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<p><br></p>
<div id="import-visiumhd-data" class="section level2">
<h2>Import VisiumHD Data</h2>
<p>We first have to download some packages that are necessary to import
datasets from <code>.parquet</code> and <code>.h5</code> files provided
by the VisiumHD readouts.</p>
<pre class="r watch-out"><code>install.packages("arrow")
BiocManager::install("rhdf5")
library(arrow)
library(rhdf5)</code></pre>
<p>We use the <strong>importVisiumHD</strong> function to start
analyzing the data. The data has 393401 spots which we will use
OnDisk-backed methods to efficiently manipulate, analyze and visualize
these spots.</p>
<p>The VisiumHD readouts provide multiple bin sizes which are aggregated
versions of the original 2<span class="math inline">\(\mu\)</span>m
<span class="math inline">\(x\)</span> 2<span
class="math inline">\(\mu\)</span>m capture spots. The default bin sizes
are <strong>(i)</strong> 2<span class="math inline">\(\mu\)</span>m
<span class="math inline">\(x\)</span> 2<span
class="math inline">\(\mu\)</span>m, <strong>(ii)</strong> 8<span
class="math inline">\(\mu\)</span>m <span
class="math inline">\(x\)</span> 8<span
class="math inline">\(\mu\)</span>m and <strong>(iii)</strong> 16<span
class="math inline">\(\mu\)</span>m <span
class="math inline">\(x\)</span> 16<span
class="math inline">\(\mu\)</span>m.</p>
<pre class="r watch-out"><code>hddata <- importVisiumHD(dir.path = "VisiumHD/outs/",
bin.size = "8",
resolution_level = "hires")</code></pre>
<p><br></p>
</div>
<div id="savingloading-voltron-objects" class="section level2">
<h2>Saving/Loading VoltRon Objects</h2>
<p>We use <strong>BPCells</strong> and <strong>ImageArray</strong>
packages to accelerate operations of feature matrices and images. Here
<strong>BPCells</strong> allows users access and operate on large
feature matrices or clustering/spatial analysis, while
<strong>ImageArray</strong> provides <a
href="https://en.wikipedia.org/wiki/Pyramid_(image_processing)">pyramids
images</a> to allow fast access to large microscopy images. You can
download these package from GitHub using <strong>devtools</strong>.</p>
<pre class="r watch-out"><code>devtools::install_github("bnprks/BPCells/r")
devtools::install_github("BIMSBbioinfo/ImageArray")
library(BPCells)
library(ImageArray)</code></pre>
<p>We can now save the VoltRon object to disk, large matrices and images
will be written to either <strong>hdf5</strong> or <strong>zarr</strong>
files depending on the <strong>format</strong> arguement, and the rest
of the R object would be written to an <code>.rds</code> file, both
under the designated <strong>output</strong>.</p>
<pre class="r watch-out"><code>hddata <- saveVoltRon(hddata, format = "HDF5VoltRon", output = "data/VisiumHD")</code></pre>
<p>If you want you can load the VoltRon object from the same path as you
have saved.</p>
<pre class="r watch-out"><code>hddata <- loadVoltRon("data/VisiumHD/")</code></pre>
<p><br></p>
</div>
<div id="cellspot-analysis" class="section level2">
<h2>Cell/Spot Analysis</h2>
<p>The <strong>BPCells</strong> package provides fast methods to achieve
operations common to single cell analysis such as filtering,
normalization and dimensionality reduction. Here we have an example of
single-cell like clustering of VisiumHD bins which is efficiently
clustered.</p>
<pre class="r watch-out"><code>spatialpoints <- vrSpatialPoints(hddata)[as.vector(Metadata(hddata)$Count > 10)]
hddata <- subset(hddata, spatialpoints = spatialpoints)
hddata <- normalizeData(hddata, sizefactor = 10000)
hddata <- getFeatures(hddata, n = 3000)
selected_features <- getVariableFeatures(hddata)
hddata <- getPCA(hddata, features = selected_features, dims = 30)
hddata <- getUMAP(hddata, dims = 1:30)</code></pre>
<p>We can now visualized genes over embedding or spatial plots.</p>
<pre class="r watch-out"><code>vrEmbeddingFeaturePlot(hddata, features = "Nrgn", embedding = "umap")
vrSpatialFeaturePlot(hddata, features = "Nrgn")</code></pre>
<table>
<tbody>
<tr style="vertical-align: center">
<td style="width:50%; vertical-align: center">
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/ondisk_embeddingfeature.png" class="center">
</td>
<td style="width:55%; vertical-align: center">
<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/ondisk_spatialfeature.png" class="center">
</td>
</tr>
</tbody>
</table>
<p><br></p>
</div>
<div id="spatial-data-alignment" class="section level2">
<h2>Spatial Data Alignment</h2>
<p>The image registration workflow in the <a
href="registration.html">Spatial Data Alignment</a> tutorial can also be
conducted using disk-backed methods of the VoltRon package.</p>
<pre class="r watch-out"><code>library(VoltRon)
Xen_R1 <- importXenium("Xenium_R1/outs", sample_name = "XeniumR1", resolution_level = 3)
Xen_R1_image <- importImageData("Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.tif",
sample_name = "XeniumR1image",
image_name = "H&E")</code></pre>
<p><br></p>
<p>We can save both Xenium and H&E (image) datasets to disk before
using the mini Shiny app for registration</p>
<pre class="r watch-out"><code>Xen_R1_disk <- saveVoltRon(Xen_R1,
format = "HDF5VoltRon",
output = "data/Xen_R1_h5", replace = TRUE)
Xen_R1_image_disk <- saveVoltRon(Xen_R1_image,
format = "HDF5VoltRon",
output = "data/Xen_R1_image_h5", replace = TRUE)</code></pre>
<p><br></p>
<p>These disk-based datasets can then be loaded from the disk
easily.</p>
<pre class="r watch-out"><code>Xen_R1_disk <- loadVoltRon("../data/OnDisk/Xen_R1_h5/")
Xen_R1_image_disk <- loadVoltRon("../data/OnDisk/Xen_R1_image_h5/")</code></pre>
<p><br></p>
<p>VoltRon stores large images as pyramids to increase interactive
visualization efficiency. This storage strategy allows shiny apps to
zoom in to tissue niches in a speedy fashion. VoltRon incorporates
<code>Image_Array</code> objects (<a
href="https://github.com/BIMSBbioinfo/ImageArray"
class="uri">https://github.com/BIMSBbioinfo/ImageArray</a>) to define
these pyramids.</p>
<pre class="r watch-out"><code>vrImages(Xen_R1_image_disk, as.raster = TRUE)</code></pre>
<pre><code>Image_Array Object
Series 1 of size (3,27587,20511)
Series 2 of size (3,13794,10256)
Series 3 of size (3,6897,5128)
Series 4 of size (3,3449,2564)
Series 5 of size (3,1725,1282)
Series 6 of size (3,863,641)
Series 7 of size (3,432,321) </code></pre>
<p><br></p>
<p>We can now visualize and align the Xenium and H&E objects.</p>
<pre class="r watch-out"><code># Align spatial data
xen_reg <- registerSpatialData(object_list = list(Xen_R1_disk, Xen_R1_image_disk))</code></pre>
<div>
<p><video width="100%" height="100%" controls autoplay>
<source src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/video_temp.mov" type="video/mp4">
Your browser does not support the video tag. </video></p>
</div>
<p><br></p>
<pre class="r watch-out"><code># transfer aligned H&E to Xenium data
Xenium_reg <- xen_reg$registered_spat[[2]]
vrImages(Xen_R1_disk[["Assay1"]], name = "main", channel = "H&E") <- vrImages(Xenium_reg, name = "H&E_reg")
# visualize
vrImages(Xen_R1_disk, channel = "H&E", scale.perc = 10)</code></pre>
<p><br></p>
<p><img width="92%" height="92%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/ondisk_alignedHE.png" class="center"></p>
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