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<!DOCTYPE html>





  
  

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<html>





  
  

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<head>





  
  

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<meta charset="utf-8" />





  
  

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<meta name="generator" content="pandoc" />





  
  

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<title>Conversion</title>





  
  

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<script src="site_libs/header-attrs-2.29/header-attrs.js"></script>





  
  

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<script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>





  
  

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<meta name="viewport" content="width=device-width, initial-scale=1" />





  
  

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<link href="site_libs/bootstrap-3.3.5/css/flatly.min.css" rel="stylesheet" />





  
  

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<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>





  
  

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<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>





  
  

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<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>





  
  

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<style>h1 {font-size: 34px;}





  
  

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       pre:not([class]) { background-color: white }</style>





  
  

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<script src="site_libs/jqueryui-1.13.2/jquery-ui.min.js"></script>





  
  

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<link href="site_libs/tocify-1.9.1/jquery.tocify.css" rel="stylesheet" />





  
  

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summary {





  
  

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<script type="text/javascript">





  
  

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$(document).ready(function () {





  
  

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}





  
  

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.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {





  
  

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.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {





  
  

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</style>





  
  

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<style type="text/css">





  
  

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#TOC {





  
  

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}





  
  

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@media (max-width: 768px) {





  
  

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#TOC {





  
  

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}





  
  

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div.tocify {





  
  

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}





  
  

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@media (min-width: 768px) and (max-width: 991px) {





  
  

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    width: 25%;





  
  

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}





  
  

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<body>





  
  

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<div class="container-fluid main-container">





  
  

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





  
  

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        <li>





  
  

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





  
  

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<h1 class="title toc-ignore">Conversion</h1>





  
  

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<p><br></p>





  
  

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<div id="conversion-to-other-platforms" class="section level1">





  
  

  462
  
<h1>Conversion to Other Platforms</h1>





  
  

  463
  
<p>VoltRon is capable of end-to-end spatial data analysis for all levels





  
  

  464
  
of spatial resolutions, including those of single cell resolution.





  
  

  465
  
However, VoltRon provides a ecosystem friendly infrastructure where





  
  

  466
  
VoltRon objects could be transformed into data structures used by





  
  

  467
  
popular computational platforms such as <a





  
  

  468
  
href="https://satijalab.org/seurat/">Seurat</a>, <a





  
  

  469
  
href="https://squidpy.readthedocs.io/en/stable/">Squidpy</a> and even <a





  
  

  470
  
href="http://vitessce.io/docs/data-file-types/#anndata-zarr">Zarr</a>





  
  

  471
  
for interactive spatial data visualizatiob with <a





  
  

  472
  
href="http://vitessce.io/">Vitessce</a>.</p>





  
  

  473
  
<p>For both <strong>Seurat (R)</strong> and <strong>Squidpy





  
  

  474
  
(Python)</strong>, we analyse readouts of the experiments conducted on





  
  

  475
  
example tissue sections analysed by the <a





  
  

  476
  
href="https://www.10xgenomics.com/platforms/xenium">Xenium In Situ</a>





  
  

  477
  
platform. For more information on processing and analyzing Xenium





  
  

  478
  
datasets, check the <a href="spotanalysis.html">Cell/Spot Analysis</a>





  
  

  479
  
tutorial.</p>





  
  

  480
  
<p><br></p>





  
  

  481
  
<div id="seurat" class="section level2">





  
  

  482
  
<h2>Seurat</h2>





  
  

  483
  
<p>We will first see how we can transform VoltRon objects into Seurat





  
  

  484
  
object and use built-in functions such as





  
  

  485
  
<strong>FindAllMarkers</strong> to visualize marker genes of clusters





  
  

  486
  
found by VoltRon. You can find the clustered Xenium data using VoltRon





  
  

  487
  
<a





  
  

  488
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xenium_data_clustered.rds">here</a>.</p>





  
  

  489
  
<pre class="r watch-out"><code>Xen_data &lt;- readRDS(&quot;Xenium_data_clustered.rds&quot;)





  
  

  490
  
SampleMetadata(Xen_data)</code></pre>





  
  

  491
  
<pre><code>        Assay    Layer   Sample





  
  

  492
  
Assay1 Xenium Section1 XeniumR1





  
  

  493
  
Assay3 Xenium Section1 XeniumR2</code></pre>





  
  

  494
  
<p><br></p>





  
  

  495
  
<p>We use the <strong>as.Seurat</strong> function to convert spatial





  
  

  496
  
assays of VoltRon into Seurat objects. Here, a Seurat object defines





  
  

  497
  
spatial components of cellular and subcellular assays as





  
  

  498
  
<strong>FOV</strong> objects, and we use the <strong>type =





  
  

  499
  
“image”</strong> argument to convert spatial coordinates of cells and





  
  

  500
  
molecules into individual FOV objects for each Xenium assay/layer in the





  
  

  501
  
VoltRon object.</p>





  
  

  502
  
<p>Please check the <a





  
  

  503
  
href="https://satijalab.org/seurat/articles/seurat5_spatial_vignette_2">Analysis





  
  

  504
  
of Image-based Spatial Data in Seurat</a> tutorial for more information





  
  

  505
  
on analyzing FOV-based spatial data sets with Seurat.</p>





  
  

  506
  
<p>note: Use VoltRon::as.Seurat to avoid conflict with Seurat package’s





  
  

  507
  
as.Seurat function</p>





  
  

  508
  
<pre class="r watch-out"><code>library(Seurat)





  
  

  509
  
Xen_data_seu &lt;- VoltRon::as.Seurat(Xen_data, cell.assay = &quot;Xenium&quot;, type = &quot;image&quot;)





  
  

  510
  
Xen_data_seu &lt;- NormalizeData(Xen_data_seu)





  
  

  511
  
Xen_data_seu</code></pre>





  
  

  512
  
<pre><code>An object of class Seurat 





  
  

  513
  
313 features across 283298 samples within 1 assay 





  
  

  514
  
Active assay: Xenium (313 features, 0 variable features)





  
  

  515
  
 1 layers present: counts





  
  

  516
  
 2 dimensional reductions calculated: pca, umap





  
  

  517
  
 2 spatial fields of view present: fov_Assay1 fov_Assay3</code></pre>





  
  

  518
  
<p><br></p>





  
  

  519
  
<div id="marker-analysis" class="section level3">





  
  

  520
  
<h3>Marker Analysis</h3>





  
  

  521
  
<p>Now that we converted VoltRon into a Seurat object, we can pick the





  
  

  522
  
<strong>Clusters</strong> metadata column indicating the clustering of





  
  

  523
  
cells and test for marker genes of each individual cluster.</p>





  
  

  524
  
<pre class="r watch-out"><code>Idents(Xen_data_seu) &lt;- &quot;Clusters&quot;





  
  

  525
  
markers &lt;- FindAllMarkers(Xen_data_seu)





  
  

  526
  
head(markers[order(markers$avg_log2FC, decreasing = TRUE),])</code></pre>





  
  

  527
  
<div>





  
  

  528
  
<pre><code style="font-size: 13px;">         p_val avg_log2FC pct.1 pct.2 p_val_adj cluster   gene





  
  

  529
  
CPA3         0   7.343881 0.977 0.029         0      16   CPA3





  
  

  530
  
CTSG         0   7.114698 0.878 0.011         0      16   CTSG





  
  

  531
  
LILRA4.1     0   6.992717 0.939 0.015         0      19 LILRA4





  
  

  532
  
ADIPOQ       0   6.860190 0.974 0.025         0       5 ADIPOQ





  
  

  533
  
MS4A1        0   6.763083 0.919 0.027         0      17  MS4A1





  
  

  534
  
BANK1        0   6.082192 0.889 0.037         0      17  BANK1</code></pre>





  
  

  535
  
</div>





  
  

  536
  
<p><br></p>





  
  

  537
  
</div>





  
  

  538
  
<div id="visualization" class="section level3">





  
  

  539
  
<h3>Visualization</h3>





  
  

  540
  
<p>We can now pick top positive markers from each of these clusters





  
  

  541
  
prior to visualization.</p>





  
  

  542
  
<pre class="r watch-out"><code>library(dplyr)





  
  

  543
  
topmarkers &lt;- markers %&gt;%





  
  

  544
  
  group_by(cluster) %&gt;%





  
  

  545
  
  top_n(n = 5, wt = avg_log2FC)</code></pre>





  
  

  546
  
<p>Here, VoltRon incorporates the unique markers learned by the





  
  

  547
  
<strong>FindAllMarkers</strong> function from Seurat and uses them to





  
  

  548
  
visualize the expression of these markers on heatmaps, and now we can





  
  

  549
  
also use these markers for annotating the clusters.</p>





  
  

  550
  
<pre class="r watch-out"><code>library(ComplexHeatmap)





  
  

  551
  
marker_features &lt;- unique(topmarkers$gene)





  
  

  552
  
vrHeatmapPlot(Xen_data, features = marker_features, group.by = &quot;Clusters&quot;, 





  
  

  553
  
              show_row_names = TRUE, font.size = 10)</code></pre>





  
  

  554
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/conversions_seurat_heatmap.png" class="center"></p>





  
  

  555
  
<p><br></p>





  
  

  556
  
</div>





  
  

  557
  
<div id="convert-with-molecule-data" class="section level3">





  
  

  558
  
<h3>Convert with Molecule Data</h3>





  
  

  559
  
<p>If defined, the <strong>as.Seurat</strong> function may also convert





  
  

  560
  
the molecule assay of the VoltRon object into a Seurat FOV object and





  
  

  561
  
allow visualizing molecules. You can find the Xenium VoltRon object with





  
  

  562
  
the molecule assay <a





  
  

  563
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xen_R1.rds">here</a>.</p>





  
  

  564
  
<pre class="r watch-out"><code>Xen_R1 &lt;- readRDS(&quot;Xen_R1.rds&quot;)





  
  

  565
  
SampleMetadata(Xen_R1)</code></pre>





  
  

  566
  
<pre><code>            Assay    Layer   Sample





  
  

  567
  
Assay1     Xenium Section1 XeniumR1





  
  

  568
  
Assay2 Xenium_mol Section1 XeniumR1</code></pre>





  
  

  569
  
<p><br></p>





  
  

  570
  
<p>We define both the cell level assay and the molecule level assay.</p>





  
  

  571
  
<pre class="r watch-out"><code>Xen_R1_seu &lt;- as.Seurat(Xen_R1, cell.assay = &quot;Xenium&quot;, molecule.assay = &quot;Xenium_mol&quot;, type = &quot;image&quot;)</code></pre>





  
  

  572
  
<p><br></p>





  
  

  573
  
<p>Now we can visualize molecules alongside with cells.</p>





  
  

  574
  
<pre class="r watch-out"><code>ImageDimPlot(Xen_R1_seu, fov = &quot;fovAssay1&quot;, molecules = &quot;PGR&quot;, group.by = &quot;orig.ident&quot;, cols = &quot;lightgrey&quot;, mols.size = 1)</code></pre>





  
  

  575
  
<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/conversions_seurat_imagedimplot.png" class="center"></p>





  
  

  576
  
<p><br></p>





  
  

  577
  
</div>





  
  

  578
  
</div>





  
  

  579
  
<div id="spatialexperiment" class="section level2">





  
  

  580
  
<h2>SpatialExperiment</h2>





  
  

  581
  
<p>VoltRon can also convert objects in <a





  
  

  582
  
href="https://www.bioconductor.org/packages/release/bioc/html/SpatialExperiment.html">SpatialExperiment</a>





  
  

  583
  
objects. We are going to use the Xenium data clustered using VoltRon <a





  
  

  584
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xenium_data_clustered.rds">here</a>.</p>





  
  

  585
  
<pre class="r watch-out"><code>Xen_data &lt;- readRDS(&quot;Xenium_data_clustered.rds&quot;)





  
  

  586
  
SampleMetadata(Xen_data)</code></pre>





  
  

  587
  
<p>We use the <strong>as.SpatialExperiment</strong> function to convert





  
  

  588
  
spatial assays of VoltRon into SpatialExperiment objects. Please check





  
  

  589
  
the <a





  
  

  590
  
href="https://www.bioconductor.org/packages/release/bioc/vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html">Introduction





  
  

  591
  
to the SpatialExperiment class</a> tutorial for more information.</p>





  
  

  592
  
<pre class="r watch-out"><code>library(SpatialExperiment)





  
  

  593
  
spe &lt;- as.SpatialExperiment(Xen_data, assay = &quot;Xenium&quot;)</code></pre>





  
  

  594
  
<p>Here we can parse the image and visualize.</p>





  
  

  595
  
<pre class="r watch-out"><code>img &lt;- imgRaster(spe, 





  
  

  596
  
                 sample_id = &quot;Assay1&quot;, 





  
  

  597
  
                 image_id = &quot;main&quot;)





  
  

  598
  
plot(img)</code></pre>





  
  

  599
  
<p><br></p>





  
  

  600
  
</div>





  
  

  601
  
<div id="squidpy-anndata-h5ad" class="section level2">





  
  

  602
  
<h2>Squidpy (Anndata, h5ad)</h2>





  
  

  603
  
<p>A true ecosystem friendly computational platform should support data





  
  

  604
  
types across multiple computing environments. By allowing users to





  
  

  605
  
convert VoltRon objects into annotated data matrix formats such as <a





  
  

  606
  
href="https://github.com/scverse/anndata">anndata</a>, we can use





  
  

  607
  
built-in spatial data analysis methods available on <a





  
  

  608
  
href="https://squidpy.readthedocs.io/en/stable/">squidpy</a>.</p>





  
  

  609
  
<p>You can find the clustered and the annotated Xenium data using





  
  

  610
  
VoltRon <a





  
  

  611
  
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xenium_data_clustered_annotated.rds">here</a>.</p>





  
  

  612
  
<p>Anndata objects wrapped on h5ad files are commonly used by the <a





  
  

  613
  
href="https://www.nature.com/articles/s41587-023-01733-8">scverse</a>





  
  

  614
  
ecosystem for single cell analysis which bring together numeruous tools





  
  

  615
  
maintained and distributed by a large community effort. Both squidpy and





  
  

  616
  
<a href="https://scanpy.readthedocs.io/en/stable/">scanpy</a> are





  
  

  617
  
currently available on scverse.</p>





  
  

  618
  
<pre class="r watch-out"><code>Xen_data &lt;- readRDS(&quot;Xenium_data_clustered_annotated.rds&quot;)





  
  

  619
  
as.AnnData(Xen_data, assay = &quot;Xenium&quot;, file = &quot;Xen_adata_annotated.h5ad&quot;)</code></pre>





  
  

  620
  
<p><br></p>





  
  

  621
  
<div id="configure-squidpy-scverse" class="section level3">





  
  

  622
  
<h3>Configure Squidpy (scverse)</h3>





  
  

  623
  
<p>Here, we use the <a





  
  

  624
  
href="https://rstudio.github.io/reticulate/">reticulate</a> package to





  
  

  625
  
call <strong>scverse</strong> module in Python through a prebuilt





  
  

  626
  
anaconda environment. However, any python installation with the scverse





  
  

  627
  
module can be incorporated by reticulate.</p>





  
  

  628
  
<pre class="r watch-out"><code>library(reticulate)





  
  

  629
  
use_condaenv(&quot;scverse&quot;, required = T)</code></pre>





  
  

  630
  
<p>We import some other necessary modules such as pandas, scanpy and





  
  

  631
  
squidpy.</p>





  
  

  632
  
<pre class="python watch-out"><code>from pathlib import Path





  
  

  633
  
import numpy as np





  
  

  634
  
import pandas as pd





  
  

  635
  
import matplotlib.pyplot as plt





  
  

  636
  
import seaborn as sns





  
  

  637
  
import scanpy as sc





  
  

  638
  
import squidpy as sq





  
  

  639
  
sc.logging.print_header()</code></pre>





  
  

  640
  
<p><br></p>





  
  

  641
  
</div>





  
  

  642
  
<div id="filter-and-normalize" class="section level3">





  
  

  643
  
<h3>Filter and Normalize</h3>





  
  

  644
  
<p>We read the annotated Xenium object that was saved as an h5ad file





  
  

  645
  
using the <strong>as.Anndata</strong> function in VoltRon, and process





  
  

  646
  
before analysis. For more information using scanpy and squidpy on Xenium





  
  

  647
  
datasets, check the <a





  
  

  648
  
href="https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_xenium.html">Analyzing





  
  

  649
  
Xenium data</a> tutorial at squidpy webpage.</p>





  
  

  650
  
<pre class="python watch-out"><code>adata = sc.read_h5ad(&quot;Xen_adata_annotated.h5ad&quot;)





  
  

  651
  
adata.layers[&quot;counts&quot;] = adata.X.copy()





  
  

  652
  
sc.pp.normalize_total(adata, inplace=True)





  
  

  653
  
sc.pp.log1p(adata)</code></pre>





  
  

  654
  
<p><br></p>





  
  

  655
  
</div>





  
  

  656
  
<div id="visualize" class="section level3">





  
  

  657
  
<h3>Visualize</h3>





  
  

  658
  
<p>We use the <strong>squidpy.pl.spatial_scatter</strong> functions





  
  

  659
  
available in squidpy to visualize the spatial localization of cell types





  
  

  660
  
of both Xenium replicates.</p>





  
  

  661
  
<pre class="python watch-out"><code>fig, ax = plt.subplots(1, 2, figsize=(10, 7))





  
  

  662
  
sq.pl.spatial_scatter(adata, library_key = &quot;library_id&quot;, library_id = &quot;Assay1&quot;, 





  
  

  663
  
                      color=[&quot;CellType&quot;], shape=None, size=1, img = False, ax=ax[0])





  
  

  664
  
sq.pl.spatial_scatter(adata, library_key = &quot;library_id&quot;, library_id = &quot;Assay3&quot;, 





  
  

  665
  
                      color=[&quot;CellType&quot;], shape=None, size=1, img = False, ax=ax[1])





  
  

  666
  
plt.show(ax)</code></pre>





  
  

  667
  
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/conversions_anndata_spatial_scatter.png" class="center"></p>





  
  

  668
  
<p><br></p>





  
  

  669
  
</div>





  
  

  670
  
<div id="neighborhood-enrichment" class="section level3">





  
  

  671
  
<h3>Neighborhood Enrichment</h3>





  
  

  672
  
<p>We can now use high level spatially-aware functions available in





  
  

  673
  
squidpy. We first establish spatial neighbors using the delaunay graphs.





  
  

  674
  
The spatial graph and distances will be stored under





  
  

  675
  
<strong>.obsp</strong> attribute/matrix.</p>





  
  

  676
  
<pre class="python watch-out"><code>sq.gr.spatial_neighbors(adata, coord_type=&quot;generic&quot;, delaunay=True)





  
  

  677
  
adata</code></pre>





  
  

  678
  
<pre><code>AnnData object with n_obs × n_vars = 283298 × 313





  
  

  679
  
    obs: &#39;Count&#39;, &#39;Assay&#39;, &#39;Layer&#39;, &#39;Sample&#39;, &#39;Clusters&#39;, &#39;CellType&#39;, &#39;library_id&#39;





  
  

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    uns: &#39;log1p&#39;, &#39;spatial_neighbors&#39;





  
  

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    obsm: &#39;spatial&#39;





  
  

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    layers: &#39;counts&#39;





  
  

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    obsp: &#39;spatial_connectivities&#39;, &#39;spatial_distances&#39;</code></pre>





  
  

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<p>We can now conduct the permutation test for neighborhood enrichment





  
  

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across cell type pairs.</p>





  
  

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<pre class="python watch-out"><code>sq.gr.nhood_enrichment(adata, cluster_key=&quot;CellType&quot;)</code></pre>





  
  

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<pre class="python watch-out"><code>fig, ax = plt.subplots(1, 2, figsize=(13, 7))





  
  

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sq.pl.nhood_enrichment(adata, cluster_key=&quot;CellType&quot;, figsize=(8, 8), 





  
  

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                       title=&quot;Neighborhood enrichment adata&quot;, ax=ax[0])





  
  

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sq.pl.spatial_scatter(adata, color=&quot;CellType&quot;, library_key = &quot;library_id&quot;, 





  
  

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                      library_id = &quot;Assay1&quot;, shape=None, size=2, ax=ax[1])





  
  

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plt.show(ax)</code></pre>





  
  

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





  
  

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<p><br></p>





  
  

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</div>





  
  

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</div>





  
  

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<div id="vitessce-anndata-zarr" class="section level2">





  
  

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<h2>Vitessce (Anndata, zarr)</h2>





  
  

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<p>In this section, we will transform VoltRon objects of Xenium data





  
  

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into zarr arrays, and use them for interactive visualization in <a





  
  

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href="http://vitessce.io/">Vitessce</a>. We should first download the





  
  

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vitessceR package which incorporates wrapper function to visualize zarr





  
  

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arrays interactively in R.</p>





  
  

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<pre class="r watch-out"><code>install.packages(&quot;devtools&quot;)





  
  

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devtools::install_github(&quot;vitessce/vitessceR&quot;)</code></pre>





  
  

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<p><br></p>





  
  

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<p>You can find the clustered and annotated Xenium data using VoltRon <a





  
  

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href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xenium_data_clustered_annotated.rds">here</a>.</p>





  
  

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<pre class="r watch-out"><code>Xen_data &lt;- readRDS(&quot;Xenium_data_clustered_annotated.rds&quot;)





  
  

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SampleMetadata(Xen_data)</code></pre>





  
  

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<pre><code>        Assay    Layer   Sample





  
  

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Assay1 Xenium Section1 XeniumR1





  
  

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Assay2 Xenium Section1 XeniumR2</code></pre>





  
  

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<p><br></p>





  
  

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<div id="interactive-visualization" class="section level3">





  
  

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<h3>Interactive Visualization</h3>





  
  

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<p>Now we can convert the VoltRon object into a zarr array using the





  
  

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<strong>as.Zarr</strong> function which will create the array in a





  
  

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specified location.</p>





  
  

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<pre class="r watch-out"><code>as.AnnData(Xen_data, assays = &quot;Assay1&quot;, 





  
  

  721
  
           file = &quot;xendata_clustered_annotated.zarr&quot;, flip_coordinates = TRUE)</code></pre>





  
  

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<p>We can use the zarr file directly in the





  
  

  723
  
<strong>vrSpatialPlot</strong> function to visualize the zarr array





  
  

  724
  
interactively in Rstudio viewer. The <strong>reduction</strong>





  
  

  725
  
arguement allows the umap of the Xenium data to be visualized alongside





  
  

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with the spatial coordinates of the Xenium cells.</p>





  
  

  727
  
<pre class="r watch-out"><code>vrSpatialPlot(&quot;xendata_clustered_annotated.zarr&quot;, group.by = &quot;CellType&quot;, reduction = &quot;umap&quot;)</code></pre>





  
  

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





  
  

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<br>





  
  

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





  
  

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