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Expand Down Expand Up @@ -457,7 +462,7 @@ <h2 class="anchored" data-anchor-id="contact">Contact</h2>
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Expand Down Expand Up @@ -165,7 +170,7 @@ <h2 class="anchored" data-anchor-id="single-cell-rna-seq-analysis">Single Cell R
</ul>
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<p>Updated: 18-01-2024 at 17:22:21.</p>
<p>Updated: 23-01-2024 at 11:35:27.</p>
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41 changes: 29 additions & 12 deletions docs/labs/bioc/bioc_01_qc.html
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Expand Down Expand Up @@ -202,7 +207,7 @@ <h1 class="title"><i class="fa-solid fa-clipboard-list" aria-label="clipboard-li
<div>
<div class="quarto-title-meta-heading">Published</div>
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<p class="date">16-Jan-2024</p>
<p class="date">23-Jan-2024</p>
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Expand Down Expand Up @@ -330,7 +335,7 @@ <h2 data-number="1" class="anchored" data-anchor-id="meta-qc_data"><span class="
</section>
<section id="meta-qc_collate" class="level2" data-number="2">
<h2 data-number="2" class="anchored" data-anchor-id="meta-qc_collate"><span class="header-section-number">2</span> Collate</h2>
<p>We can now load the expression matrices and merge them into a single object. Each analysis workflow (Seurat, Scater, Scanpy, etc) has its own way of storing data. We will add dataset labels as <strong>cell.ids</strong> just in case you have overlapping barcodes between the datasets. After that we add a column <strong>Chemistry</strong> in the metadata for plotting later on.</p>
<p>We can now merge them objects into a single object. Each analysis workflow (Seurat, Scater, Scanpy, etc) has its own way of storing data. We will add dataset labels as <strong>cell.ids</strong> just in case you have overlapping barcodes between the datasets. After that we add a column <strong>type</strong> in the metadata to define covid and ctrl samples.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>sce <span class="ot">&lt;-</span> <span class="fu">SingleCellExperiment</span>(<span class="at">assays =</span> <span class="fu">list</span>(<span class="at">counts =</span> <span class="fu">cbind</span>(cov<span class="fl">.1</span>, cov<span class="fl">.15</span>, cov<span class="fl">.17</span>, ctrl<span class="fl">.5</span>, ctrl<span class="fl">.13</span>, ctrl<span class="fl">.14</span>)))</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">dim</span>(sce)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
Expand All @@ -349,8 +354,8 @@ <h2 data-number="2" class="anchored" data-anchor-id="meta-qc_collate"><span clas
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="fu">gc</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> used (Mb) gc trigger (Mb) max used (Mb)
Ncells 10216587 545.7 17147474 915.8 13915408 743.2
Vcells 44612623 340.4 94446392 720.6 83350999 636.0</code></pre>
Ncells 10216383 545.7 17147170 915.8 13915194 743.2
Vcells 44612100 340.4 94440822 720.6 83350476 636.0</code></pre>
</div>
</div>
<p>Here is how the count matrix and the metadata look like for every cell.</p>
Expand Down Expand Up @@ -475,7 +480,7 @@ <h2 data-number="4" class="anchored" data-anchor-id="meta-qc_plotqc"><span class
</div>
</div>
</div>
<p>As you can see, there is quite some difference in quality for the 4 datasets, with for instance the covid_15 sample having fewer cells with many detected genes and more mitochondrial content. As the ribosomal proteins are highly expressed they will make up a larger proportion of the transcriptional landscape when fewer of the lowly expressed genes are detected. And we can plot the different QC-measures as scatter plots.</p>
<p>As you can see, there is quite some difference in quality for the 4 datasets, with for instance the covid_15 and covid_16 samples having fewer cells with many detected genes and more mitochondrial content. As the ribosomal proteins are highly expressed they will make up a larger proportion of the transcriptional landscape when fewer of the lowly expressed genes are detected. We can also plot the different QC-measures as scatter plots.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plotColData</span>(sce, <span class="at">x =</span> <span class="st">"total"</span>, <span class="at">y =</span> <span class="st">"detected"</span>, <span class="at">colour_by =</span> <span class="st">"sample"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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Expand Down Expand Up @@ -594,7 +599,7 @@ <h3 data-number="5.3" class="anchored" data-anchor-id="meta-qc_filter_plot"><spa
</section>
<section id="meta-qc_filter_genes" class="level3" data-number="5.4">
<h3 data-number="5.4" class="anchored" data-anchor-id="meta-qc_filter_genes"><span class="header-section-number">5.4</span> Filter genes</h3>
<p>As the level of expression of mitochondrial and MALAT1 genes are judged as mainly technical, it can be wise to remove them from the dataset before any further analysis.</p>
<p>As the level of expression of mitochondrial and MALAT1 genes are judged as mainly technical, it can be wise to remove them from the dataset before any further analysis. In this case we will also remove the HB genes.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">dim</span>(sce.filt)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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Expand All @@ -610,11 +615,11 @@ <h3 data-number="5.4" class="anchored" data-anchor-id="meta-qc_filter_genes"><sp
<span id="cb32-8"><a href="#cb32-8" aria-hidden="true" tabindex="-1"></a><span class="co"># sce.filt &lt;- sce.filt[ ! grepl("^RP[SL]", rownames(sce.filt)), ]</span></span>
<span id="cb32-9"><a href="#cb32-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-10"><a href="#cb32-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Filter Hemoglobin gene</span></span>
<span id="cb32-11"><a href="#cb32-11" aria-hidden="true" tabindex="-1"></a>sce.filt <span class="ot">&lt;-</span> sce.filt[<span class="sc">!</span><span class="fu">grepl</span>(<span class="st">"^HB[^(P)]"</span>, <span class="fu">rownames</span>(sce.filt)), ]</span>
<span id="cb32-11"><a href="#cb32-11" aria-hidden="true" tabindex="-1"></a>sce.filt <span class="ot">&lt;-</span> sce.filt[<span class="sc">!</span><span class="fu">grepl</span>(<span class="st">"^HB[^(PES)]"</span>, <span class="fu">rownames</span>(sce.filt)), ]</span>
<span id="cb32-12"><a href="#cb32-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb32-13"><a href="#cb32-13" aria-hidden="true" tabindex="-1"></a><span class="fu">dim</span>(sce.filt)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 18183 6023</code></pre>
<pre><code>[1] 18186 6023</code></pre>
</div>
</div>
</section>
Expand All @@ -636,7 +641,7 @@ <h2 data-number="6" class="anchored" data-anchor-id="meta-qc_sex"><span class="h
<span id="cb34-10"><a href="#cb34-10" aria-hidden="true" tabindex="-1"></a> <span class="st">"description"</span>, <span class="st">"gene_biotype"</span>, <span class="st">"chromosome_name"</span>, <span class="st">"start_position"</span></span>
<span id="cb34-11"><a href="#cb34-11" aria-hidden="true" tabindex="-1"></a>), <span class="at">mart =</span> mart, <span class="at">useCache =</span> F))</span>
<span id="cb34-12"><a href="#cb34-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb34-13"><a href="#cb34-13" aria-hidden="true" tabindex="-1"></a><span class="fu">write.csv</span>(genes_table, <span class="at">file =</span> <span class="st">"data/results/genes_table.csv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<span id="cb34-13"><a href="#cb34-13" aria-hidden="true" tabindex="-1"></a><span class="fu">write.csv</span>(genes_table, <span class="at">file =</span> <span class="st">"data/covid/results/genes_table.csv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>genes_file <span class="ot">&lt;-</span> <span class="fu">file.path</span>(path_results, <span class="st">"genes_table.csv"</span>)</span>
Expand Down Expand Up @@ -679,7 +684,19 @@ <h2 data-number="6" class="anchored" data-anchor-id="meta-qc_sex"><span class="h
</div>
</div>
</div>
<p>Here, we can see clearly that we have two males and 4 females, can you see which samples they are? Do you think this will cause any problems for downstream analysis? Discuss with your group: what would be the best way to deal with this type of sex bias?</p>
<div class="callout callout-style-default callout-note callout-titled">
<div class="callout-header d-flex align-content-center">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<div class="callout-title-container flex-fill">
Discuss
</div>
</div>
<div class="callout-body-container callout-body">
<p>Here, we can see clearly that we have three males and five females, can you see which samples they are? Do you think this will cause any problems for downstream analysis? Discuss with your group: what would be the best way to deal with this type of sex bias?</p>
</div>
</div>
</section>
<section id="meta-qc_cellcycle" class="level2" data-number="7">
<h2 data-number="7" class="anchored" data-anchor-id="meta-qc_cellcycle"><span class="header-section-number">7</span> Cell cycle state</h2>
Expand Down Expand Up @@ -790,7 +807,7 @@ <h2 data-number="8" class="anchored" data-anchor-id="meta-qc_doublet"><span clas
<div class="sourceCode cell-code" id="cb46"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>sce.filt <span class="ot">&lt;-</span> sce.filt[, sce.filt<span class="sc">$</span>scDblFinder.score <span class="sc">&lt;</span> <span class="dv">2</span>]</span>
<span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a><span class="fu">dim</span>(sce.filt)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 18183 6023</code></pre>
<pre><code>[1] 18186 6023</code></pre>
</div>
</div>
</section>
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Expand Down Expand Up @@ -509,22 +514,22 @@ <h2 data-number="1" class="anchored" data-anchor-id="meta-int_prep"><span class=
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a><span class="fu">lapply</span>(scelist, dim)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<pre><code>[[1]]
[1] 923 454
[1] 923 500

[[2]]
[1] 611 454
[1] 611 500

[[3]]
[1] 1111 454
[1] 1111 500

[[4]]
[1] 1067 454
[1] 1067 500

[[5]]
[1] 1203 454
[1] 1203 500

[[6]]
[1] 1108 454</code></pre>
[1] 1108 500</code></pre>
</div>
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<p>INTEG_R5:</p>
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