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<meta name="date" content="2025-01-16" />
<meta name="date" content="2025-02-19" />

<title>Finding Markers with Seurat</title>

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<h1 class="title toc-ignore">Finding Markers with Seurat</h1>
<h4 class="date">2025-01-16</h4>
<h4 class="date">2025-02-19</h4>

</div>

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<div class="tab-content">
<div id="summary" class="tab-pane fade in active">
<p>
<strong>Last updated:</strong> 2025-01-16
<strong>Last updated:</strong> 2025-02-19
</p>
<p>
<strong>Checks:</strong> <span
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<p>
The results in this page were generated with repository version
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</p>
<pre><code>
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</code></pre>
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Rmd
</td>
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CD 8 markers
</td>
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html
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2025-01-16
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Rmd
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<td>
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<div id="seurat-object" class="section level2">
<h2>Seurat object</h2>
<p>Load 10x data into a matrix using <code>Read10X()</code>.</p>
<pre class="r"><code>pbmc.data &lt;- Read10X(
data.dir = &quot;data/pbmc3k/filtered_gene_bc_matrices/hg19/&quot;
<pre class="r"><code>work_dir &lt;- rprojroot::find_rstudio_root_file()
data_dir &lt;- paste0(work_dir, &quot;/data/pbmc3k/filtered_gene_bc_matrices/hg19/&quot;)
stopifnot(dir.exists(data_dir))
pbmc.data &lt;- Read10X(
data.dir = data_dir
)</code></pre>
<p>Create the Seurat object using <code>CreateSeuratObject</code>; see
<code>?SeuratObject</code> for more information on the class.</p>
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3 layers present: counts, data, scale.data
2 dimensional reductions calculated: pca, umap</code></pre>
</div>
<div id="known-markers" class="section level2">
<h2>Known markers</h2>
<p>UMAP.</p>
<pre class="r"><code>DimPlot(seurat_obj, label = TRUE)</code></pre>
<p><img src="figure/seurat_markers.Rmd/umap-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>Known markers.</p>
<pre class="r"><code>known_markers &lt;- tibble::tribble(
~Label, ~`Expanded Label`, ~`OBO Ontology ID`, ~Markers,
&quot;Mono&quot;, &quot;Monocyte&quot;, &quot;CL:0000576&quot;, &quot;CTSS, FCN1, NEAT1, LYZ, PSAP, S100A9, AIF1, MNDA, SERPINA1, TYROBP&quot;,
&quot;CD4 T&quot;, &quot;CD4+ T cell&quot;, &quot;CL:0000624&quot;, &quot;IL7R, MAL, LTB, CD4, LDHB, TPT1, TRAC, TMSB10, CD3D, CD3G&quot;,
&quot;CD8 T&quot;, &quot;CD8+ T cell&quot;, &quot;CL:0000625&quot;, &quot;CD8B, CD8A, CD3D, TMSB10, HCST, CD3G, LINC02446, CTSW, CD3E, TRAC&quot;,
&quot;NK&quot;, &quot;natural killer cell&quot;, &quot;CL:0000623&quot;, &quot;NKG7, KLRD1, TYROBP, GNLY, FCER1G, PRF1, CD247, KLRF1, CST7, GZMB&quot;,
&quot;B&quot;, &quot;B cell&quot;, &quot;CL:0000785&quot;, &quot;CD79A, RALGPS2, CD79B, MS4A1, BANK1, CD74, TNFRSF13C, HLA-DQA1, IGHM, MEF2C&quot;,
&quot;other T&quot;, &quot;other T cell&quot;, &quot;CL:0002419&quot;, &quot;CD3D, TRDC, GZMK, KLRB1, NKG7, TRGC2, CST7, LYAR, KLRG1, GZMA&quot;,
&quot;DC&quot;, &quot;dendritic cell&quot;, &quot;CL:0000451&quot;, &quot;CD74, HLA-DPA1, HLA-DPB1, HLA-DQA1, CCDC88A, HLA-DRA, HLA-DMA, CST3, HLA-DQB1, HLA-DRB1&quot;
)

known_markers</code></pre>
<pre><code># A tibble: 7 × 4
Label `Expanded Label` `OBO Ontology ID` Markers
&lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt;
1 Mono Monocyte CL:0000576 CTSS, FCN1, NEAT1, LYZ, PSAP, S…
2 CD4 T CD4+ T cell CL:0000624 IL7R, MAL, LTB, CD4, LDHB, TPT1…
3 CD8 T CD8+ T cell CL:0000625 CD8B, CD8A, CD3D, TMSB10, HCST,…
4 NK natural killer cell CL:0000623 NKG7, KLRD1, TYROBP, GNLY, FCER…
5 B B cell CL:0000785 CD79A, RALGPS2, CD79B, MS4A1, B…
6 other T other T cell CL:0002419 CD3D, TRDC, GZMK, KLRB1, NKG7, …
7 DC dendritic cell CL:0000451 CD74, HLA-DPA1, HLA-DPB1, HLA-D…</code></pre>
<p>Plot CD8 markers.</p>
<pre class="r"><code>known_markers |&gt;
dplyr::filter(Label == &quot;CD8 T&quot;) |&gt;
dplyr::pull(Markers) |&gt;
stringr::str_split(pattern = &quot;, &quot;) |&gt;
unlist() -&gt; cd8_markers

Seurat::FeaturePlot(seurat_obj, features = cd8_markers)</code></pre>
<pre><code>Warning: The following requested variables were not found: LINC02446, TRAC</code></pre>
<p><img src="figure/seurat_markers.Rmd/cd8_markers-1.png" width="768" style="display: block; margin: auto;" /></p>
</div>
<div id="find-all-markers" class="section level2">
<h2>Find all markers</h2>
<p><code>FindAllMarkers()</code> will find markers (differentially
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expression, and the control features are randomly selected from each
bin.</p>
</blockquote>
<p>Arguments:</p>
<ul>
<li><code>object</code> - Seurat object</li>
<li><code>features</code> - A list of vectors of features for expression
programs; each entry should be a vector of feature names</li>
<li><code>pool</code> - List of features to check expression levels
against, defaults to rownames(x = object)</li>
<li><code>nbin</code> - Number of bins of aggregate expression levels
for all analyzed features</li>
<li><code>ctrl</code> - Number of control features selected from the
same bin per analyzed feature</li>
<li><code>k</code> - Use feature clusters returned from DoKMeans</li>
<li><code>assay</code> - Name of assay to use</li>
<li><code>name</code> - Name for the expression programs; will append a
number to the end for each entry in features (eg. if features has three
programs, the results will be stored as name1, name2, name3,
respectively)</li>
<li><code>seed</code> - Set a random seed. If NULL, seed is not
set.</li>
<li><code>search</code> - Search for symbol synonyms for features in
features that don’t match features in object? Searches the HGNC’s gene
names database; see UpdateSymbolList for more details</li>
<li><code>slot</code> - Slot to calculate score values off of. Defaults
to data slot (i.e log-normalized counts)</li>
</ul>
<pre class="r"><code>pbmc_small_markers |&gt;
head(10) |&gt;
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</table>
</div>
</div>
<div id="cd-8" class="section level3">
<h3>CD 8</h3>
<p>Add module score for CD 8 markers.</p>
<pre class="r"><code>cd8_markers_found &lt;- cd8_markers[cd8_markers %in% row.names(seurat_obj@assays$RNA$data)]

AddModuleScore(
object = seurat_obj,
features = cd8_markers_found,
name = &#39;cd8_markers&#39;
) -&gt; seurat_obj

FeaturePlot(
seurat_obj,
features = &quot;cd8_markers1&quot;, label = TRUE, repel = TRUE
)</code></pre>
<p><img src="figure/seurat_markers.Rmd/add_module_score_cd8-1.png" width="672" style="display: block; margin: auto;" /></p>
<br>
<p>
<button type="button" class="btn btn-default btn-workflowr btn-workflowr-sessioninfo" data-toggle="collapse" data-target="#workflowr-sessioninfo" style="display: block;">
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[16] KernSmooth_2.23-24 bslib_0.8.0 htmlwidgets_1.6.4
[19] ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[22] zoo_1.8-12 cachem_1.1.0 whisker_0.4.1
[25] igraph_2.1.2 mime_0.12 lifecycle_1.0.4
[25] igraph_2.1.1 mime_0.12 lifecycle_1.0.4
[28] pkgconfig_2.0.3 Matrix_1.7-0 R6_2.5.1
[31] fastmap_1.2.0 fitdistrplus_1.2-1 shiny_1.9.1
[34] digest_0.6.37 colorspace_2.1-1 patchwork_1.3.0
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[49] compiler_4.4.1 withr_3.0.2 fastDummies_1.7.4
[52] highr_0.11 R.utils_2.12.3 MASS_7.3-60.2
[55] tools_4.4.1 lmtest_0.9-40 httpuv_1.6.15
[58] goftest_1.2-3 R.oo_1.27.0 glue_1.8.0
[58] goftest_1.2-3 R.oo_1.26.0 glue_1.8.0
[61] callr_3.7.6 nlme_3.1-164 promises_1.3.0
[64] grid_4.4.1 Rtsne_0.17 getPass_0.2-4
[67] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3
[70] gtable_0.3.6 spatstat.data_3.1-2 R.methodsS3_1.8.2
[73] tidyr_1.3.1 utf8_1.2.4 spatstat.geom_3.3-3
[76] RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2
[79] pillar_1.9.0 stringr_1.5.1 limma_3.62.1
[79] pillar_1.9.0 stringr_1.5.1 limma_3.62.2
[82] spam_2.11-0 RcppHNSW_0.6.0 later_1.3.2
[85] splines_4.4.1 dplyr_1.1.4 lattice_0.22-6
[88] survival_3.6-4 deldir_2.0-4 tidyselect_1.2.1
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</div>
</div>
</div>
</div>


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