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+} +body { + text-align: justify +} +.center { + display: block; + margin-left: auto; + margin-right: auto; +} +</style> +<style type="text/css"> +.watch-out { + color: black; +} +</style> +<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> +</div> + + + +</div> +</div> + +</div> + +<script> + +// add bootstrap table styles to pandoc tables +function bootstrapStylePandocTables() { + $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed'); +} +$(document).ready(function () { + bootstrapStylePandocTables(); +}); + + +</script> + +<!-- tabsets --> + +<script> +$(document).ready(function () { + window.buildTabsets("TOC"); +}); + +$(document).ready(function () { + $('.tabset-dropdown > .nav-tabs > li').click(function () { + $(this).parent().toggleClass('nav-tabs-open'); + }); +}); +</script> + +<!-- code folding --> + +<script> +$(document).ready(function () { + + // temporarily add toc-ignore selector to headers for the consistency with Pandoc + $('.unlisted.unnumbered').addClass('toc-ignore') + + // move toc-ignore selectors from section div to header + $('div.section.toc-ignore') + .removeClass('toc-ignore') + .children('h1,h2,h3,h4,h5').addClass('toc-ignore'); + + // establish options + var options = { + selectors: "h1,h2,h3", + theme: "bootstrap3", + context: '.toc-content', + hashGenerator: function (text) { + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + }, + ignoreSelector: ".toc-ignore", + scrollTo: 0 + }; + options.showAndHide = false; + options.smoothScroll = false; + + // tocify + var toc = $("#TOC").tocify(options).data("toc-tocify"); +}); +</script> + +<!-- dynamically load mathjax for compatibility with self-contained --> +<script> + (function () { + var script = document.createElement("script"); + script.type = "text/javascript"; + script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; + document.getElementsByTagName("head")[0].appendChild(script); + })(); +</script> + +</body> +</html>