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<title>ROI Analysis</title>





  
  

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          <a href="pixelanalysis.html">Pixels (Image Only) Analysis</a>





  
  

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          <a href="importingdata.html">Importing Spatial Data</a>





  
  

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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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  405
  
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  432
  





  
  

  433
  
<div id="header">





  
  

  434
  





  
  

  435
  





  
  

  436
  





  
  

  437
  
<h1 class="title toc-ignore">ROI Analysis</h1>





  
  

  438
  





  
  

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</div>





  
  

  440
  





  
  

  441
  





  
  

  442
  
<style>





  
  

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.title{





  
  

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  display: none;





  
  

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}





  
  

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body {





  
  

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  text-align: justify





  
  

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}





  
  

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.center {





  
  

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table, th, td {





  
  

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<style type="text/css">





  
  

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</style>





  
  

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<p><br></p>





  
  

  467
  
<div id="roi-analysis" class="section level1">





  
  

  468
  
<h1>ROI Analysis</h1>





  
  

  469
  
<p>VoltRon is capable of analyzing readouts from distinct spatial





  
  

  470
  
technologies including <strong>segmentation (ROI)-based transciptomics





  
  

  471
  
assays</strong> that capture large polygonic regions on tissue sections.





  
  

  472
  
VoltRon recognizes such readouts including ones from commercially





  
  

  473
  
available tools and allows users to implement a workflow similar to ones





  
  

  474
  
conducted on bulk RNA-Seq datasets. In this tutorial, we will analyze





  
  

  475
  
morphological images and gene expression profiles provided by the





  
  

  476
  
readouts of the <a





  
  

  477
  
href="https://nanostring.com/products/geomx-digital-spatial-profiler/geomx-dsp-overview/">Nanostring’s





  
  

  478
  
GeoMx Digital Spatial Profiler</a> platform, a high-plex spatial





  
  

  479
  
profiling technology which produces segmentation-based protein and RNA





  
  

  480
  
assays.</p>





  
  

  481
  
<p>In this use case, <strong>eight tissue micro array (TMA) tissue





  
  

  482
  
sections</strong> were fitted into the scan area of the slide loaded on





  
  

  483
  
the GeoMx DSP instrument. These sections were cut from <strong>control





  
  

  484
  
and COVID-19 lung tissues</strong> of donors categorized based on





  
  

  485
  
disease durations (acute and prolonged). See <a





  
  

  486
  
href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190732">GSE190732</a>





  
  

  487
  
for more information.</p>





  
  

  488
  
<p>You can download these tutorial files from here:</p>





  
  

  489
  
<table>





  
  

  490
  
<tr>





  
  

  491
  
<th>





  
  

  492
  
File





  
  

  493
  
</th>





  
  

  494
  
<th>





  
  

  495
  
Link





  
  

  496
  
</th>





  
  

  497
  
</tr>





  
  

  498
  
<tr>





  
  

  499
  
<td>





  
  

  500
  
Counts





  
  

  501
  
</td>





  
  

  502
  
<td>





  
  

  503
  
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/DCC-20230427.zip">DDC





  
  

  504
  
files</a>





  
  

  505
  
</td>





  
  

  506
  
</tr>





  
  

  507
  
<tr>





  
  

  508
  
<td>





  
  

  509
  
GeoMx Human Whole Transcriptome Atlas





  
  

  510
  
</td>





  
  

  511
  
<td>





  
  

  512
  
<a href="https://nanostring.com/wp-content/uploads/Hs_R_NGS_WTA_v1.0.pkc_.zip">Human





  
  

  513
  
RNA WTA for NGS</a>





  
  

  514
  
</td>





  
  

  515
  
</tr>





  
  

  516
  
<tr>





  
  

  517
  
<td>





  
  

  518
  
Segment Summary





  
  

  519
  
</td>





  
  

  520
  
<td>





  
  

  521
  
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/segmentSummary.csv">





  
  

  522
  
ROI Metadata file </a>





  
  

  523
  
</td>





  
  

  524
  
</tr>





  
  

  525
  
<tr>





  
  

  526
  
<td>





  
  

  527
  
Morphology Image





  
  

  528
  
</td>





  
  

  529
  
<td>





  
  

  530
  
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/Lu1A1B5umtrueexp.tif">





  
  

  531
  
Image file </a>





  
  

  532
  
</td>





  
  

  533
  
</tr>





  
  

  534
  
<tr>





  
  

  535
  
<td>





  
  

  536
  
OME.TIFF Image





  
  

  537
  
</td>





  
  

  538
  
<td>





  
  

  539
  
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/Lu1A1B5umtrueexp.ome.tiff">





  
  

  540
  
OME.TIFF file </a>





  
  

  541
  
</td>





  
  

  542
  
</tr>





  
  

  543
  
<tr>





  
  

  544
  
<td>





  
  

  545
  
OME.TIFF Image (XML)





  
  

  546
  
</td>





  
  

  547
  
<td>





  
  

  548
  
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/Lu1A1B5umtrueexp.ome.tiff.xml" download target="_blank">





  
  

  549
  
OME.TIFF (XML) file </a>





  
  

  550
  
</td>





  
  

  551
  
</tr>





  
  

  552
  
</table>





  
  

  553
  
<p>





  
  

  554
  
</p>





  
  

  555
  
<p>We now import the GeoMx data, and start analyzing 87 user selected





  
  

  556
  
segments (i.e. region of interests, <strong>ROI</strong>) to check





  
  

  557
  
spatial localization of signals. The <strong>importGeoMx</strong>





  
  

  558
  
function requires:</p>





  
  

  559
  
<ul>





  
  

  560
  
<li>The path to the Digital Count Conversion file,





  
  

  561
  
<strong>dcc.path</strong>, and Probe Kit Configuration file,





  
  

  562
  
<strong>pkc.file</strong>, which are both provided as the output of the





  
  

  563
  
<a





  
  

  564
  
href="https://emea.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/nanostring-geomxr-ngs-pipeline.html">GeoMx





  
  

  565
  
NGS Pipeline</a>.</li>





  
  

  566
  
<li>The path the to the metadata file, <strong>summarySegment</strong>,





  
  

  567
  
and the specific excel sheet that the metadata is found,





  
  

  568
  
<strong>summarySegmentSheetName</strong>, the path to the main





  
  

  569
  
morphology <strong>image</strong> and the original <strong>ome.tiff





  
  

  570
  
(xml)</strong> file, all of which are provided and imported from the DSP





  
  

  571
  
Control Center. Please see <a





  
  

  572
  
href="https://nanostring.com/support-documents/geomx-dsp-data-analysis-user-manual/">GeoMx





  
  

  573
  
DSP Data Analysis User Manual</a> for more information.</li>





  
  

  574
  
</ul>





  
  

  575
  
<pre class="r watch-out"><code>library(VoltRon)





  
  

  576
  
library(xlsx)





  
  

  577
  
GeoMxR1 &lt;- importGeoMx(dcc.path = &quot;DCC-20230427/&quot;, 





  
  

  578
  
                       pkc.file = &quot;Hs_R_NGS_WTA_v1.0.pkc&quot;,





  
  

  579
  
                       summarySegment = &quot;segmentSummary.csv&quot;,





  
  

  580
  
                       image = &quot;Lu1A1B5umtrueexp.tif&quot;,





  
  

  581
  
                       ome.tiff = &quot;Lu1A1B5umtrueexp.ome.tiff.xml&quot;,





  
  

  582
  
                       sample_name = &quot;GeoMxR1&quot;)</code></pre>





  
  

  583
  
<p>We can import the GeoMx ROI segments from the





  
  

  584
  
<strong>Lu1A1B5umtrueexp.ome.tiff</strong> image file directly by





  
  

  585
  
replacing the .xml file with the .ome.tiff file in the





  
  

  586
  
<strong>ome.tiff</strong> argument. Note that you need to call the





  
  

  587
  
<strong>RBioFormats</strong> library. If you are getting a <strong>java





  
  

  588
  
error</strong> when running importGeoMx, try increasing the maximum heap





  
  

  589
  
size by supplying the <strong>-Xmx</strong> parameter. Run the code





  
  

  590
  
below before rerunning <strong>importGeoMx</strong> again.</p>





  
  

  591
  
<pre class="r watch-out"><code>options(java.parameters = &quot;-Xmx4g&quot;)





  
  

  592
  
library(RBioFormats)</code></pre>





  
  

  593
  
<p><br></p>





  
  

  594
  
<div id="quality-control" class="section level2">





  
  

  595
  
<h2>Quality Control</h2>





  
  

  596
  
<p>Once the GeoMx data is imported, we can start off with examining key





  
  

  597
  
quality control measures and statistics on each segment to investigate





  
  

  598
  
each individual ROI such as sequencing saturation and the number of





  
  

  599
  
cells (nuclei) within each segment. VoltRon also provides the total





  
  

  600
  
number of unique transcripts per ROI and stores in the metadata.</p>





  
  

  601
  
<pre class="r watch-out"><code>library(ggplot2)





  
  

  602
  
vrBarPlot(GeoMxR1, 





  
  

  603
  
          features = c(&quot;Count&quot;, &quot;Nuclei.count&quot;, &quot;Sequencing.saturation&quot;),





  
  

  604
  
          x.label = &quot;ROI.name&quot;, group.by = &quot;ROI.type&quot;) + 





  
  

  605
  
  theme(axis.text.x = element_text(size = 3))</code></pre>





  
  

  606
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_barplot.png" class="center"></p>





  
  

  607
  
<p>For measuring the quality of individual ROIs, we can add a new





  
  

  608
  
metadata column, called <strong>CountPerNuclei</strong>, to check the





  
  

  609
  
average quality of cells per ROI. It seems some number of ROIs with low





  
  

  610
  
counts per nuclei also have low sequencing saturation.</p>





  
  

  611
  
<pre class="r watch-out"><code>GeoMxR1$CountPerNuclei &lt;- GeoMxR1$Count/GeoMxR1$Nuclei.count





  
  

  612
  
vrBarPlot(GeoMxR1, 





  
  

  613
  
          features = c(&quot;Count&quot;, &quot;Nuclei.count&quot;, 





  
  

  614
  
                       &quot;Sequencing.saturation&quot;, &quot;CountPerNuclei&quot;),





  
  

  615
  
          x.label = &quot;ROI.name&quot;, group.by = &quot;ROI.type&quot;, ncol = 2) + 





  
  

  616
  
  theme(axis.text.x = element_text(size = 5))</code></pre>





  
  

  617
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_barplot2.png" class="center"></p>





  
  

  618
  
<p><br></p>





  
  

  619
  
</div>





  
  

  620
  
<div id="processing" class="section level2">





  
  

  621
  
<h2>Processing</h2>





  
  

  622
  
<p>We can now filter ROIs based on our earlier observation of them





  
  

  623
  
having low count per nuclei where some also have low sequencing





  
  

  624
  
saturation.</p>





  
  

  625
  
<pre class="r watch-out"><code># Filter for count per nuclei





  
  

  626
  
GeoMxR1 &lt;- subset(GeoMxR1, subset = CountPerNuclei &gt; 500)</code></pre>





  
  

  627
  
<p>We then filter genes with low counts by extracting the count matrix





  
  

  628
  
and putting aside all genes whose maximum count across all 87 ROIs are





  
  

  629
  
less than 10.</p>





  
  

  630
  
<pre class="r watch-out"><code>GeoMxR1_data &lt;- vrData(GeoMxR1, norm = FALSE)





  
  

  631
  
feature_ind &lt;- apply(GeoMxR1_data, 1, function(x) max(x) &gt; 10)





  
  

  632
  
selected_features &lt;- vrFeatures(GeoMxR1)[feature_ind]





  
  

  633
  
GeoMxR1_lessfeatures &lt;- subset(GeoMxR1, features = selected_features)</code></pre>





  
  

  634
  
<p>VoltRon is capable of normalizing data provided by a diverse set of





  
  

  635
  
spatial technologies, including the quantile normalization method





  
  

  636
  
suggested by the <a





  
  

  637
  
href="https://nanostring.com/support-documents/geomx-dsp-data-analysis-user-manual/">GeoMx





  
  

  638
  
DSP Data Analysis User Manual</a> which scales the ROI profiles to the





  
  

  639
  
third quartile followed by the geometric mean of all third quartiles





  
  

  640
  
multipled to the scaled profile.</p>





  
  

  641
  
<pre class="r watch-out"><code>GeoMxR1 &lt;- normalizeData(GeoMxR1, method = &quot;Q3Norm&quot;)</code></pre>





  
  

  642
  
<p><br></p>





  
  

  643
  
</div>





  
  

  644
  
<div id="interactive-subsetting" class="section level2">





  
  

  645
  
<h2>Interactive Subsetting</h2>





  
  

  646
  
<p>Spatially informed genomic technologies heavily depend on image





  
  

  647
  
manipulation as images provide an additional set of information. Hence,





  
  

  648
  
VoltRon incorporates several interactive built-in utilities. One such





  
  

  649
  
functionality allows manipulating images of VoltRon assays where users





  
  

  650
  
can interactively choose subsets of images. However, we first resize the





  
  

  651
  
morphology image by providing the width of the new image (thus height





  
  

  652
  
will be reduced to preserve the aspect ratio).</p>





  
  

  653
  
<pre class="r watch-out"><code># resizing the image





  
  

  654
  
# GeoMxR1 &lt;- resizeImage(GeoMxR1, size = 4000)</code></pre>





  
  

  655
  
<p>VoltRon provides <strong>a mini Shiny app</strong> for subsetting





  
  

  656
  
spatial points of a VoltRon object by using the image as a reference.





  
  

  657
  
This app is particularly useful when multiple tissue sections were





  
  

  658
  
fitted to a scan area of a slide, such as the one from GeoMx DSP





  
  

  659
  
instrument. We use <strong>interactive = TRUE</strong> option in the





  
  

  660
  
subset function to call the mini Shiny app, and select bounding boxes of





  
  

  661
  
each newly created subset. <strong>Please continue until all eight





  
  

  662
  
sections are selected and subsetted</strong>.</p>





  
  

  663
  
<pre class="r watch-out"><code>GeoMxR1_subset &lt;- subset(GeoMxR1, interactive = TRUE)</code></pre>





  
  

  664
  
<p><img width="85%" height="85%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/interactivesubset.gif" class="center"></p>





  
  

  665
  
<p>We can now merge the list of subsets, or samples, each associated





  
  

  666
  
with one of eight sections. You can provide a list of names for the





  
  

  667
  
newly subsetted samples.</p>





  
  

  668
  
<pre class="r watch-out"><code>GeoMxR1_subset_list &lt;- GeoMxR1_subset$subsets





  
  

  669
  
GeoMxR1 &lt;- merge(GeoMxR1_subset_list[[1]], GeoMxR1_subset_list[-1])





  
  

  670
  
GeoMxR1</code></pre>





  
  

  671
  
<pre><code>VoltRon Object 





  
  

  672
  
prolonged case 4: 





  
  

  673
  
  Layers: Section1 





  
  

  674
  
acute case 3: 





  
  

  675
  
  Layers: Section1 





  
  

  676
  
control case 2: 





  
  

  677
  
  Layers: Section1 





  
  

  678
  
acute case 1: 





  
  

  679
  
  Layers: Section1 





  
  

  680
  
acute case 2: 





  
  

  681
  
  Layers: Section1 





  
  

  682
  
... 





  
  

  683
  
There are 8 samples in total 





  
  

  684
  
Assays: GeoMx(Main) </code></pre>





  
  

  685
  
<p><br></p>





  
  

  686
  
<p>You may also save the selected image subsets and reproduce the





  
  

  687
  
interactive subsetting operation for later use.</p>





  
  

  688
  
<pre class="r watch-out"><code>samples &lt;- c(&quot;prolonged case 4&quot;, &quot;acute case 3&quot;, &quot;control case 2&quot;, 





  
  

  689
  
             &quot;acute case 1&quot;, &quot;acute case 2&quot;, &quot;prolonged case 5&quot;, 





  
  

  690
  
             &quot;prolonged case 3&quot;, &quot;control case 1&quot;)





  
  

  691
  
subset_info_list &lt;- GeoMxR1_subset$subset_info_list[[1]]





  
  

  692
  
GeoMxR1_subset_list &lt;- list()





  
  

  693
  
for(i in 1:length(subset_info_list)){





  
  

  694
  
  GeoMxR1_subset_list[[i]]  &lt;- subset(GeoMxR1, image = subset_info_list[i])





  
  

  695
  
  GeoMxR1_subset_list[[i]] &lt;- samples[i]





  
  

  696
  
}





  
  

  697
  
GeoMxR1 &lt;- merge(GeoMxR1_subset_list[[1]], GeoMxR1_subset_list[-1])</code></pre>





  
  

  698
  
<p><br></p>





  
  

  699
  
</div>





  
  

  700
  
<div id="visualization" class="section level2">





  
  

  701
  
<h2>Visualization</h2>





  
  

  702
  
<p>We will now select sections of interests from the VoltRon object, and





  
  

  703
  
visualize ROIs and features for each of these sections.</p>





  
  

  704
  
<p>The function <strong>vrSpatialPlot</strong> plots categorical





  
  

  705
  
attributes associated with ROIs. In this case, we will visualize types





  
  

  706
  
of ROIs that were labelled and annotated during ROI selection.</p>





  
  

  707
  
<pre class="r watch-out"><code>GeoMxR1_subset &lt;- subset(GeoMxR1, sample = c(&quot;prolonged case 4&quot;,&quot;acute case 3&quot;))





  
  

  708
  
vrSpatialPlot(GeoMxR1_subset, group.by = &quot;ROI.type&quot;, ncol = 3, alpha = 0.6)</code></pre>





  
  

  709
  
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_spatialplot.png" class="center"></p>





  
  

  710
  
<p>The function <strong>vrSpatialFeaturePlot</strong> detects the number





  
  

  711
  
of assays within each VoltRon object and visualizes each feature per





  
  

  712
  
each spatial image. A grid of images are visualized either the number of





  
  

  713
  
assays or the number of features are larger than 1. Here, we visualize





  
  

  714
  
the normalized expression of COL1A1 and C1S, two fibrotic markers,





  
  

  715
  
across ROIs of two prolonged covid cases. One may observe that the





  
  

  716
  
fibrotic regions of prolonged case 5 have considerably more COL1A1 and





  
  

  717
  
C1S compared to prolonged case 4.</p>





  
  

  718
  
<pre class="r watch-out"><code>GeoMxR1_subset &lt;- subset(GeoMxR1, sample = c(&quot;prolonged case 4&quot;,&quot;prolonged case 5&quot;))





  
  

  719
  
vrSpatialFeaturePlot(GeoMxR1_subset, features = c(&quot;COL1A1&quot;, &quot;C1S&quot;), group.by = &quot;ROI.name&quot;, 





  
  

  720
  
                     log = TRUE, label = TRUE, keep.scale = &quot;feature&quot;, title.size = 15)</code></pre>





  
  

  721
  
<p><img width="85%" height="85%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_featureplot.png" class="center"></p>





  
  

  722
  
<p><br></p>





  
  

  723
  
</div>





  
  

  724
  
<div id="dimensionality-reduction" class="section level2">





  
  

  725
  
<h2>Dimensionality Reduction</h2>





  
  

  726
  
<p>We can now process the normalized and demultiplexed samples to map





  
  

  727
  
ROIs across all sections onto lower dimensional spaces. The functions





  
  

  728
  
<strong>getFeatures</strong> and <strong>getPCA</strong> select features





  
  

  729
  
(i.e. genes) of interest from the data matrix across all samples and





  
  

  730
  
reduce it to a selected number of principal components.</p>





  
  

  731
  
<pre class="r watch-out"><code>GeoMxR1 &lt;- getFeatures(GeoMxR1)





  
  

  732
  
GeoMxR1 &lt;- getPCA(GeoMxR1, dims = 30)</code></pre>





  
  

  733
  
<p>The function <strong>vrEmbeddingPlot</strong> can be used to





  
  

  734
  
visualize embedding spaces (pca, umap, etc.) for any spatial point





  
  

  735
  
supported by VoltRon, hence cells, spots and ROI are all visualized





  
  

  736
  
using the same set of functions. Here we generate a new metadata column





  
  

  737
  
that represents the <strong>disease durations (control, acute and





  
  

  738
  
prolonged case)</strong>, then map gene profiles to the first two





  
  

  739
  
principal components.</p>





  
  

  740
  
<pre class="r watch-out"><code>GeoMxR1$Condition &lt;- gsub(&quot; [0-9]+$&quot;, &quot;&quot;, GeoMxR1$Sample)





  
  

  741
  
vrEmbeddingPlot(GeoMxR1, group.by = c(&quot;Condition&quot;), embedding = &quot;pca&quot;, pt.size = 3)</code></pre>





  
  

  742
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_embeddingplotsingle.png" class="center"></p>





  
  

  743
  
<p>VoltRon provides additional dimensionality reduction techniques such





  
  

  744
  
as <strong>UMAP</strong>.</p>





  
  

  745
  
<pre class="r watch-out"><code>GeoMxR1 &lt;- getUMAP(GeoMxR1)</code></pre>





  
  

  746
  
<p>Gene expression profiles of ROIs associated with prolonged case





  
  

  747
  
sections seem to show some heterogeneity. We now color segments by





  
  

  748
  
section (or replicate, <strong>Sample</strong>) to observe the sources





  
  

  749
  
of variability. Three replicates of prolonged cases exhibit three





  
  

  750
  
different clusters of ROIs.</p>





  
  

  751
  
<pre class="r watch-out"><code>vrEmbeddingPlot(GeoMxR1, group.by = c(&quot;Condition&quot;), embedding = &quot;pca&quot;, pt.size = 3)





  
  

  752
  
vrEmbeddingPlot(GeoMxR1, group.by = c(&quot;ROI.type&quot;), embedding = &quot;pca&quot;, pt.size = 3)





  
  

  753
  
vrEmbeddingPlot(GeoMxR1, group.by = c(&quot;ROI.type&quot;), embedding = &quot;umap&quot;, pt.size = 3)</code></pre>





  
  

  754
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_embeddingplot.png" class="center"></p>





  
  

  755
  
</div>





  
  

  756
  
<div id="differential-expression-analysis" class="section level2">





  
  

  757
  
<h2>Differential Expression Analysis</h2>





  
  

  758
  
<p>VoltRon provides wrapping functions for calling tools and methods





  
  

  759
  
from popular differential expression analysis package such as <a





  
  

  760
  
href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8">DESeq2</a>.





  
  

  761
  
We utilize <strong>DESeq2</strong> to find differentially expressed





  
  

  762
  
genes across each pair of ROI types conditions.</p>





  
  

  763
  
<pre class="r watch-out"><code># get DE for all conditions





  
  

  764
  
library(DESeq2)





  
  

  765
  
library(dplyr)





  
  

  766
  
DEresults &lt;- getDiffExp(GeoMxR1, group.by = &quot;ROI.type&quot;,





  
  

  767
  
                        group.base = &quot;vessel&quot;, method = &quot;DESeq2&quot;)





  
  

  768
  
DEresults_sig &lt;- DEresults %&gt;% filter(!is.na(padj)) %&gt;% 





  
  

  769
  
  filter(padj &lt; 0.05, abs(log2FoldChange) &gt; 1)





  
  

  770
  
head(DEresults_sig)</code></pre>





  
  

  771
  
<div>





  
  

  772
  
<pre><code style="font-size: 11.7px;">         baseMean log2FoldChange     lfcSE     stat       pvalue         padj     gene             comparison





  
  

  773
  
ACTA2    33.48395       1.508701 0.3458464 4.362343 1.286768e-05 4.902586e-03    ACTA2 ROI.type_vessel_epithelium





  
  

  774
  
ADAMTS1  22.29160       1.152556 0.2272085 5.072680 3.922515e-07 4.109815e-04  ADAMTS1 ROI.type_vessel_epithelium





  
  

  775
  
C11orf96 27.48924       1.142085 0.3041057 3.755554 1.729585e-04 2.588819e-02 C11orf96 ROI.type_vessel_epithelium





  
  

  776
  
CNN1     16.87670       1.112662 0.2680222 4.151381 3.304757e-05 9.766004e-03     CNN1 ROI.type_vessel_epithelium





  
  

  777
  
CRYAB    21.85960       1.264747 0.2173272 5.819552 5.900570e-09 2.472929e-05    CRYAB ROI.type_vessel_epithelium





  
  

  778
  
FLNA     44.50957       1.270025 0.3243115 3.916066 9.000548e-05 1.985331e-02     FLNA ROI.type_vessel_epithelium</code></pre>





  
  

  779
  
</div>





  
  

  780
  
<p><br></p>





  
  

  781
  
<p>The <strong>vrHeatmapPlot</strong> takes a set of features for any





  
  

  782
  
type of spatial point (cells, spots and ROIs) and visualizes scaled data





  
  

  783
  
per each feature. We select <strong>highlight.some = TRUE</strong> to





  
  

  784
  
annotate features which could be large in size where you can also select





  
  

  785
  
the number of such highlighted genes with <strong>n_highlight</strong>.





  
  

  786
  
There seems to be two groups of fibrotic regions that most likely





  
  

  787
  
associated with two prolonged case samples.</p>





  
  

  788
  
<pre class="r watch-out"><code># get DE for all conditions





  
  

  789
  
library(ComplexHeatmap)





  
  

  790
  
vrHeatmapPlot(GeoMxR1, features = unique(DEresults_sig$gene), group.by = &quot;ROI.type&quot;, 





  
  

  791
  
              highlight.some = TRUE, n_highlight = 40)</code></pre>





  
  

  792
  
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_heatmap.png" class="center"></p>





  
  

  793
  
<p><br></p>





  
  

  794
  
<p>In order to get a deeper understanding of differences between





  
  

  795
  
fibrotic regions across two prolonged case replicates. We first subset





  
  

  796
  
the GeoMx data to only sections with fibrotic ROIs.</p>





  
  

  797
  
<pre class="r watch-out"><code>fibrotic_ROI &lt;- vrSpatialPoints(GeoMxR1)[GeoMxR1$ROI.type == &quot;fibrotic&quot;]





  
  

  798
  
GeoMxR1_subset &lt;- subset(GeoMxR1, spatialpoints = fibrotic_ROI)





  
  

  799
  
vrSpatialPlot(GeoMxR1_subset, group.by = &quot;ROI.type&quot;, ncol = 3, alpha = 0.4)</code></pre>





  
  

  800
  
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_spatialplot_fibrotic.png" class="center"></p>





  
  

  801
  
<p>The <strong>getDiffExp</strong> function is capable of testing





  
  

  802
  
differential expression across all metadata columns, including the





  
  

  803
  
<strong>Samples</strong> column that captures labels of different tissue





  
  

  804
  
sections. Macrophage markers such as CD68 and CD163 are differentially





  
  

  805
  
expressed in fibrotic regions of case 5 compared to case 4, including





  
  

  806
  
FN1, a profibrotic gene whose expression can be found on





  
  

  807
  
macrophages.</p>





  
  

  808
  
<pre class="r watch-out"><code>DEresults &lt;- getDiffExp(GeoMxR1_subset, group.by = &quot;Sample&quot;, 





  
  

  809
  
                        group.base = &quot;prolonged case 5&quot;, method = &quot;DESeq2&quot;)





  
  

  810
  
DEresults_sig &lt;- DEresults %&gt;% filter(!is.na(padj)) %&gt;%





  
  

  811
  
  filter(padj &lt; 0.05, abs(log2FoldChange) &gt; 1)





  
  

  812
  
DEresults_sig &lt;- DEresults_sig[order(DEresults_sig$log2FoldChange, decreasing = TRUE),]





  
  

  813
  
head(DEresults_sig)</code></pre>





  
  

  814
  
<div>





  
  

  815
  
<pre><code style="font-size: 10.7px;">       baseMean log2FoldChange     lfcSE      stat       pvalue         padj   gene                               comparison





  
  

  816
  
COL3A1 708.5599       6.635411 0.5198805 12.763338 2.626978e-37 1.596809e-33 COL3A1 Sample_prolonged case 5_prolonged case 4





  
  

  817
  
COL1A2 836.0790       5.237228 0.4407380 11.882861 1.453071e-32 4.416246e-29 COL1A2 Sample_prolonged case 5_prolonged case 4





  
  

  818
  
COL1A1 460.2184       5.175153 0.5237868  9.880267 5.069785e-23 3.081669e-20 COL1A1 Sample_prolonged case 5_prolonged case 4





  
  

  819
  
FN1    278.6594       5.083687 0.3717299 13.675754 1.417301e-42 1.723013e-38    FN1 Sample_prolonged case 5_prolonged case 4





  
  

  820
  
HBB    202.7693       4.944228 0.4884175 10.122955 4.370193e-24 3.794888e-21    HBB Sample_prolonged case 5_prolonged case 4





  
  

  821
  
A2M    466.4328       4.925236 0.4542682 10.842133 2.173435e-27 3.774636e-24    A2M Sample_prolonged case 5_prolonged case 4</code></pre>





  
  

  822
  
</div>





  
  

  823
  
<p><br></p>





  
  

  824
  
<!-- Markers of each individual tissue section for each disease duration is shown on the Heatmap.  -->





  
  

  825
  
<!-- ```{r eval = FALSE, class.source="watch-out"} -->





  
  

  826
  
<!-- # get DE for all conditions -->





  
  

  827
  
<!-- vrHeatmapPlot(GeoMxR1, features = unique(DEresults_sig$gene),  -->





  
  

  828
  
<!--               group.by = "Sample", highlight.some = TRUE) -->





  
  

  829
  
<!-- ``` -->





  
  

  830
  
<!-- <img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_heatmap2.png" class="center"> -->





  
  

  831
  
<!-- <br> -->





  
  

  832
  
</div>





  
  

  833
  
<div id="roi-deconvolution" class="section level2">





  
  

  834
  
<h2>ROI Deconvolution</h2>





  
  

  835
  
<p>VoltRon supports multiple bulk RNA deconvolution algorithms to





  
  

  836
  
analyze the cellular composition of both ROIs and spots. In order to





  
  

  837
  
integrate the scRNA data and the GeoMx data sets within the VoltRon





  
  

  838
  
objects, we will use the <a





  
  

  839
  
href="https://xuranw.github.io/MuSiC/articles/MuSiC.html">MuSiC</a>





  
  

  840
  
package. We will use a human lung scRNA dataset (GSE198864) as





  
  

  841
  
reference, which is found <a





  
  

  842
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GSE198864_lung.rds">here</a>.</p>





  
  

  843
  
<pre class="r watch-out"><code>set.seed(1)





  
  

  844
  
library(Seurat)





  
  

  845
  
library(SingleCellExperiment)





  
  

  846
  
seu &lt;- readRDS(&quot;GSE198864_lung.rds&quot;)





  
  

  847
  
scRNAlung &lt;- seu[,sample(1:ncol(seu), 10000, replace = FALSE)]





  
  

  848
  





  
  

  849
  
# Visualize clusters





  
  

  850
  
DimPlot(scRNAlung, reduction = &quot;umap&quot;, label = T, group.by = &quot;Clusters&quot;) + NoLegend()</code></pre>





  
  

  851
  
<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_deconvolution_singlecell.png" class="center"></p>





  
  

  852
  
<p>We utilize the <strong>getDeconvolution</strong> function to call





  
  

  853
  
wrapper functions for deconvolution algorithms (see ). For all layers





  
  

  854
  
with GoeMx assays, an additional assay within the same layer with





  
  

  855
  
<strong>_decon</strong> postfix will be created. The





  
  

  856
  
<strong>sc.object</strong> argument can either be a





  
  

  857
  
<strong>Seurat</strong> or <strong>SingleCellExperiment</strong>





  
  

  858
  
object.</p>





  
  

  859
  
<pre class="r watch-out"><code>library(MuSiC)





  
  

  860
  
GeoMxR1 &lt;- getDeconvolution(GeoMxR1,





  
  

  861
  
                            sc.object = scRNAlung, sc.assay = &quot;RNA&quot;, 





  
  

  862
  
                            sc.cluster = &quot;Clusters&quot;, sc.samples = &quot;orig.ident&quot;)</code></pre>





  
  

  863
  
<pre><code>VoltRon Object 





  
  

  864
  
prolonged case 4: 





  
  

  865
  
  Layers: Section1 





  
  

  866
  
acute case 3: 





  
  

  867
  
  Layers: Section1 





  
  

  868
  
control case 2: 





  
  

  869
  
  Layers: Section1 





  
  

  870
  
acute case 1: 





  
  

  871
  
  Layers: Section1 





  
  

  872
  
acute case 2: 





  
  

  873
  
  Layers: Section1 





  
  

  874
  
... 





  
  

  875
  
There are 8 samples in total 





  
  

  876
  
Assays: GeoMx(Main) 





  
  

  877
  
Features: RNA(Main) NegProbe Decon </code></pre>





  
  

  878
  
<p>We can now visualize cell type compositions of each ROI. Before





  
  

  879
  
running <strong>vrProportionPlot</strong> function, we need to set the





  
  

  880
  
main feature type as <strong>Decon</strong>. One may see the increased





  
  

  881
  
proportion of cells NK cells and T cells in immune ROIs.</p>





  
  

  882
  
<pre class="r watch-out"><code>vrMainFeatureType(GeoMxR1) &lt;- &quot;Decon&quot;





  
  

  883
  
vrProportionPlot(GeoMxR1, x.label = &quot;ROI.name&quot;)</code></pre>





  
  

  884
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_deconvolution.png" class="center"></p>





  
  

  885
  
<p>Here, we can subset the GeoMx object further to dive deep into the





  
  

  886
  
cellular proportions of each fibrotic region of prolonged cases.





  
  

  887
  
Comparing prolonged case 5 against case 4, we see an increase in the





  
  

  888
  
cellular population of the stromal cluster defined in <a





  
  

  889
  
href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9922044/">Mothes et.





  
  

  890
  
al 2023</a> (that are both vascular and airway smooth muscle cells), and





  
  

  891
  
an increase abundance of macrophages which may indicate that these





  
  

  892
  
macrophages are profibrotic being within and close to fibrotic regions





  
  

  893
  
with increased gene expression of FN1.</p>





  
  

  894
  
<pre class="r watch-out"><code># subsetting fibrotic regions





  
  

  895
  
spatialpoints &lt;- vrSpatialPoints(GeoMxR1)[GeoMxR1$ROI.type == &quot;fibrotic&quot;]





  
  

  896
  
GeoMxR1_subset &lt;- subset(GeoMxR1, spatialpoints = spatialpoints)





  
  

  897
  





  
  

  898
  
# Proportion plot





  
  

  899
  
vrProportionPlot(GeoMxR1_subset, x.label = &quot;ROI.name&quot;, split.method = &quot;facet_grid&quot;, 





  
  

  900
  
                 split.by = &quot;Sample&quot;)





  
  

  901
  





  
  

  902
  
# barplot 





  
  

  903
  
vrBarPlot(GeoMxR1_subset, features = c(&quot;stromal&quot;, &quot;macrophages&quot;), group.by = &quot;Sample&quot;,





  
  

  904
  
          x.label = &quot;ROI.name&quot;, split.by = &quot;Sample&quot;)</code></pre>





  
  

  905
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_heatmap_fibrotic.png" class="center"></p>





  
  

  906
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_barplot_prop_fibrotic.png" class="center"></p>





  
  

  907
  
</div>





  
  

  908
  
<div id="he-image-integration" class="section level2">





  
  

  909
  
<h2>H&amp;E Image Integration</h2>





  
  

  910
  
<p>Questions may arise if in fact these ROIs are fibrotic even though





  
  

  911
  
they were initially annotated as fibrotic regions. VoltRon provides





  
  

  912
  
advanced image registration workflows which we can use to H&amp;E images





  
  

  913
  
of from the same TMA blocks where GeoMx slides were established.</p>





  
  

  914
  
<p>We first import the H&amp;E image of the prolonged case 4 TMA section





  
  

  915
  
using the <strong>importImageData</strong> function. This will import





  
  

  916
  
the H&amp;E image as a standalone VoltRon object. For more information





  
  

  917
  
on image-based VoltRon objects, see the <a





  
  

  918
  
href="pixelanalysis.html">Downstream analysis of Pixels</a> section.</p>





  
  

  919
  
<p>We will use the H&amp;E image of TMA section taken from the same





  
  

  920
  
block as the Prolonged case 4. You can download the image from <a





  
  

  921
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/prolonged_case4_HE.tif">here</a>.</p>





  
  

  922
  
<pre class="r watch-out"><code>vrHEimage &lt;- importImageData(&quot;prolonged_case4_HE.tif&quot;, channel_names = &quot;H&amp;E&quot;)





  
  

  923
  
vrImages(vrHEimage, scale.perc = 40)</code></pre>





  
  

  924
  
<p><img width="50%" height="50%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/prolonged_case4_HE.png" class="center"></p>





  
  

  925
  
<p><br></p>





  
  

  926
  
<p>We can now register the GeoMx slide with the newly imported H&amp;E





  
  

  927
  
based VoltRon object. Since two images are associated with TMA sections





  
  

  928
  
that are not adjacent, we have to rely on the localization of vessels





  
  

  929
  
visible in both images for alignment. VoltRon allows manipulating





  
  

  930
  
multiple channels of an image object two choose the best background





  
  

  931
  
image for manual landmark selection. For more information on both manual





  
  

  932
  
and automated registration of VoltRon objects, see <a





  
  

  933
  
href="registration.html">here</a>.</p>





  
  

  934
  
<p>VoltRon provides multiple registration methods to align images. Here,





  
  

  935
  
after running the <strong>registerSpatialData</strong> function, we





  
  

  936
  
choose the <strong>Homography + Non-rigid (TPS)</strong> method which





  
  

  937
  
utilizes a perspective transformation on the H&amp;E image followed by a





  
  

  938
  
thin plate spline (TPS) alignment. The perspective transformation





  
  

  939
  
performs a global alignment between the H&amp;E image and the main GeoMx





  
  

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image (here the scan image with combined antibody channels), and the TPS





  
  

  941
  
method allows correct local deformations between the perspective





  
  

  942
  
transformed H&amp;E image and the GeoMx image for a more accurate</p>





  
  

  943
  
<pre class="r watch-out"><code>GeoMxR1_subset &lt;- subset(GeoMxR1, sample = c(&quot;prolonged case 4&quot;))





  
  

  944
  
GeoMxReg &lt;- registerSpatialData(reference_spatdata = GeoMxR1_subset, 





  
  

  945
  
                                query_spatdata = vrHEimage)</code></pre>





  
  

  946
  
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_manualregistration.png" class="center"></p>





  
  

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<p>The result of the registration is a list of registered VoltRon





  
  

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objects which we can use for parsing the registered image as well. In





  
  

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this case, the second element of the resulting list would be the





  
  

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registered H&amp;E based VoltRon object.</p>





  
  

  951
  
<pre class="r watch-out"><code>vrHEimage_reg &lt;- GeoMxReg$registered_spat[[2]]</code></pre>





  
  

  952
  
<p>We can check the additional coordinate system now available for the





  
  

  953
  
registered H&amp;E image. One is the coordinate system with the original





  
  

  954
  
image, and the other with the one that is registered.</p>





  
  

  955
  
<pre class="r watch-out"><code>vrSpatialNames(vrHEimage_reg)</code></pre>





  
  

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<pre><code>[1] &quot;main&quot;     &quot;main_reg&quot;</code></pre>





  
  

  957
  
<p>We can also exchange images where the H&amp;E image now is registered





  
  

  958
  
to the perspective of the GeoMx channels, and can be defined as a new





  
  

  959
  
channel in the original GeoMx object.</p>





  
  

  960
  
<pre class="r watch-out"><code>vrImages(GeoMxR1_subset[[&quot;Assay1&quot;]], name = &quot;main&quot;, channel = &quot;H&amp;E&quot;) &lt;- vrImages(vrHEimage_reg, name = &quot;main_reg&quot;, channel = &quot;H&amp;E&quot;)</code></pre>





  
  

  961
  
<p>We can now observe the new channels (H&amp;E) available for the GeoMx





  
  

  962
  
assay using <strong>vrImageChannelNames</strong>. H&amp;E is saved as a





  
  

  963
  
separate channel along with the originally available antibody channels





  
  

  964
  
of the original GeoMx experiment.</p>





  
  

  965
  
<pre class="r watch-out"><code>vrImageChannelNames(GeoMxR1_subset)</code></pre>





  
  

  966
  
<div>





  
  

  967
  
<pre><code style="font-size: 12px;">       Assay    Layer           Sample Spatial                                               Channels





  
  

  968
  
Assay1 GeoMx Section1 prolonged case 4    main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E</code></pre>





  
  

  969
  
</div>





  
  

  970
  
<p>We can now visualize the ROIs and their annotations where the





  
  

  971
  
registered H&amp;E visible in the background. We define the spatial key





  
  

  972
  
<strong>main</strong> and the channel name <strong>H&amp;E</strong>.</p>





  
  

  973
  
<pre class="r watch-out"><code>vrSpatialPlot(GeoMxR1_subset, group.by = &quot;ROI.type&quot;, alpha = 0.7, 





  
  

  974
  
              spatial = &quot;main&quot;, channel = &quot;H&amp;E&quot;)</code></pre>





  
  

  975
  
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_spatialplot_withHE.png" class="center"></p>





  
  

  976
  
<p>Interactive Visualization can also be used to zoom in to ROIs and





  
  

  977
  
investigate the pathology associated with GeoMx ROIs labeled as





  
  

  978
  
fibrotic.</p>





  
  

  979
  
<pre class="r watch-out"><code>vrSpatialPlot(GeoMxR1_subset, group.by = &quot;ROI.type&quot;, alpha = 0.7, 





  
  

  980
  
              spatial = &quot;main&quot;, channel = &quot;H&amp;E&quot;, 





  
  

  981
  
              interactive = TRUE)</code></pre>





  
  

  982
  
<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_spatialplot_withHE_interactive.png" class="center"></p>





  
  

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</div>





  
  

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