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<h1 class="title toc-ignore">Conversion</h1>
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<p><br></p>
<div id="conversion-to-other-platforms" class="section level1">
<h1>Conversion to Other Platforms</h1>
<p>VoltRon is capable of end-to-end spatial data analysis for all levels
of spatial resolutions, including those of single cell resolution.
However, VoltRon provides a ecosystem friendly infrastructure where
VoltRon objects could be transformed into data structures used by
popular computational platforms such as <a
href="https://satijalab.org/seurat/">Seurat</a>, <a
href="https://squidpy.readthedocs.io/en/stable/">Squidpy</a> and even <a
href="http://vitessce.io/docs/data-file-types/#anndata-zarr">Zarr</a>
for interactive spatial data visualizatiob with <a
href="http://vitessce.io/">Vitessce</a>.</p>
<p>For both <strong>Seurat (R)</strong> and <strong>Squidpy
(Python)</strong>, we analyse readouts of the experiments conducted on
example tissue sections analysed by the <a
href="https://www.10xgenomics.com/platforms/xenium">Xenium In Situ</a>
platform. For more information on processing and analyzing Xenium
datasets, check the <a href="spotanalysis.html">Cell/Spot Analysis</a>
tutorial.</p>
<p><br></p>
<div id="seurat" class="section level2">
<h2>Seurat</h2>
<p>We will first see how we can transform VoltRon objects into Seurat
object and use built-in functions such as
<strong>FindAllMarkers</strong> to visualize marker genes of clusters
found by VoltRon. You can find the clustered Xenium data using VoltRon
<a
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xenium_data_clustered.rds">here</a>.</p>
<pre class="r watch-out"><code>Xen_data <- readRDS("Xenium_data_clustered.rds")
SampleMetadata(Xen_data)</code></pre>
<pre><code> Assay Layer Sample
Assay1 Xenium Section1 XeniumR1
Assay3 Xenium Section1 XeniumR2</code></pre>
<p><br></p>
<p>We use the <strong>as.Seurat</strong> function to convert spatial
assays of VoltRon into Seurat objects. Here, a Seurat object defines
spatial components of cellular and subcellular assays as
<strong>FOV</strong> objects, and we use the <strong>type =
“image”</strong> argument to convert spatial coordinates of cells and
molecules into individual FOV objects for each Xenium assay/layer in the
VoltRon object.</p>
<p>Please check the <a
href="https://satijalab.org/seurat/articles/seurat5_spatial_vignette_2">Analysis
of Image-based Spatial Data in Seurat</a> tutorial for more information
on analyzing FOV-based spatial data sets with Seurat.</p>
<p>note: Use VoltRon::as.Seurat to avoid conflict with Seurat package’s
as.Seurat function</p>
<pre class="r watch-out"><code>library(Seurat)
Xen_data_seu <- VoltRon::as.Seurat(Xen_data, cell.assay = "Xenium", type = "image")
Xen_data_seu <- NormalizeData(Xen_data_seu)
Xen_data_seu</code></pre>
<pre><code>An object of class Seurat
313 features across 283298 samples within 1 assay
Active assay: Xenium (313 features, 0 variable features)
1 layers present: counts
2 dimensional reductions calculated: pca, umap
2 spatial fields of view present: fov_Assay1 fov_Assay3</code></pre>
<p><br></p>
<div id="marker-analysis" class="section level3">
<h3>Marker Analysis</h3>
<p>Now that we converted VoltRon into a Seurat object, we can pick the
<strong>Clusters</strong> metadata column indicating the clustering of
cells and test for marker genes of each individual cluster.</p>
<pre class="r watch-out"><code>Idents(Xen_data_seu) <- "Clusters"
markers <- FindAllMarkers(Xen_data_seu)
head(markers[order(markers$avg_log2FC, decreasing = TRUE),])</code></pre>
<div>
<pre><code style="font-size: 13px;"> p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
CPA3 0 7.343881 0.977 0.029 0 16 CPA3
CTSG 0 7.114698 0.878 0.011 0 16 CTSG
LILRA4.1 0 6.992717 0.939 0.015 0 19 LILRA4
ADIPOQ 0 6.860190 0.974 0.025 0 5 ADIPOQ
MS4A1 0 6.763083 0.919 0.027 0 17 MS4A1
BANK1 0 6.082192 0.889 0.037 0 17 BANK1</code></pre>
</div>
<p><br></p>
</div>
<div id="visualization" class="section level3">
<h3>Visualization</h3>
<p>We can now pick top positive markers from each of these clusters
prior to visualization.</p>
<pre class="r watch-out"><code>library(dplyr)
topmarkers <- markers %>%
group_by(cluster) %>%
top_n(n = 5, wt = avg_log2FC)</code></pre>
<p>Here, VoltRon incorporates the unique markers learned by the
<strong>FindAllMarkers</strong> function from Seurat and uses them to
visualize the expression of these markers on heatmaps, and now we can
also use these markers for annotating the clusters.</p>
<pre class="r watch-out"><code>library(ComplexHeatmap)
marker_features <- unique(topmarkers$gene)
vrHeatmapPlot(Xen_data, features = marker_features, group.by = "Clusters",
show_row_names = TRUE, font.size = 10)</code></pre>
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/conversions_seurat_heatmap.png" class="center"></p>
<p><br></p>
</div>
<div id="convert-with-molecule-data" class="section level3">
<h3>Convert with Molecule Data</h3>
<p>If defined, the <strong>as.Seurat</strong> function may also convert
the molecule assay of the VoltRon object into a Seurat FOV object and
allow visualizing molecules. You can find the Xenium VoltRon object with
the molecule assay <a
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xen_R1.rds">here</a>.</p>
<pre class="r watch-out"><code>Xen_R1 <- readRDS("Xen_R1.rds")
SampleMetadata(Xen_R1)</code></pre>
<pre><code> Assay Layer Sample
Assay1 Xenium Section1 XeniumR1
Assay2 Xenium_mol Section1 XeniumR1</code></pre>
<p><br></p>
<p>We define both the cell level assay and the molecule level assay.</p>
<pre class="r watch-out"><code>Xen_R1_seu <- as.Seurat(Xen_R1, cell.assay = "Xenium", molecule.assay = "Xenium_mol", type = "image")</code></pre>
<p><br></p>
<p>Now we can visualize molecules alongside with cells.</p>
<pre class="r watch-out"><code>ImageDimPlot(Xen_R1_seu, fov = "fovAssay1", molecules = "PGR", group.by = "orig.ident", cols = "lightgrey", mols.size = 1)</code></pre>
<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/conversions_seurat_imagedimplot.png" class="center"></p>
<p><br></p>
</div>
</div>
<div id="spatialexperiment" class="section level2">
<h2>SpatialExperiment</h2>
<p>VoltRon can also convert objects in <a
href="https://www.bioconductor.org/packages/release/bioc/html/SpatialExperiment.html">SpatialExperiment</a>
objects. We are going to use the Xenium data clustered using VoltRon <a
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xenium_data_clustered.rds">here</a>.</p>
<pre class="r watch-out"><code>Xen_data <- readRDS("Xenium_data_clustered.rds")
SampleMetadata(Xen_data)</code></pre>
<p>We use the <strong>as.SpatialExperiment</strong> function to convert
spatial assays of VoltRon into SpatialExperiment objects. Please check
the <a
href="https://www.bioconductor.org/packages/release/bioc/vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html">Introduction
to the SpatialExperiment class</a> tutorial for more information.</p>
<pre class="r watch-out"><code>library(SpatialExperiment)
spe <- as.SpatialExperiment(Xen_data, assay = "Xenium")</code></pre>
<p>Here we can parse the image and visualize.</p>
<pre class="r watch-out"><code>img <- imgRaster(spe,
sample_id = "Assay1",
image_id = "main")
plot(img)</code></pre>
<p><br></p>
</div>
<div id="squidpy-anndata-h5ad" class="section level2">
<h2>Squidpy (Anndata, h5ad)</h2>
<p>A true ecosystem friendly computational platform should support data
types across multiple computing environments. By allowing users to
convert VoltRon objects into annotated data matrix formats such as <a
href="https://github.com/scverse/anndata">anndata</a>, we can use
built-in spatial data analysis methods available on <a
href="https://squidpy.readthedocs.io/en/stable/">squidpy</a>.</p>
<p>You can find the clustered and the annotated Xenium data using
VoltRon <a
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xenium_data_clustered_annotated.rds">here</a>.</p>
<p>Anndata objects wrapped on h5ad files are commonly used by the <a
href="https://www.nature.com/articles/s41587-023-01733-8">scverse</a>
ecosystem for single cell analysis which bring together numeruous tools
maintained and distributed by a large community effort. Both squidpy and
<a href="https://scanpy.readthedocs.io/en/stable/">scanpy</a> are
currently available on scverse.</p>
<pre class="r watch-out"><code>Xen_data <- readRDS("Xenium_data_clustered_annotated.rds")
as.AnnData(Xen_data, assay = "Xenium", file = "Xen_adata_annotated.h5ad")</code></pre>
<p><br></p>
<div id="configure-squidpy-scverse" class="section level3">
<h3>Configure Squidpy (scverse)</h3>
<p>Here, we use the <a
href="https://rstudio.github.io/reticulate/">reticulate</a> package to
call <strong>scverse</strong> module in Python through a prebuilt
anaconda environment. However, any python installation with the scverse
module can be incorporated by reticulate.</p>
<pre class="r watch-out"><code>library(reticulate)
use_condaenv("scverse", required = T)</code></pre>
<p>We import some other necessary modules such as pandas, scanpy and
squidpy.</p>
<pre class="python watch-out"><code>from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
import squidpy as sq
sc.logging.print_header()</code></pre>
<p><br></p>
</div>
<div id="filter-and-normalize" class="section level3">
<h3>Filter and Normalize</h3>
<p>We read the annotated Xenium object that was saved as an h5ad file
using the <strong>as.Anndata</strong> function in VoltRon, and process
before analysis. For more information using scanpy and squidpy on Xenium
datasets, check the <a
href="https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_xenium.html">Analyzing
Xenium data</a> tutorial at squidpy webpage.</p>
<pre class="python watch-out"><code>adata = sc.read_h5ad("Xen_adata_annotated.h5ad")
adata.layers["counts"] = adata.X.copy()
sc.pp.normalize_total(adata, inplace=True)
sc.pp.log1p(adata)</code></pre>
<p><br></p>
</div>
<div id="visualize" class="section level3">
<h3>Visualize</h3>
<p>We use the <strong>squidpy.pl.spatial_scatter</strong> functions
available in squidpy to visualize the spatial localization of cell types
of both Xenium replicates.</p>
<pre class="python watch-out"><code>fig, ax = plt.subplots(1, 2, figsize=(10, 7))
sq.pl.spatial_scatter(adata, library_key = "library_id", library_id = "Assay1",
color=["CellType"], shape=None, size=1, img = False, ax=ax[0])
sq.pl.spatial_scatter(adata, library_key = "library_id", library_id = "Assay3",
color=["CellType"], shape=None, size=1, img = False, ax=ax[1])
plt.show(ax)</code></pre>
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/conversions_anndata_spatial_scatter.png" class="center"></p>
<p><br></p>
</div>
<div id="neighborhood-enrichment" class="section level3">
<h3>Neighborhood Enrichment</h3>
<p>We can now use high level spatially-aware functions available in
squidpy. We first establish spatial neighbors using the delaunay graphs.
The spatial graph and distances will be stored under
<strong>.obsp</strong> attribute/matrix.</p>
<pre class="python watch-out"><code>sq.gr.spatial_neighbors(adata, coord_type="generic", delaunay=True)
adata</code></pre>
<pre><code>AnnData object with n_obs × n_vars = 283298 × 313
obs: 'Count', 'Assay', 'Layer', 'Sample', 'Clusters', 'CellType', 'library_id'
uns: 'log1p', 'spatial_neighbors'
obsm: 'spatial'
layers: 'counts'
obsp: 'spatial_connectivities', 'spatial_distances'</code></pre>
<p>We can now conduct the permutation test for neighborhood enrichment
across cell type pairs.</p>
<pre class="python watch-out"><code>sq.gr.nhood_enrichment(adata, cluster_key="CellType")</code></pre>
<pre class="python watch-out"><code>fig, ax = plt.subplots(1, 2, figsize=(13, 7))
sq.pl.nhood_enrichment(adata, cluster_key="CellType", figsize=(8, 8),
title="Neighborhood enrichment adata", ax=ax[0])
sq.pl.spatial_scatter(adata, color="CellType", library_key = "library_id",
library_id = "Assay1", shape=None, size=2, ax=ax[1])
plt.show(ax)</code></pre>
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/conversions_anndata_neighborhood.png" class="center"></p>
<p><br></p>
</div>
</div>
<div id="vitessce-anndata-zarr" class="section level2">
<h2>Vitessce (Anndata, zarr)</h2>
<p>In this section, we will transform VoltRon objects of Xenium data
into zarr arrays, and use them for interactive visualization in <a
href="http://vitessce.io/">Vitessce</a>. We should first download the
vitessceR package which incorporates wrapper function to visualize zarr
arrays interactively in R.</p>
<pre class="r watch-out"><code>install.packages("devtools")
devtools::install_github("vitessce/vitessceR")</code></pre>
<p><br></p>
<p>You can find the clustered and annotated Xenium data using VoltRon <a
href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Xenium/Xenium_data_clustered_annotated.rds">here</a>.</p>
<pre class="r watch-out"><code>Xen_data <- readRDS("Xenium_data_clustered_annotated.rds")
SampleMetadata(Xen_data)</code></pre>
<pre><code> Assay Layer Sample
Assay1 Xenium Section1 XeniumR1
Assay2 Xenium Section1 XeniumR2</code></pre>
<p><br></p>
<div id="interactive-visualization" class="section level3">
<h3>Interactive Visualization</h3>
<p>Now we can convert the VoltRon object into a zarr array using the
<strong>as.Zarr</strong> function which will create the array in a
specified location.</p>
<pre class="r watch-out"><code>as.AnnData(Xen_data, assays = "Assay1",
file = "xendata_clustered_annotated.zarr", flip_coordinates = TRUE)</code></pre>
<p>We can use the zarr file directly in the
<strong>vrSpatialPlot</strong> function to visualize the zarr array
interactively in Rstudio viewer. The <strong>reduction</strong>
arguement allows the umap of the Xenium data to be visualized alongside
with the spatial coordinates of the Xenium cells.</p>
<pre class="r watch-out"><code>vrSpatialPlot("xendata_clustered_annotated.zarr", group.by = "CellType", reduction = "umap")</code></pre>
<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/conversions_interactive.png" class="center">
<br>
<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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Datasets
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