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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="spot-based-niche-clustering" class="section level1"> +<h1>Spot-based Niche Clustering</h1> +<p>Spot-based spatial transcriptomic assays capture spatially-resolved +gene expression profiles that are somewhat closer to single cell +resolution. However, each spot still includes a few number of cells that +are likely originated from few number of cell types, hence +transcriptomic profile of each spot would likely include markers from +multiple cell types. Here, <strong>RNA deconvolution</strong> can be +incorporated to estimate the percentage/abundance of cell types for each +spot. We use a scRNAseq dataset as a reference to computationally +estimate the relative abundance of cell types across across the +spots.</p> +<p>VoltRon includes wrapper commands for using popular spot-level RNA +deconvolution methods such as <a +href="https://www.nature.com/articles/s41587-021-00830-w">RCTD</a> and +return estimated abundances as additional feature sets within each +layer. These estimated percentages of cell types for each spot could be +incorporated to detect <strong>niches</strong> (i.e. small local +microenvironments of cells) within the tissue. We can process cell type +abundance assays and used them for clustering to detect these +niches.</p> +<p><br></p> +<div id="import-visium-data" class="section level2"> +<h2>Import Visium Data</h2> +<p>For this tutorial we will analyze spot-based transcriptomic assays +from Mouse Brain generated by the <a +href="https://www.10xgenomics.com/products/spatial-gene-expression">Visium</a> +instrument.</p> +<p>You can find and download readouts of all four Visium sections <a +href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/Visium/MouseBrainSerialSections.zip">here</a>. +The <strong>Mouse Brain Serial Section 1/2</strong> datasets can be also +downloaded from <a +href="https://www.10xgenomics.com/resources/datasets?menu%5Bproducts.name%5D=Spatial%20Gene%20Expression&query=&page=1&configure%5BhitsPerPage%5D=50&configure%5BmaxValuesPerFacet%5D=1000">here</a> +(specifically, please filter for <strong>Species=Mouse</strong>, +<strong>AnatomicalEntity=brain</strong>, <strong>Chemistry=v1</strong> +and <strong>PipelineVersion=v1.1.0</strong>).</p> +<p>We will now import each of four samples separately and merge them +into one VoltRon object. There are four brain tissue sections in total +given two serial anterior and serial posterior sections, hence we have +<strong>two tissue blocks each having two layers</strong>.</p> +<pre class="r watch-out"><code>library(VoltRon) +Ant_Sec1 <- importVisium("Sagittal_Anterior/Section1/", sample_name = "Anterior1") +Ant_Sec2 <- importVisium("Sagittal_Anterior/Section2/", sample_name = "Anterior2") +Pos_Sec1 <- importVisium("Sagittal_Posterior/Section1/", sample_name = "Posterior1") +Pos_Sec2 <- importVisium("Sagittal_Posterior/Section2/", sample_name = "Posterior2") + +# merge datasets +MBrain_Sec_list <- list(Ant_Sec1, Ant_Sec2, Pos_Sec1, Pos_Sec2) +MBrain_Sec <- merge(MBrain_Sec_list[[1]], MBrain_Sec_list[-1], + samples = c("Anterior", "Anterior", "Posterior", "Posterior")) +MBrain_Sec</code></pre> +<pre><code>VoltRon Object +Anterior: + Layers: Section1 Section2 +Posterior: + Layers: Section1 Section2 +Assays: Visium(Main) +Features: RNA(Main) </code></pre> +<p>VoltRon maps metadata features on the spatial images, multiple +features can be provided for all assays/layers associated with the main +assay (Visium).</p> +<pre class="r watch-out"><code>vrSpatialFeaturePlot(MBrain_Sec, features = "Count", crop = TRUE, alpha = 1, ncol = 2)</code></pre> +<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_first_plot.png" class="center"></p> +<p><br></p> +</div> +<div id="import-scrna-data" class="section level2"> +<h2>Import scRNA data</h2> +<p>We will now import the scRNA data for reference which can be +downloaded from <a +href="https://www.dropbox.com/s/cuowvm4vrf65pvq/allen_cortex.rds?dl=1">here</a>. +Specifically, we will use a scRNA data of Mouse cortical adult brain +with 14,000 cells, generated with the SMART-Seq2 protocol, from the +Allen Institute. This scRNA data is also used by the Spatial Data +Analysis tutorial in <a +href="https://satijalab.org/seurat/articles/spatial_vignette.html#integration-with-single-cell-data">Seurat</a> +website.</p> +<pre class="r watch-out"><code># install packages if necessary +if(!requireNamespace("Seurat")) + install.packages("Seurat") +if(!requireNamespace("dplyr")) + install.packages("dplyr") + +# import scRNA data +library(Seurat) +allen_reference <- readRDS("allen_cortex.rds") + +# process and reduce dimensionality +library(dplyr) +allen_reference <- SCTransform(allen_reference, ncells = 3000, verbose = FALSE) %>% + RunPCA(verbose = FALSE) %>% + RunUMAP(dims = 1:30)</code></pre> +<p>Before deconvoluting Visium spots, we correct cell types labels and +drop some cell types with extremely few number of cells (e.g. “CR”).</p> +<pre class="r watch-out"><code># update labels and subset +allen_reference$subclass <- gsub("L2/3 IT", "L23 IT", allen_reference$subclass) +allen_reference <- allen_reference[,colnames(allen_reference)[!allen_reference@meta.data$subclass %in% "CR"]] + +# visualize +Idents(allen_reference) <- "subclass" +gsubclass <- DimPlot(allen_reference, reduction = "umap", label = T) + NoLegend() +Idents(allen_reference) <- "class" +gclass <- DimPlot(allen_reference, reduction = "umap", label = T) + NoLegend() +gsubclass | gclass</code></pre> +<p><img width="95%" height="95%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_singlecell.png" class="center"></p> +<p><br></p> +</div> +<div id="spot-deconvolution-with-rctd" class="section level2"> +<h2>Spot Deconvolution with RCTD</h2> +<p>In order to integrate the scRNA data and the spatial data sets within +the VoltRon object and estimate relative cell type abundances for each +Visium spot, we will use <strong>RCTD</strong> algorithm which is +accessible with the <a +href="https://github.com/dmcable/spacexr">spacexr</a> package.</p> +<pre class="r watch-out"><code>if(!requireNamespace("spacexr")) + devtools::install_github("dmcable/spacexr", build_vignettes = FALSE)</code></pre> +<p>After running <strong>getDeconvolution</strong>, an additional +feature set within the same Visium assay with name +<strong>Decon</strong> will be created.</p> +<pre class="r watch-out"><code>library(spacexr) +MBrain_Sec <- getDeconvolution(MBrain_Sec, sc.object = allen_reference, sc.cluster = "subclass", max_cores = 6) +MBrain_Sec</code></pre> +<pre><code>VoltRon Object +Anterior: + Layers: Section1 Section2 +Posterior: + Layers: Section1 Section2 +Assays: Visium(Main) +Features: RNA(Main) Decon </code></pre> +<p>We can now switch to the <strong>Decon</strong> feature type where +features are cell types from the scRNA reference and the data values are +cell types percentages in each spot.</p> +<pre class="r watch-out"><code>vrMainFeatureType(MBrain_Sec) <- "Decon" +vrFeatures(MBrain_Sec)</code></pre> +<pre><code> [1] "Astro" "Endo" "L23 IT" "L4" "L5 IT" "L5 PT" + [7] "L6 CT" "L6 IT" "L6b" "Lamp5" "Macrophage" "Meis2" +[13] "NP" "Oligo" "Peri" "Pvalb" "Serpinf1" "SMC" +[19] "Sncg" "Sst" "Vip" "VLMC" </code></pre> +<p>These features (i.e. cell type abundances) can be visualized like any +other feature type.</p> +<pre class="r watch-out"><code>vrSpatialFeaturePlot(MBrain_Sec, features = c("L4", "L5 PT", "Oligo", "Vip"), + crop = TRUE, ncol = 2, alpha = 1, keep.scale = "all")</code></pre> +<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_spatialfeature_plot.png" class="center"></p> +<p><br></p> +</div> +<div id="clustering" class="section level2"> +<h2>Clustering</h2> +<p>Relative cell type abundances that are learned by RCTD and stored +within VoltRon can now be used to cluster spots. These groups or +clusters of spots can often be referred to as <strong>niches</strong>. +Here, as a definition, a niche is a region or a collection of regions +within tissue that have a distinct cell type composition as opposed to +the remaining parts of the tissue.</p> +<p>The cell type abundances (which adds up to one for each spot) can be +normalized and processed like transcriptomic and proteomic profiles +prior to clustering (i.e. niche clustering). We treat cell type +abundances as <a +href="https://en.wikipedia.org/wiki/Compositional_data">compositional +data</a>, hence we incorporate <strong>centred log ratio (CLR)</strong> +transformation for normalizing them.</p> +<pre class="r watch-out"><code>vrMainFeatureType(MBrain_Sec) <- "Decon" +MBrain_Sec <- normalizeData(MBrain_Sec, method = "CLR")</code></pre> +<p>The CLR normalized assay have only 25 features, each representing a +cell type from the single cell reference data. Hence, we can +<strong>directly calculate UMAP reductions from this feature +abundances</strong> since we dont have much number of features which +necessitates dimensionality reduction such as PCA.</p> +<p>However, we may still need to reduce the dimensionality of this space +with 25 features using UMAP for visualizing purposes. VoltRon is also +capable of calculating the UMAP reduction from normalized data slots. +Hence, we build a UMAP reduction from CLR data directly. However, UMAP +will always be calculated from a PCA reduction by default (if a PCA +embedding is found in the object).</p> +<pre class="r watch-out"><code>MBrain_Sec <- getUMAP(MBrain_Sec, data.type = "norm") +vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "Sample")</code></pre> +<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_embedding_sample.png" class="center"></p> +<p><br></p> +<p>Using normalized cell type abundances, we can now generate k-nearest +neighbor graphs and cluster the graph using leiden method.</p> +<pre class="r watch-out"><code>MBrain_Sec <- getProfileNeighbors(MBrain_Sec, data.type = "norm", method = "SNN") +vrGraphNames(MBrain_Sec)</code></pre> +<pre><code>[1] "SNN"</code></pre> +<pre class="r watch-out"><code>MBrain_Sec <- getClusters(MBrain_Sec, resolution = 0.6, graph = "SNN")</code></pre> +<p><br></p> +</div> +<div id="visualization" class="section level2"> +<h2>Visualization</h2> +<p>VoltRon incorporates distinct plotting functions for, +e.g. embeddings, coordinates, heatmap and even barplots. We can now map +the clusters we have generated on UMAP embeddings.</p> +<pre class="r watch-out"><code># visualize +g1 <- vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "Sample") +g2 <- vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "niche_clusters", label = TRUE) +g1 | g2</code></pre> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_embedding_clusters.png" class="center"></p> +<p><br></p> +<p>Mapping clusters on the spatial images and spots would show the niche +structure across all four tissue sections.</p> +<pre class="r watch-out"><code>vrSpatialPlot(MBrain_Sec, group.by = "niche_clusters", crop = TRUE, alpha = 1)</code></pre> +<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_spatial_clusters.png" class="center"></p> +<p><br></p> +<p>We use <strong>vrHeatmapPlot</strong> to investigate relative cell +type abundances across these niche clusters. You will need to have +<strong>ComplexHeatmap</strong> package in your namespace.</p> +<pre class="r watch-out"><code># install packages if necessary +if(!requireNamespace("ComplexHeatmap")) + BiocManager::install("ComplexHeatmap") + +# heatmap of niches +library(ComplexHeatmap) +vrHeatmapPlot(MBrain_Sec, features = vrFeatures(MBrain_Sec), group.by = "niche_clusters", + show_row_names = T, show_heatmap_legend = T)</code></pre> +<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_heatmap_clusters.png" class="center"> +<br></p> +</div> +</div> +<div id="cell-based-niche-clustering" class="section level1"> +<h1>Cell-based Niche Clustering</h1> +<p>Similar to spot-based spatial omics assays, we can build and cluster +niche associated to each cell for spatial transcriptomics datasets in +single cell resolution. For this, we require building niche assays for +the collections of cells where a niche of cell is defined as a region of +sets of regions with distinct cell type population that each of these +cells belong to.</p> +<p>Here, we dont require any scRNA reference dataset but we may first +need to cluster and annotate cells in the RNA/transcriptome level +profiles, and determine cell types. Then, we first detect the mixture of +cell types within a spatial neighborhood around all cells and use that +as a profile to perform clustering where these clusters will be +associated with niches.</p> +<div id="import-xenium-data" class="section level2"> +<h2>Import Xenium Data</h2> +<p>For this, the data has to be already clustered (and annotated if +possible). We will use the cluster labels generated at the end of the +Xenium analysis workflow from <a href="spotanalysis.html">Cell/Spot +Analysis</a>. You can download the VoltRon object with clustered and +annotated Xenium cells along with the Visium assay from <a +href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/VRBlock_data_clustered.rds">here</a>.</p> +<pre class="r watch-out"><code>Xen_data <- readRDS("VRBlock_data_clustered.rds")</code></pre> +<p>We will use all these 18 cell types used for annotating Xenium cells +for detecting niches with distinct cellular type mixtures.</p> +<pre class="r watch-out"><code>vrMainSpatial(Xen_data, assay = "Assay1") <- "main" +vrMainSpatial(Xen_data, assay = "Assay3") <- "main" +vrSpatialPlot(Xen_data, group.by = "CellType", pt.size = 0.13, background.color = "black", + legend.loc = "top", n.tile = 500)</code></pre> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_xenium_clusters.png" class="center"></p> +<p><br></p> +</div> +<div id="creating-niche-assay" class="section level2"> +<h2>Creating Niche Assay</h2> +<p>For calculating niche profiles for each cell, we have to first build +spatial neighborhoods around cells and capture the local cell type +mixtures. Using <strong>getSpatialNeighbors</strong>, we build a spatial +neighborhood graph to connect all cells to other cells within at most 15 +distance apart.</p> +<pre class="r watch-out"><code>Xen_data <- getSpatialNeighbors(Xen_data, radius = 15, method = "radius") +vrGraphNames(Xen_data)</code></pre> +<pre><code>[1] "radius"</code></pre> +<p>Now, we can build a niche assay for cells using the +<strong>getNicheAssay</strong> function which will create an additional +feature set for cells called <strong>Niche</strong>. Here, each cell +type is a feature and the profile of a cell represents the relative +abundance of cell types around each cell.</p> +<pre class="r watch-out"><code>Xen_data <- getNicheAssay(Xen_data, label = "CellType", graph.type = "radius") +Xen_data</code></pre> +<pre><code>VoltRon Object +10XBlock: + Layers: Section1 Section2 Section3 +Assays: Xenium(Main) Visium +Features: RNA(Main) Niche</code></pre> +<p><br></p> +</div> +<div id="clustering-1" class="section level2"> +<h2>Clustering</h2> +<p>The Niche assay can be normalized similar to the spot-level niche +analysis using <strong>centred log ratio (CLR)</strong> +transformation.</p> +<pre class="r watch-out"><code>vrMainFeatureType(Xen_data) <- "Niche" +Xen_data <- normalizeData(Xen_data, method = "CLR")</code></pre> +<p>Default clustering functions could be used to analyze the normalized +niche profiles of cells to detect niches associated with each cell. +However, we use K-means algorithm to perform the niche clustering. For +this exercise, we pick an estimate of 7 clusters which will be the +number of niche clusters we get.</p> +<pre class="r watch-out"><code>Xen_data <- getClusters(Xen_data, nclus = 7, method = "kmeans", label = "niche_clusters")</code></pre> +<p>After the niche clustering, the metadata is updated and observed +later like below.</p> +<pre class="r watch-out"><code>head(Metadata(Xen_data))</code></pre> +<div> +<pre><code style="font-size: 10px;"> id Count assay_id Assay Layer Sample CellType niche_clusters +1_Assay1 1_Assay1 28 Assay1 Xenium Section1 10XBlock DCIS_1 2 +2_Assay1 2_Assay1 94 Assay1 Xenium Section1 10XBlock DCIS_2 2 +3_Assay1 3_Assay1 9 Assay1 Xenium Section1 10XBlock DCIS_1 2 +4_Assay1 4_Assay1 11 Assay1 Xenium Section1 10XBlock DCIS_1 2 +5_Assay1 5_Assay1 48 Assay1 Xenium Section1 10XBlock DCIS_2 2 +6_Assay1 6_Assay1 7 Assay1 Xenium Section1 10XBlock DCIS_1 2</code></pre> +</div> +<p><br></p> +</div> +<div id="visualization-1" class="section level2"> +<h2>Visualization</h2> +<p>After niche clustering, each cell in the Xenium assay will be +assigned a niche which is initially a number which indicates the ID of +each particular niche. It is up to the user to annotate, filter and +visualize these niches moving forward.</p> +<pre class="r watch-out"><code>vrSpatialPlot(Xen_data, group.by = "niche_clusters", alpha = 1, legend.loc = "top")</code></pre> +<p>We use <strong>vrHeatmapPlot</strong> to investigate the abundance of +each cell type across the niche clusters. You will need to have +<strong>ComplexHeatmap</strong> package in your namespace. We see that +niche cluster 1 include all invasive tumor subtypes (IT 1-3). We see +this for two subtypes of in situ ductal carcinoma (DCIS 1,2) subtypes as +well other than a third DCIS subcluster being within proximity to +myoepithelial cells. Niche cluster 6 also shows regions within the +breast cancer tissue where T cells and B cells are found together +abundantly.</p> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_xenium_nicheclusters.png" class="center"> +<br></p> +<pre class="r watch-out"><code># install packages if necessary +if(!requireNamespace("ComplexHeatmap")) + BiocManager::install("ComplexHeatmap") + +# heatmap of niches +library(ComplexHeatmap) +vrHeatmapPlot(Xen_data, features = vrFeatures(Xen_data), group.by = "niche_clusters")</code></pre> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/decon_xenium_heatmapclusters.png" class="center"> +<br></p> +</div> +</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>