, <code>runPCA</code> computes the first 50 principal
 components. We can check how much original variability they explain.
 These values are stored in the attributes of the <code>percentVar</code>
 reducedDim:</p>
-<div class="codewrapper sourceCode" id="cb49">
+<div class="codewrapper sourceCode" id="cb110">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pct_var_df</span> <span class="op">&lt;-</span> <span class="fu">data.frame</span><span class="op">(</span>PC <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">50</span>,</span>
-<span>                         pct_var <span class="op">=</span> <span class="fu">attr</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="st">"percentVar"</span><span class="op">)</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">pct_var_df</span>,</span>
+<span>                         pct_var <span class="op">=</span> <span class="fu">attr</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="st">"percentVar"</span><span class="op">)</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb112">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">pct_var_df</span>,</span>
 <span>       <span class="fu">aes</span><span class="op">(</span><span class="va">PC</span>, <span class="va">pct_var</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">labs</span><span class="op">(</span>y <span class="op">=</span> <span class="st">"Variance explained (%)"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>You can see the first two PCs capture the largest amount of
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'pct_var_df' not found</code></pre>
+</div>
+<p>You can see the first two PCs capture the largest amount of
 variation, but in this case you have to take the first 8 PCs before
 you’ve captured 50% of the total.</p>
 <p>And we can of course visualize the first 2-3 components, perhaps
 color-coding each point by an interesting feature, in this case the
 total number of UMIs per cell.</p>
-<div class="codewrapper sourceCode" id="cb50">
+<div class="codewrapper sourceCode" id="cb114">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotPCA</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"sum"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-25-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It can be helpful to compare pairs of PCs. This can be done with the
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'plotPCA': object 'sce' not found</code></pre>
+</div>
+<p>It can be helpful to compare pairs of PCs. This can be done with the
 <code>ncomponents</code> argument to <code>plotReducedDim()</code>. For
 example if one batch or cell type splits off on a particular PC, this
 can help visualize the effect of that.</p>
-<div class="codewrapper sourceCode" id="cb51">
+<div class="codewrapper sourceCode" id="cb116">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotReducedDim</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span>, ncomponents <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-26-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure></div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+</div>
 <div class="section level3">
 <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anchor" href="#non-linear-methods"></a></h3>
 <p>While PCA is a simple and effective way to visualize (and interpret!)
@@ -1326,27 +1526,55 @@ <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anch
 <p>These methods attempt to find a low-dimensional representation of the
 data that attempt to preserve pair-wise distance and structure in
 high-dimensional gene space as best as possible.</p>
-<div class="codewrapper sourceCode" id="cb52">
+<div class="codewrapper sourceCode" id="cb118">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runTSNE</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runTSNE</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb120">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-27-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb53">
+<div class="codewrapper sourceCode" id="cb122">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">111</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runUMAP</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+<span><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">runUMAP</span><span class="op">(</span><span class="va">sce</span>, dimred <span class="op">=</span> <span class="st">"PCA"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
+<div class="codewrapper sourceCode" id="cb124">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
 relative sizes and positions of the visual clusters may be misleading,
 as they tend to inflate dense clusters and compress sparse ones, such
 that we cannot use the size as a measure of subpopulation
@@ -1360,28 +1588,16 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 and replotting without the need for recomputing. Note the added
 <code>reducedDimNames</code> row when printing <code>sce</code>
 here:</p>
-<div class="codewrapper sourceCode" id="cb54">
+<div class="codewrapper sourceCode" id="cb126">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">sce</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>class: SingleCellExperiment
-dim: 29453 2437
-metadata(0):
-assays(2): counts logcounts
-rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
-  ENSMUSG00000095742 tomato-td
-rowData names(2): ENSEMBL SYMBOL
-colnames(2437): AAACCTGAGACTGTAA AAACCTGAGATGCCTT ... TTTGGTTTCAGTCAGT
-  TTTGGTTTCGCCATAA
-colData names(8): sum detected ... discard sizeFactor
-reducedDimNames(3): PCA TSNE UMAP
-mainExpName: NULL
-altExpNames(0):</code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
 </div>
 <p>Despite their shortcomings, t-SNE and UMAP can be useful
 visualization techniques. When using them, it is important to consider
@@ -1407,9 +1623,9 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb56">
+<div class="codewrapper sourceCode" id="cb128">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1473,78 +1689,139 @@ <h3 id="computing-doublet-densities">Computing doublet densities<a class="anchor
 very likely to be a doublet itself.</p>
 <p>This approach is implemented below using the <code>scDblFinder</code>
 library. We then visualize the scores in a t-SNE plot.</p>
-<div class="codewrapper sourceCode" id="cb57">
+<div class="codewrapper sourceCode" id="cb129">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">set.seed</span><span class="op">(</span><span class="fl">100</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">dbl.dens</span> <span class="op">&lt;-</span> <span class="fu">computeDoubletDensity</span><span class="op">(</span><span class="va">sce</span>, subset.row <span class="op">=</span> <span class="va">hvg.sce.var</span>,</span>
-<span>                                  d <span class="op">=</span> <span class="fu">ncol</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.dens</span><span class="op">)</span></span></code></pre>
+<span>                                  d <span class="op">=</span> <span class="fu">ncol</span><span class="op">(</span><span class="fu">reducedDim</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
- 0.04874  0.28757  0.46790  0.65614  0.82371 14.88032 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'computeDoubletDensity': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb59">
+<div class="codewrapper sourceCode" id="cb131">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">DoubletScore</span> <span class="op">&lt;-</span> <span class="va">dbl.dens</span></span>
-<span></span>
-<span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.dens</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'dbl.dens' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb133">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">DoubletScore</span> <span class="op">&lt;-</span> <span class="va">dbl.dens</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.dens' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb135">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-31-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>We can explicitly convert this into doublet calls by identifying
+<p>We can explicitly convert this into doublet calls by identifying
 large outliers for the score within each sample. Here we use the
 “griffiths” method to do so.</p>
-<div class="codewrapper sourceCode" id="cb60">
+<div class="codewrapper sourceCode" id="cb137">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">dbl.calls</span> <span class="op">&lt;-</span> <span class="fu">doubletThresholding</span><span class="op">(</span><span class="fu">data.frame</span><span class="op">(</span>score <span class="op">=</span> <span class="va">dbl.dens</span><span class="op">)</span>,</span>
 <span>                                 method <span class="op">=</span> <span class="st">"griffiths"</span>,</span>
-<span>                                 returnType <span class="op">=</span> <span class="st">"call"</span><span class="op">)</span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.calls</span><span class="op">)</span></span></code></pre>
+<span>                                 returnType <span class="op">=</span> <span class="st">"call"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>singlet doublet
-   2124     313 </code></pre>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.dens' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb62">
+<div class="codewrapper sourceCode" id="cb139">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">doublet</span> <span class="op">&lt;-</span> <span class="va">dbl.calls</span></span>
-<span></span>
-<span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"DoubletScore"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">dbl.calls</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'dbl.calls' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb141">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sce</span><span class="op">$</span><span class="va">doublet</span> <span class="op">&lt;-</span> <span class="va">dbl.calls</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'dbl.calls' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb143">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, y <span class="op">=</span> <span class="st">"DoubletScore"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-32-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb63">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb145">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-32-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>One way to determine whether a cell is in a real transient state or
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'reducedDim': object 'sce' not found</code></pre>
+</div>
+<p>One way to determine whether a cell is in a real transient state or
 it is a doublet is to check the number of detected genes and total UMI
 counts.</p>
-<div class="codewrapper sourceCode" id="cb64">
+<div class="codewrapper sourceCode" id="cb147">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, <span class="st">"detected"</span>, <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"DoubletScore"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-33-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb65">
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb149">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">plotColData</span><span class="op">(</span><span class="va">sce</span>, <span class="st">"detected"</span>, <span class="st">"sum"</span>, colour_by <span class="op">=</span> <span class="st">"doublet"</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-33-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>In this case, we only have a few doublets at the periphery of some
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sce' not found</code></pre>
+</div>
+<p>In this case, we only have a few doublets at the periphery of some
 clusters. It could be fine to keep the doublets in the analysis, but it
 may be safer to discard them to avoid biases in downstream analysis
 (e.g., differential expression).</p>
@@ -1571,38 +1848,69 @@ <h3 class="callout-title">Exercise 1: Normalization</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
-<div class="codewrapper sourceCode" id="cb66">
+<div class="codewrapper sourceCode" id="cb151">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">deconv.sf2</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="co"># dropped `cluster = clust` here</span></span>
-<span></span>
-<span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf2</span><span class="op">)</span></span></code></pre>
+<code class="sourceCode R"><span><span class="va">deconv.sf2</span> <span class="op">&lt;-</span> <span class="fu">calculateSumFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="co"># dropped `cluster = clust` here</span></span></code></pre>
 </div>
 <div class="codewrapper">
-<h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
-<pre class="output" tabindex="0"><code>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
- 0.2985  0.8142  0.9583  1.0000  1.1582  2.7054 </code></pre>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pooledSizeFactors': object 'sce' not found</code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb68">
+<div class="codewrapper sourceCode" id="cb153">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf2</span> <span class="op">&lt;-</span> <span class="va">deconv.sf2</span></span>
-<span></span>
-<span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span>
-<span></span>
-<span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">deconv_sf</span>, <span class="va">deconv_sf2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
+<code class="sourceCode R"><span><span class="fu">summary</span><span class="op">(</span><span class="va">deconv.sf2</span><span class="op">)</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'deconv.sf2' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb155">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">deconv_sf2</span> <span class="op">&lt;-</span> <span class="va">deconv.sf2</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'deconv.sf2' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb157">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">sf_df</span><span class="op">$</span><span class="va">clust</span> <span class="op">&lt;-</span> <span class="va">clust</span></span></code></pre>
+</div>
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'clust' not found</code></pre>
+</div>
+<div class="codewrapper sourceCode" id="cb159">
+<h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">ggplot</span><span class="op">(</span><span class="va">sf_df</span>, <span class="fu">aes</span><span class="op">(</span><span class="va">deconv_sf</span>, <span class="va">deconv_sf2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_abline</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">geom_point</span><span class="op">(</span><span class="fu">aes</span><span class="op">(</span>color <span class="op">=</span> <span class="va">clust</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu">scale_x_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">scale_y_log10</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
 <span>    <span class="fu">facet_wrap</span><span class="op">(</span><span class="fu">vars</span><span class="op">(</span><span class="va">clust</span><span class="op">)</span><span class="op">)</span></span></code></pre>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-34-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>You can see that we get largely similar results, though for clusters
+<div class="codewrapper">
+<h3 class="code-label">ERROR<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
+</h3>
+<pre class="error" tabindex="0"><code>Error in eval(expr, envir, enclos): object 'sf_df' not found</code></pre>
+</div>
+<p>You can see that we get largely similar results, though for clusters
 3 and 9 there’s a slight deviation from the <code>y=x</code>
 relationship because these clusters (which are fairly distinct from the
 other clusters) are now being pooled with cells from other clusters.
@@ -1630,12 +1938,12 @@ <h3 class="callout-title">Exercise 2: PBMC Data</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution7" aria-expanded="false" aria-controls="collapseSolution7">
   <h4 class="accordion-header" id="headingSolution7"> Show me the solution </h4>
 </button>
-<div id="collapseSolution7" class="accordion-collapse collapse" aria-labelledby="headingSolution7" data-bs-parent="#accordionSolution7">
+<div id="collapseSolution7" class="accordion-collapse collapse" data-bs-parent="#accordionSolution7" aria-labelledby="headingSolution7">
 <div class="accordion-body">
 <p>The first few lines here read the data from ExperimentHub and the
 mitochondrial genes are identified by gene symbols in the row data.
 Otherwise the steps are the same:</p>
-<div class="codewrapper sourceCode" id="cb69">
+<div class="codewrapper sourceCode" id="cb161">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1738,7 +2046,7 @@ <h3 class="callout-title">Extension challenge 1: Spike-ins</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution8" aria-expanded="false" aria-controls="collapseSolution8">
   <h4 class="accordion-header" id="headingSolution8"> Show me the solution </h4>
 </button>
-<div id="collapseSolution8" class="accordion-collapse collapse" aria-labelledby="headingSolution8" data-bs-parent="#accordionSolution8">
+<div id="collapseSolution8" class="accordion-collapse collapse" data-bs-parent="#accordionSolution8" aria-labelledby="headingSolution8">
 <div class="accordion-body">
 <p>Spike-ins are deliberately-introduced exogeneous RNA from an exotic
 or synthetic source at a known concentration. This provides a known
@@ -1782,7 +2090,7 @@ <h3 class="callout-title">Extension challenge 3: Reduced dimensionality represen
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution9" aria-expanded="false" aria-controls="collapseSolution9">
   <h4 class="accordion-header" id="headingSolution9"> Show me the solution </h4>
 </button>
-<div id="collapseSolution9" class="accordion-collapse collapse" aria-labelledby="headingSolution9" data-bs-parent="#accordionSolution9">
+<div id="collapseSolution9" class="accordion-collapse collapse" data-bs-parent="#accordionSolution9" aria-labelledby="headingSolution9">
 <div class="accordion-body">
 <p>Mathematically, this would require the data to fall on a
 two-dimensional plane (for linear methods like PCA) or a smooth 2D
@@ -1920,8 +2228,8 @@ <h3 class="callout-title">Further Reading</h3>
   "url": "https://ccb-hms.github.io/osca-carpentries/instructor/eda_qc.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/eda_qc.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-03",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/hca.html b/instructor/hca.html
index 130b0f3..08360df 100644
--- a/instructor/hca.html
+++ b/instructor/hca.html
@@ -760,7 +760,7 @@ <h3 class="callout-title">Exercise 1: Basic counting + piping</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb23">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -811,7 +811,7 @@ <h3 class="callout-title">Exercise 2: Tissue &amp; type counting</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb25">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -852,7 +852,7 @@ <h3 class="callout-title">Exercise 3: Comparing metadata categories</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb27">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -944,7 +944,7 @@ <h3 class="callout-title">Exercise 4: Highly specific cell groups</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb31">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1066,7 +1066,7 @@ <h3 class="callout-title">Key Points</h3>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/hca.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-04",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/images.html b/instructor/images.html
index 5f04c17..dcf47c1 100644
--- a/instructor/images.html
+++ b/instructor/images.html
@@ -376,45 +376,7 @@ <h3 id="intro-sce-figure-2">Figure 2</h3>
 <figure><img src="../fig/intro-sce-rendered-unnamed-chunk-14-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr></section><section id="eda_qc"><h2 class="section-heading"><a href="eda_qc.html">Exploratory data analysis and quality control</a></h2>
 <hr class="half-width">
 <h3 id="eda_qc-figure-1">Figure 1</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-2-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-2">Figure 2</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-3">Figure 3</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-4">Figure 4</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-11-3.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-5">Figure 5</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-6">Figure 6</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-14-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-7">Figure 7</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-16-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-8">Figure 8</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-20-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-9">Figure 9</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-10">Figure 10</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-25-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-11">Figure 11</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-26-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-12">Figure 12</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-27-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-13">Figure 13</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-14">Figure 14</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-30-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-15">Figure 15</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-31-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-16">Figure 16</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-32-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-17">Figure 17</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-32-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-18">Figure 18</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-33-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-19">Figure 19</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-33-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
-<h3 id="eda_qc-figure-20">Figure 20</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-34-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr></section><section id="cell_type_annotation"><h2 class="section-heading"><a href="cell_type_annotation.html">Cell type annotation</a></h2>
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-30-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr></section><section id="cell_type_annotation"><h2 class="section-heading"><a href="cell_type_annotation.html">Cell type annotation</a></h2>
 <hr class="half-width">
 <h3 id="cell_type_annotation-figure-1">Figure 1</h3>
 <figure><img src="../fig/cell_type_annotation-rendered-cluster-viz-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
@@ -512,8 +474,8 @@ <h3 id="hca-figure-1">Figure 1</h3>
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   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/images.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/index.html b/instructor/index.html
index ce17ed3..129a80b 100644
--- a/instructor/index.html
+++ b/instructor/index.html
@@ -533,8 +533,8 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
   "url": "https://ccb-hms.github.io/osca-carpentries/instructor/index.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/index.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/instructor-notes.html b/instructor/instructor-notes.html
index 3291ece..e931daf 100644
--- a/instructor/instructor-notes.html
+++ b/instructor/instructor-notes.html
@@ -441,8 +441,8 @@ <h1>Instructor Notes</h1>
   "url": "https://ccb-hms.github.io/osca-carpentries/instructor/instructor-notes.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/instructor-notes.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/intro-sce.html b/instructor/intro-sce.html
index a039443..10b78e6 100644
--- a/instructor/intro-sce.html
+++ b/instructor/intro-sce.html
@@ -1000,7 +1000,7 @@ <h3 class="callout-title">Key Points</h3>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/intro-sce.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-07",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/key-points.html b/instructor/key-points.html
index 5d734d2..7b05c4e 100644
--- a/instructor/key-points.html
+++ b/instructor/key-points.html
@@ -540,8 +540,8 @@ <h2 class="accordion-header" id="flush-headingTwelve">
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   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/key-points.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/large_data.html b/instructor/large_data.html
index 433db5a..8b6b34f 100644
--- a/instructor/large_data.html
+++ b/instructor/large_data.html
@@ -1610,7 +1610,7 @@ <h3 class="callout-title">Key Points</h3>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/large_data.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-07",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/instructor/multi-sample.html b/instructor/multi-sample.html
index 0944874..05ea2a6 100644
--- a/instructor/multi-sample.html
+++ b/instructor/multi-sample.html
@@ -545,7 +545,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <p>Samples 5 and 6 were from the same “pool” of cells. Looking at the
 documentation for the dataset under <code>?WTChimeraData</code> we see
@@ -608,7 +608,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <p>False. Batch-level data can be retained through confounding with
 experimental factors or poor ability to distinguish experimental effects
@@ -1057,7 +1057,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
 <p>“logFC” stands for log fold-change. <code>edgeR</code> uses a log2
 convention. Rather than reporting e.g. a 5-fold increase, it’s better to
@@ -1374,7 +1374,7 @@ <h3 class="callout-title">Exercise 1: Heatmaps</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>You can simply hand <code>pheatmap()</code> a matrix as its only
 argument. <code>pheatmap()</code> has a million options you can tweak,
@@ -1388,7 +1388,7 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb52">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1423,7 +1423,7 @@ <h3 class="callout-title">Exercise 2: Model specification and comparison</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint2" aria-expanded="false" aria-controls="collapseHint2">
   <h4 class="accordion-header" id="headingHint2"> Give me a hint </h4>
 </button>
-<div id="collapseHint2" class="accordion-collapse collapse" aria-labelledby="headingHint2" data-bs-parent="#accordionHint2">
+<div id="collapseHint2" class="accordion-collapse collapse" data-bs-parent="#accordionHint2" aria-labelledby="headingHint2">
 <div class="accordion-body">
 <p>After running the second pseudobulk DGE, you can join the two
 <code>DataFrame</code>s of <code>Erythroid3</code> statistics using the
@@ -1438,7 +1438,7 @@ <h4 class="accordion-header" id="headingHint2"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb53">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1521,7 +1521,7 @@ <h3 class="callout-title">Extension challenge 1: Group effects</h3>
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   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
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+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
 <p>It’s important to have multiple samples within each experimental
 group because it helps the batch effect correction algorithm distinguish
@@ -1650,7 +1650,7 @@ <h3 class="callout-title">Key Points</h3>
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   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-04",
-  "datePublished": "2024-10-08"
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diff --git a/instructor/profiles.html b/instructor/profiles.html
index ebd16f7..ba2a072 100644
--- a/instructor/profiles.html
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@@ -370,8 +370,8 @@ <h1>Learner Profiles</h1>
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   "dateCreated": "2024-01-10",
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diff --git a/instructor/reference.html b/instructor/reference.html
index f4ae8c6..e1b9eaf 100644
--- a/instructor/reference.html
+++ b/instructor/reference.html
@@ -413,7 +413,7 @@ <h1>Reference</h1>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/instructor/reference.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-09-24",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
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diff --git a/intro-sce.html b/intro-sce.html
index a486846..65592b9 100644
--- a/intro-sce.html
+++ b/intro-sce.html
@@ -998,7 +998,7 @@ <h3 class="callout-title">Key Points</h3>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/intro-sce.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-07",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
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diff --git a/key-points.html b/key-points.html
index fca6104..1f16efd 100644
--- a/key-points.html
+++ b/key-points.html
@@ -538,8 +538,8 @@ <h2 class="accordion-header" id="flush-headingTwelve">
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   "identifier": "https://ccb-hms.github.io/osca-carpentries/key-points.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
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diff --git a/large_data.html b/large_data.html
index 507a26f..8a9e588 100644
--- a/large_data.html
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@@ -1608,7 +1608,7 @@ <h3 class="callout-title">Key Points</h3>
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   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-07",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
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diff --git a/md5sum.txt b/md5sum.txt
index 5405769..4312f1a 100644
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+++ b/md5sum.txt
@@ -5,7 +5,7 @@
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 "episodes/large_data.Rmd" "e3295a682af24d014423fa82b3f70e6e" "site/built/large_data.md" "2024-10-07"
diff --git a/multi-sample.html b/multi-sample.html
index 9970931..8127ac5 100644
--- a/multi-sample.html
+++ b/multi-sample.html
@@ -543,7 +543,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution1" aria-expanded="false" aria-controls="collapseSolution1">
   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
-<div id="collapseSolution1" class="accordion-collapse collapse" aria-labelledby="headingSolution1" data-bs-parent="#accordionSolution1">
+<div id="collapseSolution1" class="accordion-collapse collapse" data-bs-parent="#accordionSolution1" aria-labelledby="headingSolution1">
 <div class="accordion-body">
 <p>Samples 5 and 6 were from the same “pool” of cells. Looking at the
 documentation for the dataset under <code>?WTChimeraData</code> we see
@@ -606,7 +606,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution2" aria-expanded="false" aria-controls="collapseSolution2">
   <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
-<div id="collapseSolution2" class="accordion-collapse collapse" aria-labelledby="headingSolution2" data-bs-parent="#accordionSolution2">
+<div id="collapseSolution2" class="accordion-collapse collapse" data-bs-parent="#accordionSolution2" aria-labelledby="headingSolution2">
 <div class="accordion-body">
 <p>False. Batch-level data can be retained through confounding with
 experimental factors or poor ability to distinguish experimental effects
@@ -1055,7 +1055,7 @@ <h3 class="callout-title">Challenge</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution3" aria-expanded="false" aria-controls="collapseSolution3">
   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
-<div id="collapseSolution3" class="accordion-collapse collapse" aria-labelledby="headingSolution3" data-bs-parent="#accordionSolution3">
+<div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <div class="accordion-body">
 <p>“logFC” stands for log fold-change. <code>edgeR</code> uses a log2
 convention. Rather than reporting e.g. a 5-fold increase, it’s better to
@@ -1372,7 +1372,7 @@ <h3 class="callout-title">Exercise 1: Heatmaps</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint1" aria-expanded="false" aria-controls="collapseHint1">
   <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 </button>
-<div id="collapseHint1" class="accordion-collapse collapse" aria-labelledby="headingHint1" data-bs-parent="#accordionHint1">
+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>You can simply hand <code>pheatmap()</code> a matrix as its only
 argument. <code>pheatmap()</code> has a million options you can tweak,
@@ -1386,7 +1386,7 @@ <h4 class="accordion-header" id="headingHint1"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
 </button>
-<div id="collapseSolution4" class="accordion-collapse collapse" aria-labelledby="headingSolution4" data-bs-parent="#accordionSolution4">
+<div id="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb52">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1421,7 +1421,7 @@ <h3 class="callout-title">Exercise 2: Model specification and comparison</h3>
 <button class="accordion-button hint-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseHint2" aria-expanded="false" aria-controls="collapseHint2">
   <h4 class="accordion-header" id="headingHint2"> Give me a hint </h4>
 </button>
-<div id="collapseHint2" class="accordion-collapse collapse" aria-labelledby="headingHint2" data-bs-parent="#accordionHint2">
+<div id="collapseHint2" class="accordion-collapse collapse" data-bs-parent="#accordionHint2" aria-labelledby="headingHint2">
 <div class="accordion-body">
 <p>After running the second pseudobulk DGE, you can join the two
 <code>DataFrame</code>s of <code>Erythroid3</code> statistics using the
@@ -1436,7 +1436,7 @@ <h4 class="accordion-header" id="headingHint2"> Give me a hint </h4>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution5" aria-expanded="false" aria-controls="collapseSolution5">
   <h4 class="accordion-header" id="headingSolution5"> Show me the solution </h4>
 </button>
-<div id="collapseSolution5" class="accordion-collapse collapse" aria-labelledby="headingSolution5" data-bs-parent="#accordionSolution5">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb53">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1519,7 +1519,7 @@ <h3 class="callout-title">Extension challenge 1: Group effects</h3>
 <button class="accordion-button solution-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSolution6" aria-expanded="false" aria-controls="collapseSolution6">
   <h4 class="accordion-header" id="headingSolution6"> Show me the solution </h4>
 </button>
-<div id="collapseSolution6" class="accordion-collapse collapse" aria-labelledby="headingSolution6" data-bs-parent="#accordionSolution6">
+<div id="collapseSolution6" class="accordion-collapse collapse" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
 <div class="accordion-body">
 <p>It’s important to have multiple samples within each experimental
 group because it helps the batch effect correction algorithm distinguish
@@ -1648,7 +1648,7 @@ <h3 class="callout-title">Key Points</h3>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/multi-sample.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-10-04",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
   </script><script>
diff --git a/pkgdown.yml b/pkgdown.yml
index 7cbab6b..c2f0199 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,4 +2,4 @@ pandoc: 3.1.11
 pkgdown: 2.1.1
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-10-08T17:30Z
+last_built: 2024-10-10T11:31Z
diff --git a/profiles.html b/profiles.html
index d8aeae1..de3c79f 100644
--- a/profiles.html
+++ b/profiles.html
@@ -370,8 +370,8 @@ <h1>Learner Profiles</h1>
   "url": "https://ccb-hms.github.io/osca-carpentries/profiles.html",
   "identifier": "https://ccb-hms.github.io/osca-carpentries/profiles.html",
   "dateCreated": "2024-01-10",
-  "dateModified": "2024-10-08",
-  "datePublished": "2024-10-08"
+  "dateModified": "2024-10-10",
+  "datePublished": "2024-10-10"
 }
 
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diff --git a/reference.html b/reference.html
index 9867500..4f1ad1c 100644
--- a/reference.html
+++ b/reference.html
@@ -411,7 +411,7 @@ <h1>Reference</h1>
   "identifier": "https://ccb-hms.github.io/osca-carpentries/reference.html",
   "dateCreated": "2024-01-10",
   "dateModified": "2024-09-24",
-  "datePublished": "2024-10-08"
+  "datePublished": "2024-10-10"
 }
 
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