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<!DOCTYPE html>





  
  

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<title>Cell/Spot Analysis</title>





  
  

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          <a href="conversion.html">Converting VoltRon Objects</a>





  
  

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          <a href="ondisk.html">OnDisk-based Analysis Utilities</a>





  
  

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  435
  





  
  

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<h1 class="title toc-ignore">Cell/Spot Analysis</h1>





  
  

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</div>





  
  

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<p><br></p>





  
  

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<div id="xenium-data-analysis" class="section level1">





  
  

  462
  
<h1>Xenium Data Analysis</h1>





  
  

  463
  
<p>VoltRon is an end-to-end spatial omic analysis package which also





  
  

  464
  
supports investigating spatial points in single cell resolution. VoltRon





  
  

  465
  
includes essential built-in functions capable of





  
  

  466
  
<strong>filtering</strong>, <strong>processing</strong> and





  
  

  467
  
<strong>clustering</strong> as well as <strong>visualizing</strong>





  
  

  468
  
spatial datasets with a goal of cell type discovery and annotation.</p>





  
  

  469
  
<p>In this use case, we analyse readouts of the experiments conducted on





  
  

  470
  
example tissue sections analysed by the <a





  
  

  471
  
href="https://www.10xgenomics.com/platforms/xenium">Xenium In Situ</a>





  
  

  472
  
platform. Two tissue sections of 5 <span





  
  

  473
  
class="math inline">\(\mu\)</span>m tickness are derived from a single





  
  

  474
  
formalin-fixed, paraffin-embedded (FFPE) breast cancer tissue block.





  
  

  475
  
More information on the spatial datasets and the study can be also be





  
  

  476
  
found on the <a





  
  

  477
  
href="https://www.biorxiv.org/content/10.1101/2022.10.06.510405v1">BioArxiv





  
  

  478
  
preprint</a>.</p>





  
  

  479
  
<p>You can import these readouts from the <a





  
  

  480
  
href="https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast">10x





  
  

  481
  
Genomics website</a> (specifically, import <strong>In Situ Replicate





  
  

  482
  
1/2</strong>). Alternatively, you can <strong>download a zipped





  
  

  483
  
collection of Xenium readouts</strong> from <a





  
  

  484
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/10X_Xenium_Visium.zip">here</a>.</p>





  
  

  485
  
<p><br></p>





  
  

  486
  
<div id="building-voltron-objects" class="section level2">





  
  

  487
  
<h2>Building VoltRon objects</h2>





  
  

  488
  
<p>VoltRon includes built-in functions for converting readouts of Xenium





  
  

  489
  
experiments into VoltRon objects. The <strong>importXenium</strong>





  
  

  490
  
function locates all readout documents under the output folder of the





  
  

  491
  
Xenium experiment, and forms a VoltRon object. We will import both





  
  

  492
  
Xenium replicates separately, and merge them after some image





  
  

  493
  
manipulation.</p>





  
  

  494
  
<pre class="r watch-out"><code>library(VoltRon)





  
  

  495
  
Xen_R1 &lt;- importXenium(&quot;Xenium_R1/outs&quot;, sample_name = &quot;XeniumR1&quot;, import_molecules = TRUE)





  
  

  496
  
Xen_R2 &lt;- importXenium(&quot;Xenium_R2/outs&quot;, sample_name = &quot;XeniumR2&quot;, import_molecules = TRUE)</code></pre>





  
  

  497
  
<p>Before moving on to the downstream analysis of the imaging-based





  
  

  498
  
data, we can inspect both Xenium images. We use the





  
  

  499
  
<strong>vrImages</strong> function to call and visualize reference





  
  

  500
  
images of all VoltRon objects. Observe that the DAPI image of the second





  
  

  501
  
Xenium replicate is dim, hence we might need to increase the





  
  

  502
  
brightness.</p>





  
  

  503
  
<pre class="r watch-out"><code>vrImages(Xen_R1)





  
  

  504
  
vrImages(Xen_R2)</code></pre>





  
  

  505
  
<table>





  
  

  506
  
<tbody>





  
  

  507
  
<tr style="vertical-align: center">





  
  

  508
  
<td style="width:50%; vertical-align: center">





  
  

  509
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/xeniumr1.png" class="center">





  
  

  510
  
</td>





  
  

  511
  
<td style="width:50%; vertical-align: center">





  
  

  512
  
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/xeniumr2.png" class="center">





  
  

  513
  
</td>





  
  

  514
  
</tr>





  
  

  515
  
</tbody>





  
  

  516
  
</table>





  
  

  517
  
<p><br></p>





  
  

  518
  
<p>We can adjust the brightness of the second Xenium replicate using the





  
  

  519
  
<strong>modulateImage</strong> function where we can change the





  
  

  520
  
brightness and saturation of the reference image of this VoltRon object.





  
  

  521
  
This functionality is optional for VoltRon objects and should be used





  
  

  522
  
when images require further adjustments.</p>





  
  

  523
  
<pre class="r watch-out"><code>Xen_R2 &lt;- modulateImage(Xen_R2, brightness = 800)





  
  

  524
  
vrImages(Xen_R2)</code></pre>





  
  

  525
  
<p><img width="40%" height="40%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/xeniumr2_new.png" class="center"></p>





  
  

  526
  
<p><br></p>





  
  

  527
  
<p>Once both VoltRon objects are created and images are well-tuned, we





  
  

  528
  
can merge these two into a single VoltRon object.</p>





  
  

  529
  
<pre class="r watch-out"><code>Xen_list &lt;- list(Xen_R1, Xen_R2)





  
  

  530
  
Xen_data &lt;- merge(Xen_list[[1]], Xen_list[-1])</code></pre>





  
  

  531
  
<pre><code>VoltRon Object 





  
  

  532
  
XeniumR1: 





  
  

  533
  
  Layers: Section1 





  
  

  534
  
XeniumR2: 





  
  

  535
  
  Layers: Section1 





  
  

  536
  
Assays: Xenium(Main) </code></pre>





  
  

  537
  
<p><br></p>





  
  

  538
  
</div>





  
  

  539
  
<div id="spatial-visualization" class="section level2">





  
  

  540
  
<h2>Spatial Visualization</h2>





  
  

  541
  
<p>With <strong>vrSpatialPlot</strong>, we can visualize Xenium





  
  

  542
  
experiments in both cellular and subcellular context. Since we have not





  
  

  543
  
yet started analyzing raw counts of cells, we can first visualize some





  
  

  544
  
transcripts of interest. We first visualize mRNAs of ACTA2, a marker for





  
  

  545
  
smooth muscle cell actin, and TCF7, an early exhausted t cell marker. We





  
  

  546
  
can interactively select a subset of interest within the tissue section





  
  

  547
  
and visualize the localization of these transcripts. Here we subset a





  
  

  548
  
ductal carcinoma niche, and visualize visualize mRNAs of





  
  

  549
  
<strong>(i)</strong> ACTA2, a marker for smooth muscle cell actin, and





  
  

  550
  
<strong>(ii)</strong> TCF7, an early exhausted t cell marker.</p>





  
  

  551
  
<pre class="r watch-out"><code>Xen_R1_subsetinfo &lt;- subset(Xen_R1, interactive = TRUE)





  
  

  552
  
Xen_R1_subset &lt;- Xen_R1_subsetinfo$subsets[[1]]





  
  

  553
  
vrSpatialPlot(Xen_R1_subset, assay = &quot;Xenium_mol&quot;, group.by = &quot;gene&quot;,





  
  

  554
  
              group.id = c(&quot;ACTA2&quot;, &quot;KRT15&quot;, &quot;TACSTD2&quot;, &quot;CEACAM6&quot;), pt.size = 0.2, legend.pt.size = 5)</code></pre>





  
  

  555
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_transcripts_visualize.png" class="center"></p>





  
  

  556
  
<p>We can also visualize count data of cells in the Xenium replicates.





  
  

  557
  
The behaviour of <strong>vrSpatialFeaturePlot</strong> (and most





  
  

  558
  
plotting functions in VoltRon) depend on the number of assays associated





  
  

  559
  
with the assay type (e.g. Xenium is both cell and subcellular type).





  
  

  560
  
Here, we have two assays, and we visualize two features, hence the





  
  

  561
  
resulting plot would include four panels. Prior to spatial





  
  

  562
  
visualization, we can normalize the counts to correct for count depth of





  
  

  563
  
cells by <strong>(i)</strong> dividing counts with total counts in each





  
  

  564
  
cell, <strong>(ii)</strong> multiply with some constant (default:





  
  

  565
  
10000), and followed by <strong>(iii)</strong> log transformation of the





  
  

  566
  
counts.</p>





  
  

  567
  
<pre class="r watch-out"><code>Xen_data &lt;- normalizeData(Xen_data, sizefactor = 1000)





  
  

  568
  
vrSpatialFeaturePlot(Xen_data, features = c(&quot;ACTA2&quot;, &quot;TCF7&quot;), alpha = 1, pt.size = 0.7)</code></pre>





  
  

  569
  
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_spatialfeature_xenium.png" class="center"></p>





  
  

  570
  
</div>





  
  

  571
  
<div id="processing-and-embedding" class="section level2">





  
  

  572
  
<h2>Processing and Embedding</h2>





  
  

  573
  
<p>Some number of cells in both Xenium replicates might have extremely





  
  

  574
  
low counts. Although cells are detected at these locations, the low





  
  

  575
  
total counts of cells would make it challenging for phenotyping and





  
  

  576
  
clustering these cells. Hence, we remove such cells from the VoltRon





  
  

  577
  
objects.</p>





  
  

  578
  
<pre class="r watch-out"><code>Xen_data &lt;- subset(Xen_data, Count &gt; 5)</code></pre>





  
  

  579
  
<p>VoltRon is capable of reducing dimensionality of datasets using both





  
  

  580
  
PCA and UMAP which we gonna use to build profile-specific neighborhood





  
  

  581
  
graphs and partition the data into cell types.</p>





  
  

  582
  
<pre class="r watch-out"><code>Xen_data &lt;- getPCA(Xen_data, dims = 20)





  
  

  583
  
Xen_data &lt;- getUMAP(Xen_data, dims = 1:20)</code></pre>





  
  

  584
  
<p>We can also visualize the normalized expression of these features on





  
  

  585
  
embedding spaces (e.g. UMAP) using





  
  

  586
  
<strong>vrEmbeddingFeaturePlot</strong> function.</p>





  
  

  587
  
<pre class="r watch-out"><code>vrEmbeddingFeaturePlot(Xen_data, features = c(&quot;LRRC15&quot;, &quot;TCF7&quot;), embedding = &quot;umap&quot;, 





  
  

  588
  
                       pt.size = 0.4)</code></pre>





  
  

  589
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_featureplot_xenium.png" class="center"></p>





  
  

  590
  
<p><br></p>





  
  

  591
  
</div>





  
  

  592
  
<div id="clustering" class="section level2">





  
  

  593
  
<h2>Clustering</h2>





  
  

  594
  
<p>Next, we build neighborhood graphs with the <strong>shared nearest





  
  

  595
  
neighbors (SNN)</strong> of cells which are constructed from





  
  

  596
  
dimensionally reduced gene expression profiles. The function





  
  

  597
  
<strong>getProfileNeighbors</strong> also has an option of building





  
  

  598
  
<strong>k-nearest neighbors (kNN)</strong> graphs.</p>





  
  

  599
  
<pre class="r watch-out"><code>Xen_data &lt;- getProfileNeighbors(Xen_data, dims = 1:20, method = &quot;SNN&quot;)





  
  

  600
  
vrGraphNames(Xen_data)</code></pre>





  
  

  601
  
<pre><code>[1] &quot;SNN&quot;</code></pre>





  
  

  602
  
<p>We can later conduct a clustering of cells using the <strong>leiden’s





  
  

  603
  
method</strong> from the igraph package, which is utilized with the





  
  

  604
  
<strong>getClusters</strong> function.</p>





  
  

  605
  
<pre class="r watch-out"><code>Xen_data &lt;- getClusters(Xen_data, resolution = 1.0, label = &quot;Clusters&quot;, graph = &quot;SNN&quot;)</code></pre>





  
  

  606
  
<p>Now we can label each cell with the associated clustering index and





  
  

  607
  
take a look at the clustering accuracy on the embedding space, and we





  
  

  608
  
can also visualize these clusters on a spatial context.</p>





  
  

  609
  
<pre class="r watch-out"><code>vrEmbeddingPlot(Xen_data, group.by = &quot;Clusters&quot;, embedding = &quot;umap&quot;, 





  
  

  610
  
                pt.size = 0.4, label = TRUE)</code></pre>





  
  

  611
  
<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_embedplot_xenium.png" class="center"></p>





  
  

  612
  
<pre class="r watch-out"><code>vrSpatialPlot(Xen_data, group.by = &quot;Clusters&quot;, pt.size = 0.18, background.color = &quot;black&quot;)</code></pre>





  
  

  613
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_spatial_xenium.png" class="center"></p>





  
  

  614
  
<p><br></p>





  
  

  615
  
</div>





  
  

  616
  
<div id="annotation" class="section level2">





  
  

  617
  
<h2>Annotation</h2>





  
  

  618
  
<p>We can annotate each of these clusters according to their positive





  
  

  619
  
markers across 313 features. One can use the





  
  

  620
  
<strong>FindAllMarkers</strong> from the <a





  
  

  621
  
href="https://satijalab.org/seurat/">Seurat</a> package to pinpoint





  
  

  622
  
these markers by first utilizing the <strong>as.Seurat</strong> function





  
  

  623
  
first on the Xenium assays of the VoltRon object.</p>





  
  

  624
  
<p>For more information on conversion to other packages, please visit





  
  

  625
  
the <a href="conversion.html">Converting VoltRon Objects</a>.</p>





  
  

  626
  
<p>Let us create a new metadata feature from the





  
  

  627
  
<strong>Clusters</strong> column, called <strong>CellType</strong>, we





  
  

  628
  
can insert this new metadata column directly to the object.</p>





  
  

  629
  
<pre class="r watch-out"><code>clusters &lt;- factor(Xen_data$Clusters, levels = sort(unique(Xen_data$Clusters)))





  
  

  630
  
levels(clusters) &lt;- c(&quot;DCIS_1&quot;, 





  
  

  631
  
                      &quot;DCIS_2&quot;, 





  
  

  632
  
                      &quot;CD4_TCells&quot;,





  
  

  633
  
                      &quot;Adipocytes&quot;,





  
  

  634
  
                      &quot;PLD4+_LILRA4+_CD4+_Cells&quot;,





  
  

  635
  
                      &quot;ACTA2_myoepithelial&quot;,





  
  

  636
  
                      &quot;IT_2&quot;,





  
  

  637
  
                      &quot;Macrophages&quot;,





  
  

  638
  
                      &quot;MastCells&quot;,





  
  

  639
  
                      &quot;Bcells&quot;,





  
  

  640
  
                      &quot;StromalCells&quot;,





  
  

  641
  
                      &quot;CD8_TCells&quot;,





  
  

  642
  
                      &quot;CD8_TCells&quot;,





  
  

  643
  
                      &quot;EndothelialCells&quot;,





  
  

  644
  
                      &quot;StromalCells&quot;,





  
  

  645
  
                      &quot;MyelomaCells&quot;,





  
  

  646
  
                      &quot;IT_1&quot;,





  
  

  647
  
                      &quot;IT_2&quot;,





  
  

  648
  
                      &quot;ACTA2_myoepithelial&quot;,





  
  

  649
  
                      &quot;DCIS_2&quot;,





  
  

  650
  
                      &quot;IT_3&quot;,





  
  

  651
  
                      &quot;KRT15_myoepithelial&quot;)





  
  

  652
  
Xen_data$CellType &lt;- as.character(clusters)</code></pre>





  
  

  653
  
<p><strong>vrSpatialPlot</strong> function can visualize multiple types





  
  

  654
  
of metadata columns, and users can change the location of the legends as





  
  

  655
  
well.</p>





  
  

  656
  
<pre class="r watch-out"><code>vrSpatialPlot(Xen_data, group.by = &quot;CellType&quot;, pt.size = 0.13, background.color = &quot;black&quot;, 





  
  

  657
  
              legend.loc = &quot;top&quot;, n.tile = 500)</code></pre>





  
  

  658
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_spatial_xenium_annotated.png" class="center"></p>





  
  

  659
  
<p><br></p>





  
  

  660
  
</div>





  
  

  661
  
</div>





  
  

  662
  
<div id="visium-data-analysis" class="section level1">





  
  

  663
  
<h1>Visium Data Analysis</h1>





  
  

  664
  
<p>Spot-based spatial transcriptomic assays capture spatially-resolved





  
  

  665
  
gene expression profiles that are somewhat closer to single cell





  
  

  666
  
resolution. However, each spot still include a few number of cells that





  
  

  667
  
are likely from a combination of cell types within the tissue of origin.





  
  

  668
  
VoltRon analyzes spot level spatial data sets and even allows selecting





  
  

  669
  
a highly variable subset of features to cluster spots into meaningful





  
  

  670
  
groups of in situ spots for detecting niches of interests</p>





  
  

  671
  
<div id="import-st-data" class="section level2">





  
  

  672
  
<h2>Import ST Data</h2>





  
  

  673
  
<p>For this tutorial we will analyze spot-based transcriptomic assays





  
  

  674
  
from Mouse Brain generated by the <a





  
  

  675
  
href="https://www.10xgenomics.com/products/spatial-gene-expression">Visium</a>





  
  

  676
  
instrument.</p>





  
  

  677
  
<p>You can find and download readouts of all four Visium sections <a





  
  

  678
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Visium/MouseBrainSerialSections.zip">here</a>.





  
  

  679
  
The <strong>Mouse Brain Serial Section 1</strong> datasets can be





  
  

  680
  
downloaded from <a





  
  

  681
  
href="https://www.10xgenomics.com/resources/datasets?menu%5Bproducts.name%5D=Spatial%20Gene%20Expression&amp;query=&amp;page=1&amp;configure%5BhitsPerPage%5D=50&amp;configure%5BmaxValuesPerFacet%5D=1000">here</a>





  
  

  682
  
(specifically, please filter for <strong>Species=Mouse</strong>,





  
  

  683
  
<strong>AnatomicalEntity=brain</strong>, <strong>Chemistry=v1</strong>





  
  

  684
  
and <strong>PipelineVersion=v1.1.0</strong>).</p>





  
  

  685
  
<p>We will now import each of four samples separately and merge them





  
  

  686
  
into one VoltRon object. There are four sections in total given two





  
  

  687
  
serial anterior and serial posterior sections, hence we have <strong>two





  
  

  688
  
tissue blocks each having two layers</strong>.</p>





  
  

  689
  
<pre class="r watch-out"><code>library(VoltRon)





  
  

  690
  
Ant_Sec1 &lt;- importVisium(&quot;Sagittal_Anterior/Section1/&quot;, sample_name = &quot;Anterior1&quot;)





  
  

  691
  
Pos_Sec1 &lt;- importVisium(&quot;Sagittal_Posterior/Section1/&quot;, sample_name = &quot;Posterior1&quot;)





  
  

  692
  





  
  

  693
  
# merge datasets





  
  

  694
  
MBrain_Sec &lt;- merge(Ant_Sec1, Pos_Sec1, samples = c(&quot;Anterior&quot;, &quot;Posterior&quot;))





  
  

  695
  
MBrain_Sec</code></pre>





  
  

  696
  
<pre><code>VoltRon Object 





  
  

  697
  
Anterior: 





  
  

  698
  
  Layers: Section1 





  
  

  699
  
Posterior: 





  
  

  700
  
  Layers: Section1





  
  

  701
  
Assays: Visium(Main) </code></pre>





  
  

  702
  
<p>VoltRon maps metadata features on the spatial images, multiple





  
  

  703
  
features can be provided for all assays/layers associated with the main





  
  

  704
  
assay (Visium).</p>





  
  

  705
  
<pre class="r watch-out"><code>vrSpatialFeaturePlot(MBrain_Sec, features = &quot;Count&quot;, crop = TRUE, alpha = 1, ncol = 2)</code></pre>





  
  

  706
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_visium_firstplot.png" class="center"></p>





  
  

  707
  
<p><br></p>





  
  

  708
  
</div>





  
  

  709
  
<div id="feature-selection" class="section level2">





  
  

  710
  
<h2>Feature Selection</h2>





  
  

  711
  
<p>VoltRon captures the nearly full transcriptome of the Visium data





  
  

  712
  
which then can be filtered from a list of features ranked by their





  
  

  713
  
variance and importance. We use the <strong>variance stabilization





  
  

  714
  
transformation (vst)</strong> on each individual assay using the





  
  

  715
  
<strong>getFeatures</strong> function and combine these ranked list to





  
  

  716
  
capture features important for all assay of the Visium data later with





  
  

  717
  
<strong>getVariableFeatures</strong> function.</p>





  
  

  718
  
<pre class="r watch-out"><code>head(vrFeatures(MBrain_Sec))</code></pre>





  
  

  719
  
<pre><code>[1] &quot;Xkr4&quot;    &quot;Gm1992&quot;  &quot;Gm19938&quot; &quot;Gm37381&quot; &quot;Rp1&quot;     &quot;Sox17&quot;  </code></pre>





  
  

  720
  
<pre class="r watch-out"><code>length(vrFeatures(MBrain_Sec))</code></pre>





  
  

  721
  
<pre><code>[1] 33502</code></pre>





  
  

  722
  
<pre class="r watch-out"><code>MBrain_Sec &lt;- normalizeData(MBrain_Sec)





  
  

  723
  
MBrain_Sec &lt;- getFeatures(MBrain_Sec, n = 3000)





  
  

  724
  
head(vrFeatureData(MBrain_Sec))</code></pre>





  
  

  725
  
<pre><code>                mean          var    adj_var  rank





  
  

  726
  
Xkr4    0.0248608534 0.0249941807 0.02800216 14114





  
  

  727
  
Gm1992  0.0000000000 0.0000000000 0.00000000     0





  
  

  728
  
Gm19938 0.0285714286 0.0322197476 0.03224908 13889





  
  

  729
  
Gm37381 0.0000000000 0.0000000000 0.00000000     0





  
  

  730
  
Rp1     0.0003710575 0.0003710575 0.00000000     0





  
  

  731
  
Sox17   0.1907235622 0.2219629135 0.23715920 10304</code></pre>





  
  

  732
  
<pre class="r watch-out"><code>selected_features &lt;- getVariableFeatures(MBrain_Sec)





  
  

  733
  
head(selected_features, 20)</code></pre>





  
  

  734
  
<pre><code>[1] &quot;Bc1&quot;     &quot;mt-Co1&quot;  &quot;mt-Co3&quot;  &quot;mt-Atp6&quot; &quot;mt-Co2&quot;  &quot;mt-Cytb&quot; &quot;mt-Nd4&quot;  &quot;mt-Nd1&quot;  &quot;mt-Nd2&quot;  





  
  

  735
  
[2] &quot;Fth1&quot;    &quot;Hbb-bs&quot;  &quot;Cst3&quot;    &quot;Gapdh&quot;   &quot;Tmsb4x&quot;  &quot;Mbp&quot;     &quot;Rplp1&quot;   &quot;Ttr&quot;     &quot;Ppia&quot;    





  
  

  736
  
[3] &quot;Ckb&quot;     &quot;mt-Nd3&quot; </code></pre>





  
  

  737
  
</div>





  
  

  738
  
<div id="embedding" class="section level2">





  
  

  739
  
<h2>Embedding</h2>





  
  

  740
  
<p>Now we can learn and visualize PCA and UMAP embeddings on this





  
  

  741
  
smaller number of selected features</p>





  
  

  742
  
<pre class="r watch-out"><code>MBrain_Sec &lt;- getPCA(MBrain_Sec, features = selected_features, dims = 30)





  
  

  743
  
MBrain_Sec &lt;- getUMAP(MBrain_Sec, dims = 1:30)





  
  

  744
  
vrEmbeddingPlot(MBrain_Sec, embedding = &quot;umap&quot;)</code></pre>





  
  

  745
  
<p><img width="65%" height="65%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_visium_umap.png" class="center"></p>





  
  

  746
  
<p><br></p>





  
  

  747
  
</div>





  
  

  748
  
<div id="clustering-1" class="section level2">





  
  

  749
  
<h2>Clustering</h2>





  
  

  750
  
<pre class="r watch-out"><code>MBrain_Sec &lt;- getProfileNeighbors(MBrain_Sec, dims = 1:30, k = 10, method = &quot;SNN&quot;)





  
  

  751
  
vrGraphNames(MBrain_Sec)</code></pre>





  
  

  752
  
<pre><code>[1] &quot;SNN&quot;</code></pre>





  
  

  753
  
<pre class="r watch-out"><code>MBrain_Sec &lt;- getClusters(MBrain_Sec, resolution = 0.5, label = &quot;Clusters&quot;, graph = &quot;SNN&quot;)





  
  

  754
  
vrEmbeddingPlot(MBrain_Sec, embedding = &quot;umap&quot;, group.by = &quot;Clusters&quot;)</code></pre>





  
  

  755
  
<p><img width="65%" height="65%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_visium_umap_clusters.png" class="center"></p>





  
  

  756
  
<p><br></p>





  
  

  757
  
<pre class="r watch-out"><code>vrSpatialPlot(MBrain_Sec, group.by = &quot;Clusters&quot;)</code></pre>





  
  

  758
  
<p><img width="65%" height="65%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_visium_spatial_clusters1.png" class="center"></p>





  
  

  759
  
<p><br></p>





  
  

  760
  
</div>





  
  

  761
  
</div>





  
  

  762
  
<div id="melc-data-analysis" class="section level1">





  
  

  763
  
<h1>MELC Data Analysis</h1>





  
  

  764
  
<p>VoltRon also provides support for imaging based proteomics assays. In





  
  

  765
  
this next use case, we analyze cells characterized by





  
  

  766
  
<strong>multi-epitope ligand cartography (MELC)</strong> with a panel of





  
  

  767
  
44 parameters. We use the already segmented cells on which expression of





  
  

  768
  
<strong>43 protein features</strong> (excluding DAPI) were mapped to





  
  

  769
  
these cells.</p>





  
  

  770
  
<p>We use the segmented cells over microscopy images collected from





  
  

  771
  
<strong>control</strong> and <strong>COVID-19</strong> lung tissues of





  
  

  772
  
donors categorized based on disease durations (<strong>control</strong>,





  
  

  773
  
<strong>acute</strong>, <strong>chronic</strong> and





  
  

  774
  
<strong>prolonged</strong>). Each image is associated with one of few





  
  

  775
  
field of views (FOVs) from a single tissue section of a donor. See <a





  
  

  776
  
href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190732">GSE190732</a>





  
  

  777
  
for more information. You can download the <strong>IFdata.csv</strong>





  
  

  778
  
file and the folder with the <strong>DAPI</strong> images <a





  
  

  779
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/MELC/GSE190732.zip">here</a>.</p>





  
  

  780
  
<p>We import the <strong>protein intensities</strong>,





  
  

  781
  
<strong>metadata</strong> and <strong>coordinates</strong> associated





  
  

  782
  
with segmented cells across FOVs of samples.</p>





  
  

  783
  
<pre class="r watch-out"><code>library(VoltRon)





  
  

  784
  
IFdata &lt;- read.csv(&quot;IFdata.csv&quot;)





  
  

  785
  
data &lt;- IFdata[,c(2:43)]





  
  

  786
  
metadata &lt;- IFdata[,c(&quot;disease_state&quot;, &quot;object_id&quot;, &quot;cluster&quot;, &quot;Clusters&quot;,





  
  

  787
  
                      &quot;SourceID&quot;, &quot;Sample&quot;, &quot;FOV&quot;, &quot;Section&quot;)]





  
  

  788
  
coordinates &lt;- as.matrix(IFdata[,c(&quot;posX&quot;,&quot;posY&quot;)], rownames.force = TRUE)</code></pre>





  
  

  789
  
<p><br></p>





  
  

  790
  
<div id="importing-melc-data" class="section level2">





  
  

  791
  
<h2>Importing MELC data</h2>





  
  

  792
  
<p>Before analyzing MELC assays across FOVs, we should <strong>build a





  
  

  793
  
VoltRon object</strong> for each individual FOV/Section by using the





  
  

  794
  
<strong>formVoltron</strong> function. We then merge these sections to





  
  

  795
  
respective tissue blocks by defining their samples of origins. We can





  
  

  796
  
also define <strong>assay names</strong>, <strong>assay types</strong>





  
  

  797
  
and <strong>sample (i.e. block) names</strong> of these objects.</p>





  
  

  798
  
<pre class="r watch-out"><code>library(dplyr)





  
  

  799
  
library(magick)





  
  

  800
  
vr_list &lt;- list()





  
  

  801
  
sample_metadata &lt;- metadata %&gt;% select(Sample, FOV, Section) %&gt;% distinct()





  
  

  802
  
for(i in 1:nrow(sample_metadata)){





  
  

  803
  
  vrassay &lt;- sample_metadata[i,]





  
  

  804
  
  cells &lt;- rownames(metadata)[metadata$Section == vrassay$Section]





  
  

  805
  
  image &lt;- image_read(paste0(&quot;DAPI/&quot;, vrassay$Sample, &quot;/DAPI_&quot;, vrassay$FOV, &quot;.tif&quot;))





  
  

  806
  
  vr_list[[vrassay$Section]] &lt;- formVoltRon(data = t(data[cells,]), 





  
  

  807
  
                                            metadata = metadata[cells,],





  
  

  808
  
                                            image = image, 





  
  

  809
  
                                            coords = coordinates[cells,],





  
  

  810
  
                                            main.assay = &quot;MELC&quot;, 





  
  

  811
  
                                            assay.type = &quot;cell&quot;,





  
  

  812
  
                                            sample_name = vrassay$Section)





  
  

  813
  
}</code></pre>





  
  

  814
  
<p>Before moving forward with merging FOVs, we should <strong>flip





  
  

  815
  
coordinates</strong> of cells and perhaps also then





  
  

  816
  
<strong>resize</strong> these images. The main reason for this





  
  

  817
  
coordinate flipping is that the y-axis of most digital images are of the





  
  

  818
  
opposite direction to the commonly used coordinate spaces.</p>





  
  

  819
  
<pre class="r watch-out"><code>for(i in 1:nrow(sample_metadata)){





  
  

  820
  
  vrassay &lt;- sample_metadata[i,]





  
  

  821
  
  vr_list[[vrassay$Section]] &lt;- flipCoordinates(vr_list[[vrassay$Section]])





  
  

  822
  
  vr_list[[vrassay$Section]] &lt;- resizeImage(vr_list[[vrassay$Section]], size = 600)





  
  

  823
  
}</code></pre>





  
  

  824
  
<p>Finally, we merge these assays into one VoltRon object. The





  
  

  825
  
<strong>samples</strong> arguement in the merge function determines





  
  

  826
  
which assays are layers of a single tissue sample/block.</p>





  
  

  827
  
<pre class="r watch-out"><code>vr_merged &lt;- merge(vr_list[[1]], vr_list[-1], samples = sample_metadata$Sample)





  
  

  828
  
vr_merged </code></pre>





  
  

  829
  
<pre><code>VoltRon Object 





  
  

  830
  
control_case_3: 





  
  

  831
  
  Layers: Section1 Section2 





  
  

  832
  
control_case_2: 





  
  

  833
  
  Layers: Section1 Section2 





  
  

  834
  
control_case_1: 





  
  

  835
  
  Layers: Section1 Section2 Section3 





  
  

  836
  
acute_case_3: 





  
  

  837
  
  Layers: Section1 Section2 





  
  

  838
  
acute_case_1: 





  
  

  839
  
  Layers: Section1 Section2 





  
  

  840
  
... 





  
  

  841
  
There are 13 samples in total 





  
  

  842
  
Assays: MELC(Main) </code></pre>





  
  

  843
  
<p><br></p>





  
  

  844
  
<p>The prolonged case 4 has two fields of views (FOVs). By subsetting on





  
  

  845
  
the sample of a prolonged case, we can visualize only these two





  
  

  846
  
sections, and visualize the protein expression of CD31 and





  
  

  847
  
Pancytokeratin which are markers of endothelial and epithelial





  
  

  848
  
cells.</p>





  
  

  849
  
<pre class="r watch-out"><code>vr_subset &lt;- subset(vr_merged, samples = &quot;prolonged_case_4&quot;)





  
  

  850
  
g1 &lt;- vrSpatialFeaturePlot(vr_subset, features = c(&quot;CD31&quot;, &quot;Pancytokeratin&quot;), alpha = 1, 





  
  

  851
  
                           pt.size = 0.7, background.color = &quot;black&quot;)</code></pre>





  
  

  852
  
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_spatialfeature.png" class="center"></p>





  
  

  853
  
<p><br></p>





  
  

  854
  
</div>





  
  

  855
  
<div id="dimensionality-reduction" class="section level2">





  
  

  856
  
<h2>Dimensionality Reduction</h2>





  
  

  857
  
<p>We can utilize dimensional reduction of the available protein markers





  
  

  858
  
using the getPCA and getUMAP functions, but now with relatively lower





  
  

  859
  
numbers of principal components which are enough to capture the





  
  

  860
  
information across 44 features.</p>





  
  

  861
  
<pre class="r watch-out"><code>vr_merged &lt;- getPCA(vr_merged, dims = 10)





  
  

  862
  
vr_merged &lt;- getUMAP(vr_merged, dims = 1:10)





  
  

  863
  
vrEmbeddingFeaturePlot(vr_merged, features = c(&quot;CD31&quot;, &quot;Pancytokeratin&quot;), embedding = &quot;umap&quot;)</code></pre>





  
  

  864
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_embedding.png" class="center"></p>





  
  

  865
  
<p><br></p>





  
  

  866
  
</div>





  
  

  867
  
<div id="clustering-2" class="section level2">





  
  

  868
  
<h2>Clustering</h2>





  
  

  869
  
<p>Now we can visualize the clusters across these sections and perhaps





  
  

  870
  
also check for clusters that may reside in only specific disease





  
  

  871
  
conditions.</p>





  
  

  872
  
<pre class="r watch-out"><code># SNN graph and clusters





  
  

  873
  
vr_merged &lt;- getProfileNeighbors(vr_merged, dims = 1:10, k = 10, method = &quot;SNN&quot;)





  
  

  874
  
vrGraphNames(vr_merged)</code></pre>





  
  

  875
  
<pre><code>[1] &quot;SNN&quot;</code></pre>





  
  

  876
  
<pre class="r watch-out"><code>vr_merged &lt;- getClusters(vr_merged, resolution = 0.8, label = &quot;MELC_Clusters&quot;, graph = &quot;SNN&quot;)





  
  

  877
  





  
  

  878
  
# install patchwork package





  
  

  879
  
if (!requireNamespace(&quot;patchwork&quot;, quietly = TRUE))





  
  

  880
  
  install.packages(&quot;patchwork&quot;)





  
  

  881
  
library(patchwork)





  
  

  882
  





  
  

  883
  
# visualize conditions and clusters





  
  

  884
  
vr_merged$Condition &lt;- gsub(&quot;_[0-9]$&quot;, &quot;&quot;, vr_merged$Sample)





  
  

  885
  
g1 &lt;- vrEmbeddingPlot(vr_merged, group.by = c(&quot;Condition&quot;), embedding = &quot;umap&quot;)





  
  

  886
  
g2 &lt;- vrEmbeddingPlot(vr_merged, group.by = c(&quot;MELC_Clusters&quot;), embedding = &quot;umap&quot;, 





  
  

  887
  
                      label = TRUE)





  
  

  888
  
g1 | g2</code></pre>





  
  

  889
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_embeddingclusters.png" class="center"></p>





  
  

  890
  
<p><br></p>





  
  

  891
  
</div>





  
  

  892
  
<div id="visualization-of-markers" class="section level2">





  
  

  893
  
<h2>Visualization of Markers</h2>





  
  

  894
  
<p>VoltRon provides both violin plots (<strong>vrViolinPlot</strong>)





  
  

  895
  
and heatmaps (<strong>vrHeatmapPlot</strong>) to further investigate the





  
  

  896
  
enrichment of markers across newly clustered datasets.





  
  

  897
  
<strong>Note:</strong> the vrHeatmapPlot function would require you to





  
  

  898
  
have the <strong>ComplexHeatmap</strong> package in your namespace.</p>





  
  

  899
  
<pre class="r watch-out"><code># install patchwork package





  
  

  900
  
if (!requireNamespace(&quot;ComplexHeatmap&quot;, quietly = TRUE))





  
  

  901
  
  BiocManager::install(&quot;ComplexHeatmap&quot;)





  
  

  902
  
library(ComplexHeatmap)





  
  

  903
  





  
  

  904
  
# Visualize Markers





  
  

  905
  
vrHeatmapPlot(vr_merged, features = vrFeatures(vr_merged), 





  
  

  906
  
              group.by = &quot;MELC_Clusters&quot;, show_row_names = TRUE)</code></pre>





  
  

  907
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_heatmapclusters.png" class="center"></p>





  
  

  908
  
<p><br></p>





  
  

  909
  
<pre class="r watch-out"><code>vrViolinPlot(vr_merged, features = c(&quot;CD3&quot;, &quot;SMA&quot;, &quot;Pancytokeratin&quot;, &quot;CCR2&quot;), 





  
  

  910
  
             group.by = &quot;MELC_Clusters&quot;, ncol = 2)</code></pre>





  
  

  911
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_violinclusters.png" class="center"></p>





  
  

  912
  
<p><br></p>





  
  

  913
  
</div>





  
  

  914
  
<div id="neighborhood-analysis" class="section level2">





  
  

  915
  
<h2>Neighborhood Analysis</h2>





  
  

  916
  
<p>We use the <strong>vrNeighbourhoodEnrichment</strong> function to





  
  

  917
  
detect cell type pairs that co occur within each others’ neighborhoods.





  
  

  918
  
First, we establish <strong>spatial neighborhood graphs</strong> that





  
  

  919
  
determine the neighbors of each cell on tissue sections.</p>





  
  

  920
  
<p><a





  
  

  921
  
href="https://en.wikipedia.org/wiki/Delaunay_triangulation">Delaunay





  
  

  922
  
tesselations</a> or graphs are commonly used to determine neighbors of





  
  

  923
  
spatial entities. The function <strong>getSpatialNeighbors</strong>





  
  

  924
  
builds a delaunay graph of all assays of a certain type and detects





  
  

  925
  
neighbors of cells in a VoltRon object.</p>





  
  

  926
  
<pre class="r watch-out"><code>vr_merged &lt;- getSpatialNeighbors(vr_merged, method = &quot;delaunay&quot;)</code></pre>





  
  

  927
  
<p>The graph <strong>delaunay</strong>, which we will use for





  
  

  928
  
spatially-aware neightborhood analysis, is now the second graph





  
  

  929
  
available in the VoltRon object along with <strong>SNN</strong>.</p>





  
  

  930
  
<pre class="r watch-out"><code>vrGraphNames(vr_merged)</code></pre>





  
  

  931
  
<pre><code>[1] &quot;SNN&quot;      &quot;delaunay&quot;</code></pre>





  
  

  932
  
<p>Once neighbors are founds, we can apply a <strong>permutation





  
  

  933
  
test</strong> that compares the number of cell type occurances with an





  
  

  934
  
expected number of these occurances under multiple permutations of





  
  

  935
  
labels in the tissue (fixed coordinates but cells are randomly





  
  

  936
  
labelled). A similar approach is used to by several spatial analysis





  
  

  937
  
frameworks and packages (<a





  
  

  938
  
href="https://www.nature.com/articles/nmeth.4391">Schapiro et. al





  
  

  939
  
2017</a>, <a





  
  

  940
  
href="https://www.nature.com/articles/s41592-021-01358-2">Palla et. al





  
  

  941
  
2022</a>).</p>





  
  

  942
  
<p>Here, we will use the original cell type labels annotated by <a





  
  

  943
  
href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9922044/">Mothes et.





  
  

  944
  
al 2023</a>.</p>





  
  

  945
  
<pre class="r watch-out"><code>neighborhood_results &lt;- vrNeighbourhoodEnrichment(vr_merged, group.by = &quot;Clusters&quot;)</code></pre>





  
  

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<p>The neighborhood analysis provides the results of:</p>





  
  

  947
  
<ul>





  
  

  948
  
<li>the <strong>association</strong> tests (whether cell types are





  
  

  949
  
within each other’s neighborhood)</li>





  
  

  950
  
<li>the <strong>segregation</strong> tests (whether cell types are





  
  

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clustered separately)</li>





  
  

  952
  
</ul>





  
  

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<p>between all cell type pairs across each layers and assay.</p>





  
  

  954
  
<p>The number of each cell in a pair in each section is reported to





  
  

  955
  
assess the impact of the results of the test (i.e. low number of





  
  

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abundance in one cell type may indicate low impact).</p>





  
  

  957
  
<pre class="r watch-out"><code>head(neighborhood_results)</code></pre>





  
  

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<div>





  
  

  959
  
<pre><code style="font-size: 10px;">          from_value     to_value   p_assoc   p_segreg p_assoc_adj p_segreg_adj n_from n_to AssayID Assay    Layer         Sample





  
  

  960
  
Assay1.1 CD163+ macs  CD163+ macs 0.0000000 1.00000000      0.0000   1.00000000     41   41  Assay1  MELC Section1 control_case_3





  
  

  961
  
Assay1.2 CD163+ macs CD4+ T cells 0.9380000 0.03300000      0.9980   0.09762866     41   48  Assay1  MELC Section1 control_case_3





  
  

  962
  
Assay1.3 CD163+ macs  CD8+ Tcells 0.8779011 0.04339051      0.9980   0.09762866     41   11  Assay1  MELC Section1 control_case_3





  
  

  963
  
Assay1.4 CD163+ macs     NK cells 0.8190000 0.08700000      0.9980   0.15660000     41   15  Assay1  MELC Section1 control_case_3





  
  

  964
  
Assay1.5 CD163+ macs   endothelia 0.1230000 0.85100000      0.5535   0.95737500     41  139  Assay1  MELC Section1 control_case_3





  
  

  965
  
Assay1.6 CD163+ macs    epithelia 0.9320000 0.03600000      0.9980   0.09762866     41   39  Assay1  MELC Section1 control_case_3</code></pre>





  
  

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</div>





  
  

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<p><br></p>





  
  

  968
  
<pre class="r watch-out"><code>vrNeighbourhoodEnrichmentPlot(neighborhood_results, assay = &quot;Assay1&quot;, type = &quot;assoc&quot;)</code></pre>





  
  

  969
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_neighenrichment.png" class="center"></p>





  
  

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