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<h1 class="title toc-ignore">ROI Analysis</h1>
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<p><br></p>
<div id="roi-analysis" class="section level1">
<h1>ROI Analysis</h1>
<p>VoltRon is capable of analyzing readouts from distinct spatial
technologies including <strong>segmentation (ROI)-based transciptomics
assays</strong> that capture large polygonic regions on tissue sections.
VoltRon recognizes such readouts including ones from commercially
available tools and allows users to implement a workflow similar to ones
conducted on bulk RNA-Seq datasets. In this tutorial, we will analyze
morphological images and gene expression profiles provided by the
readouts of the <a
href="https://nanostring.com/products/geomx-digital-spatial-profiler/geomx-dsp-overview/">Nanostring’s
GeoMx Digital Spatial Profiler</a> platform, a high-plex spatial
profiling technology which produces segmentation-based protein and RNA
assays.</p>
<p>In this use case, <strong>eight tissue micro array (TMA) tissue
sections</strong> were fitted into the scan area of the slide loaded on
the GeoMx DSP instrument. These sections were cut from <strong>control
and COVID-19 lung tissues</strong> of donors categorized based on
disease durations (acute and prolonged). See <a
href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190732">GSE190732</a>
for more information.</p>
<p>You can download these tutorial files from here:</p>
<table>
<tr>
<th>
File
</th>
<th>
Link
</th>
</tr>
<tr>
<td>
Counts
</td>
<td>
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/DCC-20230427.zip">DDC
files</a>
</td>
</tr>
<tr>
<td>
GeoMx Human Whole Transcriptome Atlas
</td>
<td>
<a href="https://nanostring.com/wp-content/uploads/Hs_R_NGS_WTA_v1.0.pkc_.zip">Human
RNA WTA for NGS</a>
</td>
</tr>
<tr>
<td>
Segment Summary
</td>
<td>
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/segmentSummary.csv">
ROI Metadata file </a>
</td>
</tr>
<tr>
<td>
Morphology Image
</td>
<td>
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/Lu1A1B5umtrueexp.tif">
Image file </a>
</td>
</tr>
<tr>
<td>
OME.TIFF Image
</td>
<td>
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/Lu1A1B5umtrueexp.ome.tiff">
OME.TIFF file </a>
</td>
</tr>
<tr>
<td>
OME.TIFF Image (XML)
</td>
<td>
<a href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/Lu1A1B5umtrueexp.ome.tiff.xml" download target="_blank">
OME.TIFF (XML) file </a>
</td>
</tr>
</table>
<p>
</p>
<p>We now import the GeoMx data, and start analyzing 87 user selected
segments (i.e. region of interests, <strong>ROI</strong>) to check
spatial localization of signals. The <strong>importGeoMx</strong>
function requires:</p>
<ul>
<li>The path to the Digital Count Conversion file,
<strong>dcc.path</strong>, and Probe Kit Configuration file,
<strong>pkc.file</strong>, which are both provided as the output of the
<a
href="https://emea.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/nanostring-geomxr-ngs-pipeline.html">GeoMx
NGS Pipeline</a>.</li>
<li>The path the to the metadata file, <strong>summarySegment</strong>,
and the specific excel sheet that the metadata is found,
<strong>summarySegmentSheetName</strong>, the path to the main
morphology <strong>image</strong> and the original <strong>ome.tiff
(xml)</strong> file, all of which are provided and imported from the DSP
Control Center. Please see <a
href="https://nanostring.com/support-documents/geomx-dsp-data-analysis-user-manual/">GeoMx
DSP Data Analysis User Manual</a> for more information.</li>
</ul>
<pre class="r watch-out"><code>library(VoltRon)
library(xlsx)
GeoMxR1 <- importGeoMx(dcc.path = "DCC-20230427/",
pkc.file = "Hs_R_NGS_WTA_v1.0.pkc",
summarySegment = "segmentSummary.csv",
image = "Lu1A1B5umtrueexp.tif",
ome.tiff = "Lu1A1B5umtrueexp.ome.tiff.xml",
sample_name = "GeoMxR1")</code></pre>
<p>We can import the GeoMx ROI segments from the
<strong>Lu1A1B5umtrueexp.ome.tiff</strong> image file directly by
replacing the .xml file with the .ome.tiff file in the
<strong>ome.tiff</strong> argument. Note that you need to call the
<strong>RBioFormats</strong> library. If you are getting a <strong>java
error</strong> when running importGeoMx, try increasing the maximum heap
size by supplying the <strong>-Xmx</strong> parameter. Run the code
below before rerunning <strong>importGeoMx</strong> again.</p>
<pre class="r watch-out"><code>options(java.parameters = "-Xmx4g")
library(RBioFormats)</code></pre>
<p><br></p>
<div id="quality-control" class="section level2">
<h2>Quality Control</h2>
<p>Once the GeoMx data is imported, we can start off with examining key
quality control measures and statistics on each segment to investigate
each individual ROI such as sequencing saturation and the number of
cells (nuclei) within each segment. VoltRon also provides the total
number of unique transcripts per ROI and stores in the metadata.</p>
<pre class="r watch-out"><code>library(ggplot2)
vrBarPlot(GeoMxR1,
features = c("Count", "Nuclei.count", "Sequencing.saturation"),
x.label = "ROI.name", group.by = "ROI.type") +
theme(axis.text.x = element_text(size = 3))</code></pre>
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_barplot.png" class="center"></p>
<p>For measuring the quality of individual ROIs, we can add a new
metadata column, called <strong>CountPerNuclei</strong>, to check the
average quality of cells per ROI. It seems some number of ROIs with low
counts per nuclei also have low sequencing saturation.</p>
<pre class="r watch-out"><code>GeoMxR1$CountPerNuclei <- GeoMxR1$Count/GeoMxR1$Nuclei.count
vrBarPlot(GeoMxR1,
features = c("Count", "Nuclei.count",
"Sequencing.saturation", "CountPerNuclei"),
x.label = "ROI.name", group.by = "ROI.type", ncol = 2) +
theme(axis.text.x = element_text(size = 5))</code></pre>
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_barplot2.png" class="center"></p>
<p><br></p>
</div>
<div id="processing" class="section level2">
<h2>Processing</h2>
<p>We can now filter ROIs based on our earlier observation of them
having low count per nuclei where some also have low sequencing
saturation.</p>
<pre class="r watch-out"><code># Filter for count per nuclei
GeoMxR1 <- subset(GeoMxR1, subset = CountPerNuclei > 500)</code></pre>
<p>We then filter genes with low counts by extracting the count matrix
and putting aside all genes whose maximum count across all 87 ROIs are
less than 10.</p>
<pre class="r watch-out"><code>GeoMxR1_data <- vrData(GeoMxR1, norm = FALSE)
feature_ind <- apply(GeoMxR1_data, 1, function(x) max(x) > 10)
selected_features <- vrFeatures(GeoMxR1)[feature_ind]
GeoMxR1_lessfeatures <- subset(GeoMxR1, features = selected_features)</code></pre>
<p>VoltRon is capable of normalizing data provided by a diverse set of
spatial technologies, including the quantile normalization method
suggested by the <a
href="https://nanostring.com/support-documents/geomx-dsp-data-analysis-user-manual/">GeoMx
DSP Data Analysis User Manual</a> which scales the ROI profiles to the
third quartile followed by the geometric mean of all third quartiles
multipled to the scaled profile.</p>
<pre class="r watch-out"><code>GeoMxR1 <- normalizeData(GeoMxR1, method = "Q3Norm")</code></pre>
<p><br></p>
</div>
<div id="interactive-subsetting" class="section level2">
<h2>Interactive Subsetting</h2>
<p>Spatially informed genomic technologies heavily depend on image
manipulation as images provide an additional set of information. Hence,
VoltRon incorporates several interactive built-in utilities. One such
functionality allows manipulating images of VoltRon assays where users
can interactively choose subsets of images. However, we first resize the
morphology image by providing the width of the new image (thus height
will be reduced to preserve the aspect ratio).</p>
<pre class="r watch-out"><code># resizing the image
# GeoMxR1 <- resizeImage(GeoMxR1, size = 4000)</code></pre>
<p>VoltRon provides <strong>a mini Shiny app</strong> for subsetting
spatial points of a VoltRon object by using the image as a reference.
This app is particularly useful when multiple tissue sections were
fitted to a scan area of a slide, such as the one from GeoMx DSP
instrument. We use <strong>interactive = TRUE</strong> option in the
subset function to call the mini Shiny app, and select bounding boxes of
each newly created subset. <strong>Please continue until all eight
sections are selected and subsetted</strong>.</p>
<pre class="r watch-out"><code>GeoMxR1_subset <- subset(GeoMxR1, interactive = TRUE)</code></pre>
<p><img width="85%" height="85%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/interactivesubset.gif" class="center"></p>
<p>We can now merge the list of subsets, or samples, each associated
with one of eight sections. You can provide a list of names for the
newly subsetted samples.</p>
<pre class="r watch-out"><code>GeoMxR1_subset_list <- GeoMxR1_subset$subsets
GeoMxR1 <- merge(GeoMxR1_subset_list[[1]], GeoMxR1_subset_list[-1])
GeoMxR1</code></pre>
<pre><code>VoltRon Object
prolonged case 4:
Layers: Section1
acute case 3:
Layers: Section1
control case 2:
Layers: Section1
acute case 1:
Layers: Section1
acute case 2:
Layers: Section1
...
There are 8 samples in total
Assays: GeoMx(Main) </code></pre>
<p><br></p>
<p>You may also save the selected image subsets and reproduce the
interactive subsetting operation for later use.</p>
<pre class="r watch-out"><code>samples <- c("prolonged case 4", "acute case 3", "control case 2",
"acute case 1", "acute case 2", "prolonged case 5",
"prolonged case 3", "control case 1")
subset_info_list <- GeoMxR1_subset$subset_info_list[[1]]
GeoMxR1_subset_list <- list()
for(i in 1:length(subset_info_list)){
GeoMxR1_subset_list[[i]] <- subset(GeoMxR1, image = subset_info_list[i])
GeoMxR1_subset_list[[i]] <- samples[i]
}
GeoMxR1 <- merge(GeoMxR1_subset_list[[1]], GeoMxR1_subset_list[-1])</code></pre>
<p><br></p>
</div>
<div id="visualization" class="section level2">
<h2>Visualization</h2>
<p>We will now select sections of interests from the VoltRon object, and
visualize ROIs and features for each of these sections.</p>
<p>The function <strong>vrSpatialPlot</strong> plots categorical
attributes associated with ROIs. In this case, we will visualize types
of ROIs that were labelled and annotated during ROI selection.</p>
<pre class="r watch-out"><code>GeoMxR1_subset <- subset(GeoMxR1, sample = c("prolonged case 4","acute case 3"))
vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", ncol = 3, alpha = 0.6)</code></pre>
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_spatialplot.png" class="center"></p>
<p>The function <strong>vrSpatialFeaturePlot</strong> detects the number
of assays within each VoltRon object and visualizes each feature per
each spatial image. A grid of images are visualized either the number of
assays or the number of features are larger than 1. Here, we visualize
the normalized expression of COL1A1 and C1S, two fibrotic markers,
across ROIs of two prolonged covid cases. One may observe that the
fibrotic regions of prolonged case 5 have considerably more COL1A1 and
C1S compared to prolonged case 4.</p>
<pre class="r watch-out"><code>GeoMxR1_subset <- subset(GeoMxR1, sample = c("prolonged case 4","prolonged case 5"))
vrSpatialFeaturePlot(GeoMxR1_subset, features = c("COL1A1", "C1S"), group.by = "ROI.name",
log = TRUE, label = TRUE, keep.scale = "feature", title.size = 15)</code></pre>
<p><img width="85%" height="85%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_featureplot.png" class="center"></p>
<p><br></p>
</div>
<div id="dimensionality-reduction" class="section level2">
<h2>Dimensionality Reduction</h2>
<p>We can now process the normalized and demultiplexed samples to map
ROIs across all sections onto lower dimensional spaces. The functions
<strong>getFeatures</strong> and <strong>getPCA</strong> select features
(i.e. genes) of interest from the data matrix across all samples and
reduce it to a selected number of principal components.</p>
<pre class="r watch-out"><code>GeoMxR1 <- getFeatures(GeoMxR1)
GeoMxR1 <- getPCA(GeoMxR1, dims = 30)</code></pre>
<p>The function <strong>vrEmbeddingPlot</strong> can be used to
visualize embedding spaces (pca, umap, etc.) for any spatial point
supported by VoltRon, hence cells, spots and ROI are all visualized
using the same set of functions. Here we generate a new metadata column
that represents the <strong>disease durations (control, acute and
prolonged case)</strong>, then map gene profiles to the first two
principal components.</p>
<pre class="r watch-out"><code>GeoMxR1$Condition <- gsub(" [0-9]+$", "", GeoMxR1$Sample)
vrEmbeddingPlot(GeoMxR1, group.by = c("Condition"), embedding = "pca", pt.size = 3)</code></pre>
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_embeddingplotsingle.png" class="center"></p>
<p>VoltRon provides additional dimensionality reduction techniques such
as <strong>UMAP</strong>.</p>
<pre class="r watch-out"><code>GeoMxR1 <- getUMAP(GeoMxR1)</code></pre>
<p>Gene expression profiles of ROIs associated with prolonged case
sections seem to show some heterogeneity. We now color segments by
section (or replicate, <strong>Sample</strong>) to observe the sources
of variability. Three replicates of prolonged cases exhibit three
different clusters of ROIs.</p>
<pre class="r watch-out"><code>vrEmbeddingPlot(GeoMxR1, group.by = c("Condition"), embedding = "pca", pt.size = 3)
vrEmbeddingPlot(GeoMxR1, group.by = c("ROI.type"), embedding = "pca", pt.size = 3)
vrEmbeddingPlot(GeoMxR1, group.by = c("ROI.type"), embedding = "umap", pt.size = 3)</code></pre>
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_embeddingplot.png" class="center"></p>
</div>
<div id="differential-expression-analysis" class="section level2">
<h2>Differential Expression Analysis</h2>
<p>VoltRon provides wrapping functions for calling tools and methods
from popular differential expression analysis package such as <a
href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8">DESeq2</a>.
We utilize <strong>DESeq2</strong> to find differentially expressed
genes across each pair of ROI types conditions.</p>
<pre class="r watch-out"><code># get DE for all conditions
library(DESeq2)
library(dplyr)
DEresults <- getDiffExp(GeoMxR1, group.by = "ROI.type",
group.base = "vessel", method = "DESeq2")
DEresults_sig <- DEresults %>% filter(!is.na(padj)) %>%
filter(padj < 0.05, abs(log2FoldChange) > 1)
head(DEresults_sig)</code></pre>
<div>
<pre><code style="font-size: 11.7px;"> baseMean log2FoldChange lfcSE stat pvalue padj gene comparison
ACTA2 33.48395 1.508701 0.3458464 4.362343 1.286768e-05 4.902586e-03 ACTA2 ROI.type_vessel_epithelium
ADAMTS1 22.29160 1.152556 0.2272085 5.072680 3.922515e-07 4.109815e-04 ADAMTS1 ROI.type_vessel_epithelium
C11orf96 27.48924 1.142085 0.3041057 3.755554 1.729585e-04 2.588819e-02 C11orf96 ROI.type_vessel_epithelium
CNN1 16.87670 1.112662 0.2680222 4.151381 3.304757e-05 9.766004e-03 CNN1 ROI.type_vessel_epithelium
CRYAB 21.85960 1.264747 0.2173272 5.819552 5.900570e-09 2.472929e-05 CRYAB ROI.type_vessel_epithelium
FLNA 44.50957 1.270025 0.3243115 3.916066 9.000548e-05 1.985331e-02 FLNA ROI.type_vessel_epithelium</code></pre>
</div>
<p><br></p>
<p>The <strong>vrHeatmapPlot</strong> takes a set of features for any
type of spatial point (cells, spots and ROIs) and visualizes scaled data
per each feature. We select <strong>highlight.some = TRUE</strong> to
annotate features which could be large in size where you can also select
the number of such highlighted genes with <strong>n_highlight</strong>.
There seems to be two groups of fibrotic regions that most likely
associated with two prolonged case samples.</p>
<pre class="r watch-out"><code># get DE for all conditions
library(ComplexHeatmap)
vrHeatmapPlot(GeoMxR1, features = unique(DEresults_sig$gene), group.by = "ROI.type",
highlight.some = TRUE, n_highlight = 40)</code></pre>
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_heatmap.png" class="center"></p>
<p><br></p>
<p>In order to get a deeper understanding of differences between
fibrotic regions across two prolonged case replicates. We first subset
the GeoMx data to only sections with fibrotic ROIs.</p>
<pre class="r watch-out"><code>fibrotic_ROI <- vrSpatialPoints(GeoMxR1)[GeoMxR1$ROI.type == "fibrotic"]
GeoMxR1_subset <- subset(GeoMxR1, spatialpoints = fibrotic_ROI)
vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", ncol = 3, alpha = 0.4)</code></pre>
<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_spatialplot_fibrotic.png" class="center"></p>
<p>The <strong>getDiffExp</strong> function is capable of testing
differential expression across all metadata columns, including the
<strong>Samples</strong> column that captures labels of different tissue
sections. Macrophage markers such as CD68 and CD163 are differentially
expressed in fibrotic regions of case 5 compared to case 4, including
FN1, a profibrotic gene whose expression can be found on
macrophages.</p>
<pre class="r watch-out"><code>DEresults <- getDiffExp(GeoMxR1_subset, group.by = "Sample",
group.base = "prolonged case 5", method = "DESeq2")
DEresults_sig <- DEresults %>% filter(!is.na(padj)) %>%
filter(padj < 0.05, abs(log2FoldChange) > 1)
DEresults_sig <- DEresults_sig[order(DEresults_sig$log2FoldChange, decreasing = TRUE),]
head(DEresults_sig)</code></pre>
<div>
<pre><code style="font-size: 10.7px;"> baseMean log2FoldChange lfcSE stat pvalue padj gene comparison
COL3A1 708.5599 6.635411 0.5198805 12.763338 2.626978e-37 1.596809e-33 COL3A1 Sample_prolonged case 5_prolonged case 4
COL1A2 836.0790 5.237228 0.4407380 11.882861 1.453071e-32 4.416246e-29 COL1A2 Sample_prolonged case 5_prolonged case 4
COL1A1 460.2184 5.175153 0.5237868 9.880267 5.069785e-23 3.081669e-20 COL1A1 Sample_prolonged case 5_prolonged case 4
FN1 278.6594 5.083687 0.3717299 13.675754 1.417301e-42 1.723013e-38 FN1 Sample_prolonged case 5_prolonged case 4
HBB 202.7693 4.944228 0.4884175 10.122955 4.370193e-24 3.794888e-21 HBB Sample_prolonged case 5_prolonged case 4
A2M 466.4328 4.925236 0.4542682 10.842133 2.173435e-27 3.774636e-24 A2M Sample_prolonged case 5_prolonged case 4</code></pre>
</div>
<p><br></p>
<!-- Markers of each individual tissue section for each disease duration is shown on the Heatmap. -->
<!-- ```{r eval = FALSE, class.source="watch-out"} -->
<!-- # get DE for all conditions -->
<!-- vrHeatmapPlot(GeoMxR1, features = unique(DEresults_sig$gene), -->
<!-- group.by = "Sample", highlight.some = TRUE) -->
<!-- ``` -->
<!-- <img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_heatmap2.png" class="center"> -->
<!-- <br> -->
</div>
<div id="roi-deconvolution" class="section level2">
<h2>ROI Deconvolution</h2>
<p>VoltRon supports multiple bulk RNA deconvolution algorithms to
analyze the cellular composition of both ROIs and spots. In order to
integrate the scRNA data and the GeoMx data sets within the VoltRon
objects, we will use the <a
href="https://xuranw.github.io/MuSiC/articles/MuSiC.html">MuSiC</a>
package. We will use a human lung scRNA dataset (GSE198864) as
reference, which is found <a
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GSE198864_lung.rds">here</a>.</p>
<pre class="r watch-out"><code>set.seed(1)
library(Seurat)
library(SingleCellExperiment)
seu <- readRDS("GSE198864_lung.rds")
scRNAlung <- seu[,sample(1:ncol(seu), 10000, replace = FALSE)]
# Visualize clusters
DimPlot(scRNAlung, reduction = "umap", label = T, group.by = "Clusters") + NoLegend()</code></pre>
<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_deconvolution_singlecell.png" class="center"></p>
<p>We utilize the <strong>getDeconvolution</strong> function to call
wrapper functions for deconvolution algorithms (see ). For all layers
with GoeMx assays, an additional assay within the same layer with
<strong>_decon</strong> postfix will be created. The
<strong>sc.object</strong> argument can either be a
<strong>Seurat</strong> or <strong>SingleCellExperiment</strong>
object.</p>
<pre class="r watch-out"><code>library(MuSiC)
GeoMxR1 <- getDeconvolution(GeoMxR1,
sc.object = scRNAlung, sc.assay = "RNA",
sc.cluster = "Clusters", sc.samples = "orig.ident")</code></pre>
<pre><code>VoltRon Object
prolonged case 4:
Layers: Section1
acute case 3:
Layers: Section1
control case 2:
Layers: Section1
acute case 1:
Layers: Section1
acute case 2:
Layers: Section1
...
There are 8 samples in total
Assays: GeoMx(Main)
Features: RNA(Main) NegProbe Decon </code></pre>
<p>We can now visualize cell type compositions of each ROI. Before
running <strong>vrProportionPlot</strong> function, we need to set the
main feature type as <strong>Decon</strong>. One may see the increased
proportion of cells NK cells and T cells in immune ROIs.</p>
<pre class="r watch-out"><code>vrMainFeatureType(GeoMxR1) <- "Decon"
vrProportionPlot(GeoMxR1, x.label = "ROI.name")</code></pre>
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_deconvolution.png" class="center"></p>
<p>Here, we can subset the GeoMx object further to dive deep into the
cellular proportions of each fibrotic region of prolonged cases.
Comparing prolonged case 5 against case 4, we see an increase in the
cellular population of the stromal cluster defined in <a
href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9922044/">Mothes et.
al 2023</a> (that are both vascular and airway smooth muscle cells), and
an increase abundance of macrophages which may indicate that these
macrophages are profibrotic being within and close to fibrotic regions
with increased gene expression of FN1.</p>
<pre class="r watch-out"><code># subsetting fibrotic regions
spatialpoints <- vrSpatialPoints(GeoMxR1)[GeoMxR1$ROI.type == "fibrotic"]
GeoMxR1_subset <- subset(GeoMxR1, spatialpoints = spatialpoints)
# Proportion plot
vrProportionPlot(GeoMxR1_subset, x.label = "ROI.name", split.method = "facet_grid",
split.by = "Sample")
# barplot
vrBarPlot(GeoMxR1_subset, features = c("stromal", "macrophages"), group.by = "Sample",
x.label = "ROI.name", split.by = "Sample")</code></pre>
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_heatmap_fibrotic.png" class="center"></p>
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_barplot_prop_fibrotic.png" class="center"></p>
</div>
<div id="he-image-integration" class="section level2">
<h2>H&E Image Integration</h2>
<p>Questions may arise if in fact these ROIs are fibrotic even though
they were initially annotated as fibrotic regions. VoltRon provides
advanced image registration workflows which we can use to H&E images
of from the same TMA blocks where GeoMx slides were established.</p>
<p>We first import the H&E image of the prolonged case 4 TMA section
using the <strong>importImageData</strong> function. This will import
the H&E image as a standalone VoltRon object. For more information
on image-based VoltRon objects, see the <a
href="pixelanalysis.html">Downstream analysis of Pixels</a> section.</p>
<p>We will use the H&E image of TMA section taken from the same
block as the Prolonged case 4. You can download the image from <a
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/ROIanalysis/GeoMx/prolonged_case4_HE.tif">here</a>.</p>
<pre class="r watch-out"><code>vrHEimage <- importImageData("prolonged_case4_HE.tif", channel_names = "H&E")
vrImages(vrHEimage, scale.perc = 40)</code></pre>
<p><img width="50%" height="50%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/prolonged_case4_HE.png" class="center"></p>
<p><br></p>
<p>We can now register the GeoMx slide with the newly imported H&E
based VoltRon object. Since two images are associated with TMA sections
that are not adjacent, we have to rely on the localization of vessels
visible in both images for alignment. VoltRon allows manipulating
multiple channels of an image object two choose the best background
image for manual landmark selection. For more information on both manual
and automated registration of VoltRon objects, see <a
href="registration.html">here</a>.</p>
<p>VoltRon provides multiple registration methods to align images. Here,
after running the <strong>registerSpatialData</strong> function, we
choose the <strong>Homography + Non-rigid (TPS)</strong> method which
utilizes a perspective transformation on the H&E image followed by a
thin plate spline (TPS) alignment. The perspective transformation
performs a global alignment between the H&E image and the main GeoMx
image (here the scan image with combined antibody channels), and the TPS
method allows correct local deformations between the perspective
transformed H&E image and the GeoMx image for a more accurate</p>
<pre class="r watch-out"><code>GeoMxR1_subset <- subset(GeoMxR1, sample = c("prolonged case 4"))
GeoMxReg <- registerSpatialData(reference_spatdata = GeoMxR1_subset,
query_spatdata = vrHEimage)</code></pre>
<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_manualregistration.png" class="center"></p>
<p>The result of the registration is a list of registered VoltRon
objects which we can use for parsing the registered image as well. In
this case, the second element of the resulting list would be the
registered H&E based VoltRon object.</p>
<pre class="r watch-out"><code>vrHEimage_reg <- GeoMxReg$registered_spat[[2]]</code></pre>
<p>We can check the additional coordinate system now available for the
registered H&E image. One is the coordinate system with the original
image, and the other with the one that is registered.</p>
<pre class="r watch-out"><code>vrSpatialNames(vrHEimage_reg)</code></pre>
<pre><code>[1] "main" "main_reg"</code></pre>
<p>We can also exchange images where the H&E image now is registered
to the perspective of the GeoMx channels, and can be defined as a new
channel in the original GeoMx object.</p>
<pre class="r watch-out"><code>vrImages(GeoMxR1_subset[["Assay1"]], name = "main", channel = "H&E") <- vrImages(vrHEimage_reg, name = "main_reg", channel = "H&E")</code></pre>
<p>We can now observe the new channels (H&E) available for the GeoMx
assay using <strong>vrImageChannelNames</strong>. H&E is saved as a
separate channel along with the originally available antibody channels
of the original GeoMx experiment.</p>
<pre class="r watch-out"><code>vrImageChannelNames(GeoMxR1_subset)</code></pre>
<div>
<pre><code style="font-size: 12px;"> Assay Layer Sample Spatial Channels
Assay1 GeoMx Section1 prolonged case 4 main scanimage,DNA,PanCK,CD45,Alpha Smooth Muscle Actin,H&E</code></pre>
</div>
<p>We can now visualize the ROIs and their annotations where the
registered H&E visible in the background. We define the spatial key
<strong>main</strong> and the channel name <strong>H&E</strong>.</p>
<pre class="r watch-out"><code>vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7,
spatial = "main", channel = "H&E")</code></pre>
<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_spatialplot_withHE.png" class="center"></p>
<p>Interactive Visualization can also be used to zoom in to ROIs and
investigate the pathology associated with GeoMx ROIs labeled as
fibrotic.</p>
<pre class="r watch-out"><code>vrSpatialPlot(GeoMxR1_subset, group.by = "ROI.type", alpha = 0.7,
spatial = "main", channel = "H&E",
interactive = TRUE)</code></pre>
<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/roi_spatialplot_withHE_interactive.png" class="center"></p>
</div>
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Datasets
Models
