From e7f4f9e399450f9d61c46b6189ac8e48932ac996 Mon Sep 17 00:00:00 2001
From: GitHub Actions <actions@github.com>
Date: Mon, 16 Sep 2024 18:43:31 +0000
Subject: [PATCH] site deploy

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Source  : fca6e08a0e7980e4f1e2b73f15c4469e58da44e7
Branch  : md-outputs
Author  : GitHub Actions <actions@github.com>
Time    : 2024-09-16 18:42:29 +0000
Message : markdown source builds

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Source  : 6706f65df0d09dbcd6d087356595e504959f0475
Branch  : main
Author  : Andrew Ghazi <6763470+andrewGhazi@users.noreply.github.com>
Time    : 2024-09-16 18:36:13 +0000
Message : Merge pull request #38 from ccb-hms/repetition_exercises

Repetition exercises
---
 aio.html                                   | 240 ++++++++++++++-------
 eda_qc.html                                | 230 ++++++++++++++------
 fig/eda_qc-rendered-unnamed-chunk-21-1.png | Bin 72164 -> 39021 bytes
 fig/eda_qc-rendered-unnamed-chunk-25-1.png | Bin 32695 -> 6343 bytes
 fig/eda_qc-rendered-unnamed-chunk-26-1.png | Bin 11284 -> 72164 bytes
 fig/eda_qc-rendered-unnamed-chunk-27-1.png | Bin 70884 -> 32535 bytes
 fig/eda_qc-rendered-unnamed-chunk-28-1.png | Bin 94034 -> 32695 bytes
 fig/eda_qc-rendered-unnamed-chunk-29-1.png | Bin 19552 -> 11284 bytes
 fig/eda_qc-rendered-unnamed-chunk-31-1.png | Bin 0 -> 94034 bytes
 fig/eda_qc-rendered-unnamed-chunk-32-1.png | Bin 0 -> 19552 bytes
 fig/eda_qc-rendered-unnamed-chunk-32-2.png | Bin 0 -> 85035 bytes
 fig/eda_qc-rendered-unnamed-chunk-33-1.png | Bin 0 -> 70884 bytes
 fig/eda_qc-rendered-unnamed-chunk-33-2.png | Bin 0 -> 65342 bytes
 images.html                                |  22 +-
 instructor/aio.html                        | 240 ++++++++++++++-------
 instructor/eda_qc.html                     | 230 ++++++++++++++------
 instructor/images.html                     |  22 +-
 instructor/large_data.html                 |  12 +-
 large_data.html                            |  12 +-
 md5sum.txt                                 |   2 +-
 pkgdown.yml                                |   2 +-
 21 files changed, 682 insertions(+), 330 deletions(-)
 create mode 100644 fig/eda_qc-rendered-unnamed-chunk-31-1.png
 create mode 100644 fig/eda_qc-rendered-unnamed-chunk-32-1.png
 create mode 100644 fig/eda_qc-rendered-unnamed-chunk-32-2.png
 create mode 100644 fig/eda_qc-rendered-unnamed-chunk-33-1.png
 create mode 100644 fig/eda_qc-rendered-unnamed-chunk-33-2.png

diff --git a/aio.html b/aio.html
index 9fcd092..d1d0551 100644
--- a/aio.html
+++ b/aio.html
@@ -981,7 +981,7 @@ <h3 class="callout-title">Key Points</h3>
 </div>
 </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 2024-09-06 |
+<p>Last updated on 2024-09-16 |
 
         <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">
@@ -1152,6 +1152,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>[1] 2</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. -->
 </div>
 </div>
 </div>
@@ -1441,8 +1442,9 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>We’ve removed empty cells and low-quality cells to be discarded. How
-many cells are we left with at this point?</p>
+<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
+the threshold for outlier calling?</p>
 </div>
 </div>
 </div>
@@ -1453,21 +1455,16 @@ <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
 <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="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="fu">table</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>
+<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>
 </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>
-FALSE  TRUE
- 2437   694 </code></pre>
-</div>
-<p>There are 2437 cells that <em>haven’t</em> been flagged to be
-discarded, so that’s how many we have left.</p>
+<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.
+<!-- This is a direct application of what was just shown --></p>
 </div>
 </div>
 </div>
@@ -1482,7 +1479,7 @@ <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="cb27">
+<div class="codewrapper sourceCode" id="cb26">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1491,14 +1488,14 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb28">
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><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="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-12-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb29">
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-12-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb28">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1511,7 +1508,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb30">
+<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">
@@ -1524,7 +1521,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb31">
+<div class="codewrapper sourceCode" id="cb30">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1581,7 +1578,7 @@ <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="cb33">
+<div class="codewrapper sourceCode" id="cb32">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1595,7 +1592,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb35">
+<div class="codewrapper sourceCode" id="cb34">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1628,7 +1625,7 @@ <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="cb36">
+<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">
@@ -1647,7 +1644,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
   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>
 </div>
-<div class="codewrapper sourceCode" id="cb38">
+<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">
@@ -1661,7 +1658,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb40">
+<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">
@@ -1681,7 +1678,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb41">
+<div class="codewrapper sourceCode" id="cb40">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1715,10 +1712,19 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>Some sophisticated experiments perform additional steps so that they
-can estimate size factors from so-called “spike-ins”. Judging by the
-name, what do you think “spike-ins” are, and what additional steps are
-required to use them?</p>
+<p>Fill in the blanks for normalization that uses simpler library size
+factors instead of deconvolution.</p>
+<div class="codewrapper sourceCode" id="cb42">
+<h3 class="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="cb42-1"><a href="#cb42-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
+<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a></span>
+<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
+<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a></span>
+<span id="cb42-5"><a href="#cb42-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
+<span id="cb42-6"><a href="#cb42-6" tabindex="-1"></a></span>
+<span id="cb42-7"><a href="#cb42-7" tabindex="-1"></a>sce</span></code></pre>
+</div>
 </div>
 </div>
 </div>
@@ -1729,12 +1735,21 @@ <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
 <div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <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
-signal to normalize to. Exotic or synthetic RNA (e.g. soil bacteria RNA
-in a study of human cells) is used in order to avoid confusing spike-in
-RNA with sample RNA. This has the obvious advantage of accounting for
-cell-wise variation, but adds additional sample-preparation work.</p>
+<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">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">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">lib.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>
+</div>
+<p>If you run this chunk, make sure to go back and re-run the
+normalization with deconvolution normalization if you want your work to
+align with the rest of this episode.</p>
 </div>
 </div>
 </div>
@@ -1767,7 +1782,7 @@ <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="cb44">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1785,7 +1800,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-19-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The blue line represents the uninteresting “technical” variance for
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-21-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><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>
@@ -1800,9 +1815,9 @@ <h3 id="selecting-highly-variable-genes">Selecting highly variable genes<a class
 this choice should be guided by prior biological knowledge; for
 instance, we may expect that only about 10% of genes to be
 differentially expressed across our cell populations and hence select
-10% of genes as higly variable (e.g., by setting
+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="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">
@@ -1816,17 +1831,44 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>[1] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919"
 [4] "ENSMUSG00000052187" "ENSMUSG00000048583" "ENSMUSG00000051855"</code></pre>
 </div>
-<div id="discussion1" class="callout discussion">
+<div id="challenge4" class="callout challenge">
 <div class="callout-square">
-<i class="callout-icon" data-feather="message-circle"></i>
+<i class="callout-icon" data-feather="zap"></i>
 </div>
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>Run an internet search for some of the most highly variable genes
-we’ve identified here. See if you can identify the type of protein they
-produce or what sort of process they’re involved in. Do they make
-biological sense to you?</p>
+<p>Imagine you have data that were prepared by three people with varying
+level of experience, which leads to varying technical noise. How can you
+account for this blocking structure when selecting HVGs?</p>
+</div>
+</div>
+</div>
+<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
+<div class="accordion-item">
+<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" 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="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">experimenter</span> <span class="op">=</span> <span class="fu">factor</span><span class="op">(</span><span class="fu">sample</span><span class="op">(</span><span class="fu">c</span><span class="op">(</span><span class="st">"Perry"</span>, <span class="st">"Merry"</span>, <span class="st">"Gary"</span><span class="op">)</span>,</span>
+<span>                          replace <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                          size <span class="op">=</span> <span class="fu">ncol</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>
+<span><span class="va">blocked_variance_df</span> <span class="op">=</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span>, </span>
+<span>                                   block <span class="op">=</span> <span class="va">sce</span><span class="op">$</span><span class="va">experimenter</span><span class="op">)</span></span></code></pre>
+</div>
+<p>Blocked models are evaluated on each block separately then combined.
+If the experimental groups are related in some structured way, it may be
+preferable to use the <code>design</code> argument. See
+<code>?modelGeneVar</code> for more detail.</p>
+</div>
 </div>
 </div>
 </div>
@@ -1867,7 +1909,7 @@ <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="cb46">
+<div class="codewrapper sourceCode" id="cb48">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1896,7 +1938,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 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="cb48">
+<div class="codewrapper sourceCode" id="cb50">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1908,29 +1950,29 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-22-1.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
+<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>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="cb49">
+<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">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-23-1.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
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-26-1.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
 <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="cb50">
+<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>
 </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-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure>
+<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>
 <div class="section level3">
 <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anchor" href="#non-linear-methods"></a>
@@ -1942,7 +1984,7 @@ <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="cb51">
+<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">
@@ -1952,7 +1994,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span></span>
 <span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</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><div class="codewrapper sourceCode" id="cb52">
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb54">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1962,7 +2004,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span></span>
 <span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</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><p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-29-1.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
 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
@@ -1976,7 +2018,7 @@ <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="cb53">
+<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">
@@ -2005,7 +2047,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 run will lead to different plots) and that there are key parameters to
 be set that change the results substantially (e.g., the “perplexity”
 parameter of t-SNE).</p>
-<div id="challenge4" class="callout challenge">
+<div id="challenge5" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
 </div>
@@ -2017,12 +2059,12 @@ <h3 class="callout-title">Challenge</h3>
 </div>
 </div>
 </div>
-<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
+<div id="accordionSolution5" class="accordion challenge-accordion accordion-flush">
 <div class="accordion-item">
-<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 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="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <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
@@ -2075,7 +2117,7 @@ <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="cb55">
+<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">
@@ -2093,7 +2135,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb57">
+<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">
@@ -2101,10 +2143,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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>
 </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>We can explicitly convert this into doublet calls by identifying
+<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
 large outliers for the score within each sample. Here we use the
 “griffiths” method to do so.</p>
-<div class="codewrapper sourceCode" id="cb58">
+<div class="codewrapper sourceCode" id="cb60">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -2119,7 +2161,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>singlet doublet
    2124     313 </code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb60">
+<div class="codewrapper sourceCode" id="cb62">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -2127,28 +2169,28 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-29-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb61">
+<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">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-29-2.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
+<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
 it is a doublet is to check the number of detected genes and total UMI
 counts.</p>
-<div class="codewrapper sourceCode" id="cb62">
+<div class="codewrapper sourceCode" id="cb64">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-30-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb63">
+<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">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-30-2.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
+<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
 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>
@@ -2216,6 +2258,52 @@ <h3 class="callout-title">Exercise 3: PBMC Data</h3>
 </div>
 </div>
 </div>
+<div id="extension-challenge-1-spike-ins" class="callout challenge">
+<div class="callout-square">
+<i class="callout-icon" data-feather="zap"></i>
+</div>
+<div id="extension-challenge-1-spike-ins" class="callout-inner">
+<h3 class="callout-title">Extension challenge 1: Spike-ins</h3>
+<div class="callout-content">
+<p>Some sophisticated experiments perform additional steps so that they
+can estimate size factors from so-called “spike-ins”. Judging by the
+name, what do you think “spike-ins” are, and what additional steps are
+required to use them?</p>
+</div>
+</div>
+</div>
+<div id="accordionSolution6" class="accordion challenge-accordion accordion-flush">
+<div class="accordion-item">
+<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" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
+<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
+signal to normalize against. Exotic (e.g. soil bacteria RNA in a study
+of human cells) or synthetic RNA is used in order to avoid confusing
+spike-in RNA with sample RNA. This has the obvious advantage of
+accounting for cell-wise variation, but can substantially increase the
+amount of sample-preparation work.</p>
+</div>
+</div>
+</div>
+</div>
+<div id="extension-challenge-2-background-research" class="callout discussion">
+<div class="callout-square">
+<i class="callout-icon" data-feather="message-circle"></i>
+</div>
+<div id="extension-challenge-2-background-research" class="callout-inner">
+<h3 class="callout-title">Extension challenge 2: Background research</h3>
+<div class="callout-content">
+<p>Run an internet search for some of the most highly variable genes we
+identified in the feature selection section. See if you can identify the
+type of protein they produce or what sort of process they’re involved
+in. Do they make biological sense to you?</p>
+</div>
+</div>
+</div>
 <div id="keypoints1" class="callout keypoints">
 <div class="callout-square">
 <i class="callout-icon" data-feather="key"></i>
@@ -5111,7 +5199,7 @@ <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>From <code>?MulticoreParam</code> :</p>
 <blockquote>
@@ -5343,7 +5431,7 @@ <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>This code block calculates the exact PCA coordinates. Another thing
 to note: PC vectors are only identified up to a sign flip. We can see
@@ -5754,7 +5842,7 @@ <h3 class="callout-title">Exercise 1: Out of memory representation</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>See the <code>HDF5Array</code> function for reading from HDF5 and the
 <code>writeHDF5Array</code> function for writing to HDF5 from the <em><a href="https://bioconductor.org/packages/3.19/HDF5Array" class="external-link">HDF5Array</a></em>
@@ -5768,7 +5856,7 @@ <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="#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="cb50">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -5847,7 +5935,7 @@ <h3 class="callout-title">Exercise 2: Parallelization</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>Use the function <code>system.time</code> to obtain the runtime of
 each job.</p>
@@ -5860,7 +5948,7 @@ <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="#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="cb56">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
diff --git a/eda_qc.html b/eda_qc.html
index 2a67c2e..e0fce3a 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 2024-09-06 |
+        <p>Last updated on 2024-09-16 |
 
         <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>
 
@@ -500,6 +500,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>[1] 2</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. -->
 </div>
 </div>
 </div>
@@ -781,8 +782,9 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>We’ve removed empty cells and low-quality cells to be discarded. How
-many cells are we left with at this point?</p>
+<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
+the threshold for outlier calling?</p>
 </div>
 </div>
 </div>
@@ -793,21 +795,16 @@ <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
 <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="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="fu">table</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>
+<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>
 </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>
-FALSE  TRUE
- 2437   694 </code></pre>
-</div>
-<p>There are 2437 cells that <em>haven’t</em> been flagged to be
-discarded, so that’s how many we have left.</p>
+<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.
+<!-- This is a direct application of what was just shown --></p>
 </div>
 </div>
 </div>
@@ -821,7 +818,7 @@ <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="cb27">
+<div class="codewrapper sourceCode" id="cb26">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -830,14 +827,14 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb28">
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><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="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-12-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb29">
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-12-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb28">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -850,7 +847,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb30">
+<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">
@@ -863,7 +860,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb31">
+<div class="codewrapper sourceCode" id="cb30">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -918,7 +915,7 @@ <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="cb33">
+<div class="codewrapper sourceCode" id="cb32">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -932,7 +929,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb35">
+<div class="codewrapper sourceCode" id="cb34">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -964,7 +961,7 @@ <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="cb36">
+<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">
@@ -983,7 +980,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
   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>
 </div>
-<div class="codewrapper sourceCode" id="cb38">
+<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">
@@ -997,7 +994,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb40">
+<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">
@@ -1017,7 +1014,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb41">
+<div class="codewrapper sourceCode" id="cb40">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1051,10 +1048,19 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>Some sophisticated experiments perform additional steps so that they
-can estimate size factors from so-called “spike-ins”. Judging by the
-name, what do you think “spike-ins” are, and what additional steps are
-required to use them?</p>
+<p>Fill in the blanks for normalization that uses simpler library size
+factors instead of deconvolution.</p>
+<div class="codewrapper sourceCode" id="cb42">
+<h3 class="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="cb42-1"><a href="#cb42-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
+<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a></span>
+<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
+<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a></span>
+<span id="cb42-5"><a href="#cb42-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
+<span id="cb42-6"><a href="#cb42-6" tabindex="-1"></a></span>
+<span id="cb42-7"><a href="#cb42-7" tabindex="-1"></a>sce</span></code></pre>
+</div>
 </div>
 </div>
 </div>
@@ -1065,12 +1071,21 @@ <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
 <div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <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
-signal to normalize to. Exotic or synthetic RNA (e.g. soil bacteria RNA
-in a study of human cells) is used in order to avoid confusing spike-in
-RNA with sample RNA. This has the obvious advantage of accounting for
-cell-wise variation, but adds additional sample-preparation work.</p>
+<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">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">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">lib.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>
+</div>
+<p>If you run this chunk, make sure to go back and re-run the
+normalization with deconvolution normalization if you want your work to
+align with the rest of this episode.</p>
 </div>
 </div>
 </div>
@@ -1100,7 +1115,7 @@ <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="cb44">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1118,7 +1133,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-19-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The blue line represents the uninteresting “technical” variance for
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-21-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><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>
@@ -1132,9 +1147,9 @@ <h3 id="selecting-highly-variable-genes">Selecting highly variable genes<a class
 this choice should be guided by prior biological knowledge; for
 instance, we may expect that only about 10% of genes to be
 differentially expressed across our cell populations and hence select
-10% of genes as higly variable (e.g., by setting
+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="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">
@@ -1148,17 +1163,44 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>[1] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919"
 [4] "ENSMUSG00000052187" "ENSMUSG00000048583" "ENSMUSG00000051855"</code></pre>
 </div>
-<div id="discussion1" class="callout discussion">
+<div id="challenge4" class="callout challenge">
 <div class="callout-square">
-<i class="callout-icon" data-feather="message-circle"></i>
+<i class="callout-icon" data-feather="zap"></i>
 </div>
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>Run an internet search for some of the most highly variable genes
-we’ve identified here. See if you can identify the type of protein they
-produce or what sort of process they’re involved in. Do they make
-biological sense to you?</p>
+<p>Imagine you have data that were prepared by three people with varying
+level of experience, which leads to varying technical noise. How can you
+account for this blocking structure when selecting HVGs?</p>
+</div>
+</div>
+</div>
+<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
+<div class="accordion-item">
+<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" 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="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">experimenter</span> <span class="op">=</span> <span class="fu">factor</span><span class="op">(</span><span class="fu">sample</span><span class="op">(</span><span class="fu">c</span><span class="op">(</span><span class="st">"Perry"</span>, <span class="st">"Merry"</span>, <span class="st">"Gary"</span><span class="op">)</span>,</span>
+<span>                          replace <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                          size <span class="op">=</span> <span class="fu">ncol</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>
+<span><span class="va">blocked_variance_df</span> <span class="op">=</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span>, </span>
+<span>                                   block <span class="op">=</span> <span class="va">sce</span><span class="op">$</span><span class="va">experimenter</span><span class="op">)</span></span></code></pre>
+</div>
+<p>Blocked models are evaluated on each block separately then combined.
+If the experimental groups are related in some structured way, it may be
+preferable to use the <code>design</code> argument. See
+<code>?modelGeneVar</code> for more detail.</p>
+</div>
 </div>
 </div>
 </div>
@@ -1196,7 +1238,7 @@ <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="cb46">
+<div class="codewrapper sourceCode" id="cb48">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1225,7 +1267,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 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="cb48">
+<div class="codewrapper sourceCode" id="cb50">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1237,29 +1279,29 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-22-1.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
+<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>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="cb49">
+<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">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-23-1.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
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-26-1.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
 <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="cb50">
+<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>
 </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-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure></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>
 <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!)
@@ -1269,7 +1311,7 @@ <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="cb51">
+<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">
@@ -1279,7 +1321,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span></span>
 <span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</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><div class="codewrapper sourceCode" id="cb52">
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb54">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1289,7 +1331,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span></span>
 <span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</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><p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-29-1.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
 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
@@ -1303,7 +1345,7 @@ <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="cb53">
+<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">
@@ -1332,7 +1374,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 run will lead to different plots) and that there are key parameters to
 be set that change the results substantially (e.g., the “perplexity”
 parameter of t-SNE).</p>
-<div id="challenge4" class="callout challenge">
+<div id="challenge5" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
 </div>
@@ -1344,12 +1386,12 @@ <h3 class="callout-title">Challenge</h3>
 </div>
 </div>
 </div>
-<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
+<div id="accordionSolution5" class="accordion challenge-accordion accordion-flush">
 <div class="accordion-item">
-<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 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="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <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
@@ -1397,7 +1439,7 @@ <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="cb55">
+<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">
@@ -1415,7 +1457,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb57">
+<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">
@@ -1423,10 +1465,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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>
 </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>We can explicitly convert this into doublet calls by identifying
+<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
 large outliers for the score within each sample. Here we use the
 “griffiths” method to do so.</p>
-<div class="codewrapper sourceCode" id="cb58">
+<div class="codewrapper sourceCode" id="cb60">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1441,7 +1483,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>singlet doublet
    2124     313 </code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb60">
+<div class="codewrapper sourceCode" id="cb62">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1449,28 +1491,28 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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>
 </div>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-29-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb61">
+<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">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-29-2.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
+<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
 it is a doublet is to check the number of detected genes and total UMI
 counts.</p>
-<div class="codewrapper sourceCode" id="cb62">
+<div class="codewrapper sourceCode" id="cb64">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-30-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb63">
+<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">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-30-2.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
+<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
 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>
@@ -1536,6 +1578,52 @@ <h3 class="callout-title">Exercise 3: PBMC Data</h3>
 </div>
 </div>
 </div>
+<div id="extension-challenge-1-spike-ins" class="callout challenge">
+<div class="callout-square">
+<i class="callout-icon" data-feather="zap"></i>
+</div>
+<div id="extension-challenge-1-spike-ins" class="callout-inner">
+<h3 class="callout-title">Extension challenge 1: Spike-ins</h3>
+<div class="callout-content">
+<p>Some sophisticated experiments perform additional steps so that they
+can estimate size factors from so-called “spike-ins”. Judging by the
+name, what do you think “spike-ins” are, and what additional steps are
+required to use them?</p>
+</div>
+</div>
+</div>
+<div id="accordionSolution6" class="accordion challenge-accordion accordion-flush">
+<div class="accordion-item">
+<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" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
+<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
+signal to normalize against. Exotic (e.g. soil bacteria RNA in a study
+of human cells) or synthetic RNA is used in order to avoid confusing
+spike-in RNA with sample RNA. This has the obvious advantage of
+accounting for cell-wise variation, but can substantially increase the
+amount of sample-preparation work.</p>
+</div>
+</div>
+</div>
+</div>
+<div id="extension-challenge-2-background-research" class="callout discussion">
+<div class="callout-square">
+<i class="callout-icon" data-feather="message-circle"></i>
+</div>
+<div id="extension-challenge-2-background-research" class="callout-inner">
+<h3 class="callout-title">Extension challenge 2: Background research</h3>
+<div class="callout-content">
+<p>Run an internet search for some of the most highly variable genes we
+identified in the feature selection section. See if you can identify the
+type of protein they produce or what sort of process they’re involved
+in. Do they make biological sense to you?</p>
+</div>
+</div>
+</div>
 <div id="keypoints1" class="callout keypoints">
 <div class="callout-square">
 <i class="callout-icon" data-feather="key"></i>
@@ -1662,7 +1750,7 @@ <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": "2024-09-06",
+  "dateModified": "2024-09-16",
   "datePublished": "2024-09-16"
 }
 
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HcmV?d00001

diff --git a/images.html b/images.html
index 44a3001..1191214 100644
--- a/images.html
+++ b/images.html
@@ -388,27 +388,27 @@ <h3 id="eda_qc-figure-6">Figure 6</h3>
 <h3 id="eda_qc-figure-7">Figure 7</h3>
 <figure><img src="fig/eda_qc-rendered-unnamed-chunk-17-1.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-19-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-21-1.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-22-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-10">Figure 10</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-23-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-11">Figure 11</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>
+<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-12">Figure 12</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>
+<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-13">Figure 13</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>
+<figure><img src="fig/eda_qc-rendered-unnamed-chunk-29-1.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-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-15">Figure 15</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-29-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-16">Figure 16</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-29-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-17">Figure 17</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>
+<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-18">Figure 18</h3>
-<figure><img src="fig/eda_qc-rendered-unnamed-chunk-30-2.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-33-2.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>
diff --git a/instructor/aio.html b/instructor/aio.html
index 6a8e825..873cde7 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -984,7 +984,7 @@ <h3 class="callout-title">Key Points</h3>
 </div>
 </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 2024-09-06 |
+<p>Last updated on 2024-09-16 |
 
         <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>
@@ -1156,6 +1156,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>[1] 2</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. -->
 </div>
 </div>
 </div>
@@ -1445,8 +1446,9 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>We’ve removed empty cells and low-quality cells to be discarded. How
-many cells are we left with at this point?</p>
+<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
+the threshold for outlier calling?</p>
 </div>
 </div>
 </div>
@@ -1457,21 +1459,16 @@ <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
 <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="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="fu">table</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>
+<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>
 </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>
-FALSE  TRUE
- 2437   694 </code></pre>
-</div>
-<p>There are 2437 cells that <em>haven’t</em> been flagged to be
-discarded, so that’s how many we have left.</p>
+<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.
+<!-- This is a direct application of what was just shown --></p>
 </div>
 </div>
 </div>
@@ -1486,7 +1483,7 @@ <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="cb27">
+<div class="codewrapper sourceCode" id="cb26">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1495,14 +1492,14 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb28">
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><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="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-12-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb29">
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-12-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb28">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1515,7 +1512,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb30">
+<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">
@@ -1528,7 +1525,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb31">
+<div class="codewrapper sourceCode" id="cb30">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1585,7 +1582,7 @@ <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="cb33">
+<div class="codewrapper sourceCode" id="cb32">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1599,7 +1596,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb35">
+<div class="codewrapper sourceCode" id="cb34">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1632,7 +1629,7 @@ <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="cb36">
+<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">
@@ -1651,7 +1648,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
   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>
 </div>
-<div class="codewrapper sourceCode" id="cb38">
+<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">
@@ -1665,7 +1662,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb40">
+<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">
@@ -1685,7 +1682,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb41">
+<div class="codewrapper sourceCode" id="cb40">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1719,10 +1716,19 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>Some sophisticated experiments perform additional steps so that they
-can estimate size factors from so-called “spike-ins”. Judging by the
-name, what do you think “spike-ins” are, and what additional steps are
-required to use them?</p>
+<p>Fill in the blanks for normalization that uses simpler library size
+factors instead of deconvolution.</p>
+<div class="codewrapper sourceCode" id="cb42">
+<h3 class="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="cb42-1"><a href="#cb42-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
+<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a></span>
+<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
+<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a></span>
+<span id="cb42-5"><a href="#cb42-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
+<span id="cb42-6"><a href="#cb42-6" tabindex="-1"></a></span>
+<span id="cb42-7"><a href="#cb42-7" tabindex="-1"></a>sce</span></code></pre>
+</div>
 </div>
 </div>
 </div>
@@ -1733,12 +1739,21 @@ <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
 <div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <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
-signal to normalize to. Exotic or synthetic RNA (e.g. soil bacteria RNA
-in a study of human cells) is used in order to avoid confusing spike-in
-RNA with sample RNA. This has the obvious advantage of accounting for
-cell-wise variation, but adds additional sample-preparation work.</p>
+<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">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">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">lib.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>
+</div>
+<p>If you run this chunk, make sure to go back and re-run the
+normalization with deconvolution normalization if you want your work to
+align with the rest of this episode.</p>
 </div>
 </div>
 </div>
@@ -1771,7 +1786,7 @@ <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="cb44">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1789,7 +1804,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-19-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The blue line represents the uninteresting “technical” variance for
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-21-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><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>
@@ -1804,9 +1819,9 @@ <h3 id="selecting-highly-variable-genes">Selecting highly variable genes<a class
 this choice should be guided by prior biological knowledge; for
 instance, we may expect that only about 10% of genes to be
 differentially expressed across our cell populations and hence select
-10% of genes as higly variable (e.g., by setting
+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="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">
@@ -1820,17 +1835,44 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>[1] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919"
 [4] "ENSMUSG00000052187" "ENSMUSG00000048583" "ENSMUSG00000051855"</code></pre>
 </div>
-<div id="discussion1" class="callout discussion">
+<div id="challenge4" class="callout challenge">
 <div class="callout-square">
-<i class="callout-icon" data-feather="message-circle"></i>
+<i class="callout-icon" data-feather="zap"></i>
 </div>
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>Run an internet search for some of the most highly variable genes
-we’ve identified here. See if you can identify the type of protein they
-produce or what sort of process they’re involved in. Do they make
-biological sense to you?</p>
+<p>Imagine you have data that were prepared by three people with varying
+level of experience, which leads to varying technical noise. How can you
+account for this blocking structure when selecting HVGs?</p>
+</div>
+</div>
+</div>
+<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
+<div class="accordion-item">
+<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" 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="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">experimenter</span> <span class="op">=</span> <span class="fu">factor</span><span class="op">(</span><span class="fu">sample</span><span class="op">(</span><span class="fu">c</span><span class="op">(</span><span class="st">"Perry"</span>, <span class="st">"Merry"</span>, <span class="st">"Gary"</span><span class="op">)</span>,</span>
+<span>                          replace <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                          size <span class="op">=</span> <span class="fu">ncol</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>
+<span><span class="va">blocked_variance_df</span> <span class="op">=</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span>, </span>
+<span>                                   block <span class="op">=</span> <span class="va">sce</span><span class="op">$</span><span class="va">experimenter</span><span class="op">)</span></span></code></pre>
+</div>
+<p>Blocked models are evaluated on each block separately then combined.
+If the experimental groups are related in some structured way, it may be
+preferable to use the <code>design</code> argument. See
+<code>?modelGeneVar</code> for more detail.</p>
+</div>
 </div>
 </div>
 </div>
@@ -1871,7 +1913,7 @@ <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="cb46">
+<div class="codewrapper sourceCode" id="cb48">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1900,7 +1942,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 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="cb48">
+<div class="codewrapper sourceCode" id="cb50">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1912,29 +1954,29 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-22-1.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
+<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>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="cb49">
+<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">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-23-1.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
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-26-1.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
 <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="cb50">
+<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>
 </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-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure>
+<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>
 <div class="section level3">
 <h3 id="non-linear-methods">Non-linear methods<a class="anchor" aria-label="anchor" href="#non-linear-methods"></a>
@@ -1946,7 +1988,7 @@ <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="cb51">
+<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">
@@ -1956,7 +1998,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span></span>
 <span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</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><div class="codewrapper sourceCode" id="cb52">
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb54">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1966,7 +2008,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span></span>
 <span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</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><p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-29-1.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
 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
@@ -1980,7 +2022,7 @@ <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="cb53">
+<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">
@@ -2009,7 +2051,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 run will lead to different plots) and that there are key parameters to
 be set that change the results substantially (e.g., the “perplexity”
 parameter of t-SNE).</p>
-<div id="challenge4" class="callout challenge">
+<div id="challenge5" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
 </div>
@@ -2021,12 +2063,12 @@ <h3 class="callout-title">Challenge</h3>
 </div>
 </div>
 </div>
-<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
+<div id="accordionSolution5" class="accordion challenge-accordion accordion-flush">
 <div class="accordion-item">
-<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 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="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <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
@@ -2079,7 +2121,7 @@ <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="cb55">
+<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">
@@ -2097,7 +2139,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb57">
+<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">
@@ -2105,10 +2147,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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>
 </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>We can explicitly convert this into doublet calls by identifying
+<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
 large outliers for the score within each sample. Here we use the
 “griffiths” method to do so.</p>
-<div class="codewrapper sourceCode" id="cb58">
+<div class="codewrapper sourceCode" id="cb60">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -2123,7 +2165,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>singlet doublet
    2124     313 </code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb60">
+<div class="codewrapper sourceCode" id="cb62">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -2131,28 +2173,28 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-29-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb61">
+<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">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-29-2.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
+<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
 it is a doublet is to check the number of detected genes and total UMI
 counts.</p>
-<div class="codewrapper sourceCode" id="cb62">
+<div class="codewrapper sourceCode" id="cb64">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-30-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb63">
+<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">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-30-2.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
+<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
 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>
@@ -2220,6 +2262,52 @@ <h3 class="callout-title">Exercise 3: PBMC Data</h3>
 </div>
 </div>
 </div>
+<div id="extension-challenge-1-spike-ins" class="callout challenge">
+<div class="callout-square">
+<i class="callout-icon" data-feather="zap"></i>
+</div>
+<div id="extension-challenge-1-spike-ins" class="callout-inner">
+<h3 class="callout-title">Extension challenge 1: Spike-ins</h3>
+<div class="callout-content">
+<p>Some sophisticated experiments perform additional steps so that they
+can estimate size factors from so-called “spike-ins”. Judging by the
+name, what do you think “spike-ins” are, and what additional steps are
+required to use them?</p>
+</div>
+</div>
+</div>
+<div id="accordionSolution6" class="accordion challenge-accordion accordion-flush">
+<div class="accordion-item">
+<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" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
+<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
+signal to normalize against. Exotic (e.g. soil bacteria RNA in a study
+of human cells) or synthetic RNA is used in order to avoid confusing
+spike-in RNA with sample RNA. This has the obvious advantage of
+accounting for cell-wise variation, but can substantially increase the
+amount of sample-preparation work.</p>
+</div>
+</div>
+</div>
+</div>
+<div id="extension-challenge-2-background-research" class="callout discussion">
+<div class="callout-square">
+<i class="callout-icon" data-feather="message-circle"></i>
+</div>
+<div id="extension-challenge-2-background-research" class="callout-inner">
+<h3 class="callout-title">Extension challenge 2: Background research</h3>
+<div class="callout-content">
+<p>Run an internet search for some of the most highly variable genes we
+identified in the feature selection section. See if you can identify the
+type of protein they produce or what sort of process they’re involved
+in. Do they make biological sense to you?</p>
+</div>
+</div>
+</div>
 <div id="keypoints1" class="callout keypoints">
 <div class="callout-square">
 <i class="callout-icon" data-feather="key"></i>
@@ -5118,7 +5206,7 @@ <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>From <code>?MulticoreParam</code> :</p>
 <blockquote>
@@ -5350,7 +5438,7 @@ <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>This code block calculates the exact PCA coordinates. Another thing
 to note: PC vectors are only identified up to a sign flip. We can see
@@ -5761,7 +5849,7 @@ <h3 class="callout-title">Exercise 1: Out of memory representation</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>See the <code>HDF5Array</code> function for reading from HDF5 and the
 <code>writeHDF5Array</code> function for writing to HDF5 from the <em><a href="https://bioconductor.org/packages/3.19/HDF5Array" class="external-link">HDF5Array</a></em>
@@ -5775,7 +5863,7 @@ <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="#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="cb50">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -5854,7 +5942,7 @@ <h3 class="callout-title">Exercise 2: Parallelization</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>Use the function <code>system.time</code> to obtain the runtime of
 each job.</p>
@@ -5867,7 +5955,7 @@ <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="#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="cb56">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
diff --git a/instructor/eda_qc.html b/instructor/eda_qc.html
index fb847e9..b7aa206 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 2024-09-06 |
+        <p>Last updated on 2024-09-16 |
 
         <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>
 
@@ -502,6 +502,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>[1] 2</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. -->
 </div>
 </div>
 </div>
@@ -783,8 +784,9 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>We’ve removed empty cells and low-quality cells to be discarded. How
-many cells are we left with at this point?</p>
+<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
+the threshold for outlier calling?</p>
 </div>
 </div>
 </div>
@@ -795,21 +797,16 @@ <h4 class="accordion-header" id="headingSolution2"> Show me the solution </h4>
 </button>
 <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="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="fu">table</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>
+<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>
 </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>
-FALSE  TRUE
- 2437   694 </code></pre>
-</div>
-<p>There are 2437 cells that <em>haven’t</em> been flagged to be
-discarded, so that’s how many we have left.</p>
+<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.
+<!-- This is a direct application of what was just shown --></p>
 </div>
 </div>
 </div>
@@ -823,7 +820,7 @@ <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="cb27">
+<div class="codewrapper sourceCode" id="cb26">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -832,14 +829,14 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb28">
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-12-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><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="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-12-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb29">
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-12-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb28">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -852,7 +849,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb30">
+<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">
@@ -865,7 +862,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb31">
+<div class="codewrapper sourceCode" id="cb30">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -920,7 +917,7 @@ <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="cb33">
+<div class="codewrapper sourceCode" id="cb32">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -934,7 +931,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb35">
+<div class="codewrapper sourceCode" id="cb34">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -966,7 +963,7 @@ <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="cb36">
+<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">
@@ -985,7 +982,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
   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>
 </div>
-<div class="codewrapper sourceCode" id="cb38">
+<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">
@@ -999,7 +996,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb40">
+<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">
@@ -1019,7 +1016,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 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="cb41">
+<div class="codewrapper sourceCode" id="cb40">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1053,10 +1050,19 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>Some sophisticated experiments perform additional steps so that they
-can estimate size factors from so-called “spike-ins”. Judging by the
-name, what do you think “spike-ins” are, and what additional steps are
-required to use them?</p>
+<p>Fill in the blanks for normalization that uses simpler library size
+factors instead of deconvolution.</p>
+<div class="codewrapper sourceCode" id="cb42">
+<h3 class="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="cb42-1"><a href="#cb42-1" tabindex="-1"></a>____ <span class="ot">&lt;-</span> <span class="fu">____SizeFactors</span>(sce)</span>
+<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a></span>
+<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a><span class="fu">sizeFactors</span>(sce) <span class="ot">&lt;-</span> ____</span>
+<span id="cb42-4"><a href="#cb42-4" tabindex="-1"></a></span>
+<span id="cb42-5"><a href="#cb42-5" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">____</span>(sce)</span>
+<span id="cb42-6"><a href="#cb42-6" tabindex="-1"></a></span>
+<span id="cb42-7"><a href="#cb42-7" tabindex="-1"></a>sce</span></code></pre>
+</div>
 </div>
 </div>
 </div>
@@ -1067,12 +1073,21 @@ <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
 </button>
 <div id="collapseSolution3" class="accordion-collapse collapse" data-bs-parent="#accordionSolution3" aria-labelledby="headingSolution3">
 <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
-signal to normalize to. Exotic or synthetic RNA (e.g. soil bacteria RNA
-in a study of human cells) is used in order to avoid confusing spike-in
-RNA with sample RNA. This has the obvious advantage of accounting for
-cell-wise variation, but adds additional sample-preparation work.</p>
+<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">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">sizeFactors</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">lib.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>
+</div>
+<p>If you run this chunk, make sure to go back and re-run the
+normalization with deconvolution normalization if you want your work to
+align with the rest of this episode.</p>
 </div>
 </div>
 </div>
@@ -1102,7 +1117,7 @@ <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="cb44">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1120,7 +1135,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-19-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><p>The blue line represents the uninteresting “technical” variance for
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-21-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><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>
@@ -1134,9 +1149,9 @@ <h3 id="selecting-highly-variable-genes">Selecting highly variable genes<a class
 this choice should be guided by prior biological knowledge; for
 instance, we may expect that only about 10% of genes to be
 differentially expressed across our cell populations and hence select
-10% of genes as higly variable (e.g., by setting
+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="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">
@@ -1150,17 +1165,44 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>[1] "ENSMUSG00000055609" "ENSMUSG00000052217" "ENSMUSG00000069919"
 [4] "ENSMUSG00000052187" "ENSMUSG00000048583" "ENSMUSG00000051855"</code></pre>
 </div>
-<div id="discussion1" class="callout discussion">
+<div id="challenge4" class="callout challenge">
 <div class="callout-square">
-<i class="callout-icon" data-feather="message-circle"></i>
+<i class="callout-icon" data-feather="zap"></i>
 </div>
 <div class="callout-inner">
 <h3 class="callout-title">Challenge</h3>
 <div class="callout-content">
-<p>Run an internet search for some of the most highly variable genes
-we’ve identified here. See if you can identify the type of protein they
-produce or what sort of process they’re involved in. Do they make
-biological sense to you?</p>
+<p>Imagine you have data that were prepared by three people with varying
+level of experience, which leads to varying technical noise. How can you
+account for this blocking structure when selecting HVGs?</p>
+</div>
+</div>
+</div>
+<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
+<div class="accordion-item">
+<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" 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="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">experimenter</span> <span class="op">=</span> <span class="fu">factor</span><span class="op">(</span><span class="fu">sample</span><span class="op">(</span><span class="fu">c</span><span class="op">(</span><span class="st">"Perry"</span>, <span class="st">"Merry"</span>, <span class="st">"Gary"</span><span class="op">)</span>,</span>
+<span>                          replace <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                          size <span class="op">=</span> <span class="fu">ncol</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>
+<span><span class="va">blocked_variance_df</span> <span class="op">=</span> <span class="fu">modelGeneVar</span><span class="op">(</span><span class="va">sce</span>, </span>
+<span>                                   block <span class="op">=</span> <span class="va">sce</span><span class="op">$</span><span class="va">experimenter</span><span class="op">)</span></span></code></pre>
+</div>
+<p>Blocked models are evaluated on each block separately then combined.
+If the experimental groups are related in some structured way, it may be
+preferable to use the <code>design</code> argument. See
+<code>?modelGeneVar</code> for more detail.</p>
+</div>
 </div>
 </div>
 </div>
@@ -1198,7 +1240,7 @@ <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="cb46">
+<div class="codewrapper sourceCode" id="cb48">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1227,7 +1269,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 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="cb48">
+<div class="codewrapper sourceCode" id="cb50">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1239,29 +1281,29 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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-22-1.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
+<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>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="cb49">
+<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">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-23-1.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
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-26-1.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
 <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="cb50">
+<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>
 </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-24-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure></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>
 <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!)
@@ -1271,7 +1313,7 @@ <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="cb51">
+<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">
@@ -1281,7 +1323,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span></span>
 <span><span class="fu">plotTSNE</span><span class="op">(</span><span class="va">sce</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><div class="codewrapper sourceCode" id="cb52">
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb54">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1291,7 +1333,7 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <span></span>
 <span><span class="fu">plotUMAP</span><span class="op">(</span><span class="va">sce</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><p>It is easy to over-interpret t-SNE and UMAP plots. We note that the
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-29-1.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
 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
@@ -1305,7 +1347,7 @@ <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="cb53">
+<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">
@@ -1334,7 +1376,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 run will lead to different plots) and that there are key parameters to
 be set that change the results substantially (e.g., the “perplexity”
 parameter of t-SNE).</p>
-<div id="challenge4" class="callout challenge">
+<div id="challenge5" class="callout challenge">
 <div class="callout-square">
 <i class="callout-icon" data-feather="zap"></i>
 </div>
@@ -1346,12 +1388,12 @@ <h3 class="callout-title">Challenge</h3>
 </div>
 </div>
 </div>
-<div id="accordionSolution4" class="accordion challenge-accordion accordion-flush">
+<div id="accordionSolution5" class="accordion challenge-accordion accordion-flush">
 <div class="accordion-item">
-<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 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="collapseSolution4" class="accordion-collapse collapse" data-bs-parent="#accordionSolution4" aria-labelledby="headingSolution4">
+<div id="collapseSolution5" class="accordion-collapse collapse" data-bs-parent="#accordionSolution5" aria-labelledby="headingSolution5">
 <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
@@ -1399,7 +1441,7 @@ <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="cb55">
+<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">
@@ -1417,7 +1459,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <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>
 </div>
-<div class="codewrapper sourceCode" id="cb57">
+<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">
@@ -1425,10 +1467,10 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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>
 </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>We can explicitly convert this into doublet calls by identifying
+<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
 large outliers for the score within each sample. Here we use the
 “griffiths” method to do so.</p>
-<div class="codewrapper sourceCode" id="cb58">
+<div class="codewrapper sourceCode" id="cb60">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1443,7 +1485,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <pre class="output" tabindex="0"><code>singlet doublet
    2124     313 </code></pre>
 </div>
-<div class="codewrapper sourceCode" id="cb60">
+<div class="codewrapper sourceCode" id="cb62">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
 </h3>
 <pre class="downlit sourceCode r">
@@ -1451,28 +1493,28 @@ <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i
 <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>
 </div>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-29-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb61">
+<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">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-29-2.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
+<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
 it is a doublet is to check the number of detected genes and total UMI
 counts.</p>
-<div class="codewrapper sourceCode" id="cb62">
+<div class="codewrapper sourceCode" id="cb64">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-30-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><div class="codewrapper sourceCode" id="cb63">
+<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">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-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-30-2.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
+<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
 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>
@@ -1538,6 +1580,52 @@ <h3 class="callout-title">Exercise 3: PBMC Data</h3>
 </div>
 </div>
 </div>
+<div id="extension-challenge-1-spike-ins" class="callout challenge">
+<div class="callout-square">
+<i class="callout-icon" data-feather="zap"></i>
+</div>
+<div id="extension-challenge-1-spike-ins" class="callout-inner">
+<h3 class="callout-title">Extension challenge 1: Spike-ins</h3>
+<div class="callout-content">
+<p>Some sophisticated experiments perform additional steps so that they
+can estimate size factors from so-called “spike-ins”. Judging by the
+name, what do you think “spike-ins” are, and what additional steps are
+required to use them?</p>
+</div>
+</div>
+</div>
+<div id="accordionSolution6" class="accordion challenge-accordion accordion-flush">
+<div class="accordion-item">
+<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" data-bs-parent="#accordionSolution6" aria-labelledby="headingSolution6">
+<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
+signal to normalize against. Exotic (e.g. soil bacteria RNA in a study
+of human cells) or synthetic RNA is used in order to avoid confusing
+spike-in RNA with sample RNA. This has the obvious advantage of
+accounting for cell-wise variation, but can substantially increase the
+amount of sample-preparation work.</p>
+</div>
+</div>
+</div>
+</div>
+<div id="extension-challenge-2-background-research" class="callout discussion">
+<div class="callout-square">
+<i class="callout-icon" data-feather="message-circle"></i>
+</div>
+<div id="extension-challenge-2-background-research" class="callout-inner">
+<h3 class="callout-title">Extension challenge 2: Background research</h3>
+<div class="callout-content">
+<p>Run an internet search for some of the most highly variable genes we
+identified in the feature selection section. See if you can identify the
+type of protein they produce or what sort of process they’re involved
+in. Do they make biological sense to you?</p>
+</div>
+</div>
+</div>
 <div id="keypoints1" class="callout keypoints">
 <div class="callout-square">
 <i class="callout-icon" data-feather="key"></i>
@@ -1664,7 +1752,7 @@ <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": "2024-09-06",
+  "dateModified": "2024-09-16",
   "datePublished": "2024-09-16"
 }
 
diff --git a/instructor/images.html b/instructor/images.html
index 722cfd8..5f7136b 100644
--- a/instructor/images.html
+++ b/instructor/images.html
@@ -390,27 +390,27 @@ <h3 id="eda_qc-figure-6">Figure 6</h3>
 <h3 id="eda_qc-figure-7">Figure 7</h3>
 <figure><img src="../fig/eda_qc-rendered-unnamed-chunk-17-1.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-19-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-21-1.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-22-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-10">Figure 10</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-23-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-11">Figure 11</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>
+<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-12">Figure 12</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>
+<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-13">Figure 13</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>
+<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-29-1.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-28-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-15">Figure 15</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-29-1.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-16">Figure 16</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-29-2.png" style="display: block; margin: auto;" class="figure mx-auto d-block"></figure><hr>
+<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-17">Figure 17</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>
+<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-18">Figure 18</h3>
-<figure><img src="../fig/eda_qc-rendered-unnamed-chunk-30-2.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-33-2.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>
diff --git a/instructor/large_data.html b/instructor/large_data.html
index 8514606..024878c 100644
--- a/instructor/large_data.html
+++ b/instructor/large_data.html
@@ -748,7 +748,7 @@ <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>From <code>?MulticoreParam</code> :</p>
 <blockquote>
@@ -976,7 +976,7 @@ <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>
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-<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>This code block calculates the exact PCA coordinates. Another thing
 to note: PC vectors are only identified up to a sign flip. We can see
@@ -1379,7 +1379,7 @@ <h3 class="callout-title">Exercise 1: Out of memory representation</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>
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+<div id="collapseHint1" class="accordion-collapse collapse" data-bs-parent="#accordionHint1" aria-labelledby="headingHint1">
 <div class="accordion-body">
 <p>See the <code>HDF5Array</code> function for reading from HDF5 and the
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   <h4 class="accordion-header" id="headingSolution3"> Show me the solution </h4>
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 <div class="accordion-body">
 <div class="codewrapper sourceCode" id="cb50">
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@@ -1472,7 +1472,7 @@ <h3 class="callout-title">Exercise 2: Parallelization</h3>
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   <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>Use the function <code>system.time</code> to obtain the runtime of
 each job.</p>
@@ -1485,7 +1485,7 @@ <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="#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">
 <div class="codewrapper sourceCode" id="cb56">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
diff --git a/large_data.html b/large_data.html
index 7a45c79..50166f9 100644
--- a/large_data.html
+++ b/large_data.html
@@ -746,7 +746,7 @@ <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>From <code>?MulticoreParam</code> :</p>
 <blockquote>
@@ -974,7 +974,7 @@ <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>This code block calculates the exact PCA coordinates. Another thing
 to note: PC vectors are only identified up to a sign flip. We can see
@@ -1377,7 +1377,7 @@ <h3 class="callout-title">Exercise 1: Out of memory representation</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>See the <code>HDF5Array</code> function for reading from HDF5 and the
 <code>writeHDF5Array</code> function for writing to HDF5 from the <em><a href="https://bioconductor.org/packages/3.19/HDF5Array" class="external-link">HDF5Array</a></em>
@@ -1391,7 +1391,7 @@ <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="#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="cb50">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
@@ -1470,7 +1470,7 @@ <h3 class="callout-title">Exercise 2: Parallelization</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>Use the function <code>system.time</code> to obtain the runtime of
 each job.</p>
@@ -1483,7 +1483,7 @@ <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="#collapseSolution4" aria-expanded="false" aria-controls="collapseSolution4">
   <h4 class="accordion-header" id="headingSolution4"> Show me the solution </h4>
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 <div class="codewrapper sourceCode" id="cb56">
 <h3 class="code-label">R<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
diff --git a/md5sum.txt b/md5sum.txt
index b9507b3..5642182 100644
--- a/md5sum.txt
+++ b/md5sum.txt
@@ -5,7 +5,7 @@
 "index.md" "495939ddd3f110be3bbcd49b60f4a7ce" "site/built/index.md" "2024-09-06"
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-"episodes/eda_qc.Rmd" "1e88f395d30778f4526532deea43eb03" "site/built/eda_qc.md" "2024-09-06"
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 "episodes/cell_type_annotation.Rmd" "66af56b730aaa88e937bc1743afb471a" "site/built/cell_type_annotation.md" "2024-09-08"
 "episodes/multi-sample.Rmd" "2d38d9903358ea8a8067abd82a1f1f54" "site/built/multi-sample.md" "2024-09-08"
 "episodes/large_data.Rmd" "b9710492c6792ea435778c4e42f27e02" "site/built/large_data.md" "2024-09-09"
diff --git a/pkgdown.yml b/pkgdown.yml
index b7f7ccf..fad866b 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,4 +2,4 @@ pandoc: 3.1.11
 pkgdown: 2.1.0
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-09-16T17:04Z
+last_built: 2024-09-16T18:43Z