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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="xenium-data-analysis" class="section level1"> +<h1>Xenium Data Analysis</h1> +<p>VoltRon is an end-to-end spatial omic analysis package which also +supports investigating spatial points in single cell resolution. VoltRon +includes essential built-in functions capable of +<strong>filtering</strong>, <strong>processing</strong> and +<strong>clustering</strong> as well as <strong>visualizing</strong> +spatial datasets with a goal of cell type discovery and annotation.</p> +<p>In this use case, 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. Two tissue sections of 5 <span +class="math inline">\(\mu\)</span>m tickness are derived from a single +formalin-fixed, paraffin-embedded (FFPE) breast cancer tissue block. +More information on the spatial datasets and the study can be also be +found on the <a +href="https://www.biorxiv.org/content/10.1101/2022.10.06.510405v1">BioArxiv +preprint</a>.</p> +<p>You can import these readouts from the <a +href="https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast">10x +Genomics website</a> (specifically, import <strong>In Situ Replicate +1/2</strong>). Alternatively, you can <strong>download a zipped +collection of Xenium readouts</strong> from <a +href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/SpatialDataAlignment/Xenium_vs_Visium/10X_Xenium_Visium.zip">here</a>.</p> +<p><br></p> +<div id="building-voltron-objects" class="section level2"> +<h2>Building VoltRon objects</h2> +<p>VoltRon includes built-in functions for converting readouts of Xenium +experiments into VoltRon objects. The <strong>importXenium</strong> +function locates all readout documents under the output folder of the +Xenium experiment, and forms a VoltRon object. We will import both +Xenium replicates separately, and merge them after some image +manipulation.</p> +<pre class="r watch-out"><code>library(VoltRon) +Xen_R1 <- importXenium("Xenium_R1/outs", sample_name = "XeniumR1", import_molecules = TRUE) +Xen_R2 <- importXenium("Xenium_R2/outs", sample_name = "XeniumR2", import_molecules = TRUE)</code></pre> +<p>Before moving on to the downstream analysis of the imaging-based +data, we can inspect both Xenium images. We use the +<strong>vrImages</strong> function to call and visualize reference +images of all VoltRon objects. Observe that the DAPI image of the second +Xenium replicate is dim, hence we might need to increase the +brightness.</p> +<pre class="r watch-out"><code>vrImages(Xen_R1) +vrImages(Xen_R2)</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/xeniumr1.png" class="center"> +</td> +<td style="width:50%; vertical-align: center"> +<img src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/xeniumr2.png" class="center"> +</td> +</tr> +</tbody> +</table> +<p><br></p> +<p>We can adjust the brightness of the second Xenium replicate using the +<strong>modulateImage</strong> function where we can change the +brightness and saturation of the reference image of this VoltRon object. +This functionality is optional for VoltRon objects and should be used +when images require further adjustments.</p> +<pre class="r watch-out"><code>Xen_R2 <- modulateImage(Xen_R2, brightness = 800) +vrImages(Xen_R2)</code></pre> +<p><img width="40%" height="40%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/xeniumr2_new.png" class="center"></p> +<p><br></p> +<p>Once both VoltRon objects are created and images are well-tuned, we +can merge these two into a single VoltRon object.</p> +<pre class="r watch-out"><code>Xen_list <- list(Xen_R1, Xen_R2) +Xen_data <- merge(Xen_list[[1]], Xen_list[-1])</code></pre> +<pre><code>VoltRon Object +XeniumR1: + Layers: Section1 +XeniumR2: + Layers: Section1 +Assays: Xenium(Main) </code></pre> +<p><br></p> +</div> +<div id="spatial-visualization" class="section level2"> +<h2>Spatial Visualization</h2> +<p>With <strong>vrSpatialPlot</strong>, we can visualize Xenium +experiments in both cellular and subcellular context. Since we have not +yet started analyzing raw counts of cells, we can first visualize some +transcripts of interest. We first visualize mRNAs of ACTA2, a marker for +smooth muscle cell actin, and TCF7, an early exhausted t cell marker. We +can interactively select a subset of interest within the tissue section +and visualize the localization of these transcripts. Here we subset a +ductal carcinoma niche, and visualize visualize mRNAs of +<strong>(i)</strong> ACTA2, a marker for smooth muscle cell actin, and +<strong>(ii)</strong> TCF7, an early exhausted t cell marker.</p> +<pre class="r watch-out"><code>Xen_R1_subsetinfo <- subset(Xen_R1, interactive = TRUE) +Xen_R1_subset <- Xen_R1_subsetinfo$subsets[[1]] +vrSpatialPlot(Xen_R1_subset, assay = "Xenium_mol", group.by = "gene", + group.id = c("ACTA2", "KRT15", "TACSTD2", "CEACAM6"), pt.size = 0.2, legend.pt.size = 5)</code></pre> +<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_transcripts_visualize.png" class="center"></p> +<p>We can also visualize count data of cells in the Xenium replicates. +The behaviour of <strong>vrSpatialFeaturePlot</strong> (and most +plotting functions in VoltRon) depend on the number of assays associated +with the assay type (e.g. Xenium is both cell and subcellular type). +Here, we have two assays, and we visualize two features, hence the +resulting plot would include four panels. Prior to spatial +visualization, we can normalize the counts to correct for count depth of +cells by <strong>(i)</strong> dividing counts with total counts in each +cell, <strong>(ii)</strong> multiply with some constant (default: +10000), and followed by <strong>(iii)</strong> log transformation of the +counts.</p> +<pre class="r watch-out"><code>Xen_data <- normalizeData(Xen_data, sizefactor = 1000) +vrSpatialFeaturePlot(Xen_data, features = c("ACTA2", "TCF7"), alpha = 1, pt.size = 0.7)</code></pre> +<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_spatialfeature_xenium.png" class="center"></p> +</div> +<div id="processing-and-embedding" class="section level2"> +<h2>Processing and Embedding</h2> +<p>Some number of cells in both Xenium replicates might have extremely +low counts. Although cells are detected at these locations, the low +total counts of cells would make it challenging for phenotyping and +clustering these cells. Hence, we remove such cells from the VoltRon +objects.</p> +<pre class="r watch-out"><code>Xen_data <- subset(Xen_data, Count > 5)</code></pre> +<p>VoltRon is capable of reducing dimensionality of datasets using both +PCA and UMAP which we gonna use to build profile-specific neighborhood +graphs and partition the data into cell types.</p> +<pre class="r watch-out"><code>Xen_data <- getPCA(Xen_data, dims = 20) +Xen_data <- getUMAP(Xen_data, dims = 1:20)</code></pre> +<p>We can also visualize the normalized expression of these features on +embedding spaces (e.g. UMAP) using +<strong>vrEmbeddingFeaturePlot</strong> function.</p> +<pre class="r watch-out"><code>vrEmbeddingFeaturePlot(Xen_data, features = c("LRRC15", "TCF7"), embedding = "umap", + pt.size = 0.4)</code></pre> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_featureplot_xenium.png" class="center"></p> +<p><br></p> +</div> +<div id="clustering" class="section level2"> +<h2>Clustering</h2> +<p>Next, we build neighborhood graphs with the <strong>shared nearest +neighbors (SNN)</strong> of cells which are constructed from +dimensionally reduced gene expression profiles. The function +<strong>getProfileNeighbors</strong> also has an option of building +<strong>k-nearest neighbors (kNN)</strong> graphs.</p> +<pre class="r watch-out"><code>Xen_data <- getProfileNeighbors(Xen_data, dims = 1:20, method = "SNN") +vrGraphNames(Xen_data)</code></pre> +<pre><code>[1] "SNN"</code></pre> +<p>We can later conduct a clustering of cells using the <strong>leiden’s +method</strong> from the igraph package, which is utilized with the +<strong>getClusters</strong> function.</p> +<pre class="r watch-out"><code>Xen_data <- getClusters(Xen_data, resolution = 1.0, label = "Clusters", graph = "SNN")</code></pre> +<p>Now we can label each cell with the associated clustering index and +take a look at the clustering accuracy on the embedding space, and we +can also visualize these clusters on a spatial context.</p> +<pre class="r watch-out"><code>vrEmbeddingPlot(Xen_data, group.by = "Clusters", embedding = "umap", + pt.size = 0.4, label = TRUE)</code></pre> +<p><img width="60%" height="60%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_embedplot_xenium.png" class="center"></p> +<pre class="r watch-out"><code>vrSpatialPlot(Xen_data, group.by = "Clusters", pt.size = 0.18, background.color = "black")</code></pre> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_spatial_xenium.png" class="center"></p> +<p><br></p> +</div> +<div id="annotation" class="section level2"> +<h2>Annotation</h2> +<p>We can annotate each of these clusters according to their positive +markers across 313 features. One can use the +<strong>FindAllMarkers</strong> from the <a +href="https://satijalab.org/seurat/">Seurat</a> package to pinpoint +these markers by first utilizing the <strong>as.Seurat</strong> function +first on the Xenium assays of the VoltRon object.</p> +<p>For more information on conversion to other packages, please visit +the <a href="conversion.html">Converting VoltRon Objects</a>.</p> +<p>Let us create a new metadata feature from the +<strong>Clusters</strong> column, called <strong>CellType</strong>, we +can insert this new metadata column directly to the object.</p> +<pre class="r watch-out"><code>clusters <- factor(Xen_data$Clusters, levels = sort(unique(Xen_data$Clusters))) +levels(clusters) <- c("DCIS_1", + "DCIS_2", + "CD4_TCells", + "Adipocytes", + "PLD4+_LILRA4+_CD4+_Cells", + "ACTA2_myoepithelial", + "IT_2", + "Macrophages", + "MastCells", + "Bcells", + "StromalCells", + "CD8_TCells", + "CD8_TCells", + "EndothelialCells", + "StromalCells", + "MyelomaCells", + "IT_1", + "IT_2", + "ACTA2_myoepithelial", + "DCIS_2", + "IT_3", + "KRT15_myoepithelial") +Xen_data$CellType <- as.character(clusters)</code></pre> +<p><strong>vrSpatialPlot</strong> function can visualize multiple types +of metadata columns, and users can change the location of the legends as +well.</p> +<pre class="r watch-out"><code>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/cellspot_spatial_xenium_annotated.png" class="center"></p> +<p><br></p> +</div> +</div> +<div id="visium-data-analysis" class="section level1"> +<h1>Visium Data Analysis</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 include a few number of cells that +are likely from a combination of cell types within the tissue of origin. +VoltRon analyzes spot level spatial data sets and even allows selecting +a highly variable subset of features to cluster spots into meaningful +groups of in situ spots for detecting niches of interests</p> +<div id="import-st-data" class="section level2"> +<h2>Import ST 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</strong> datasets can be +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 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") +Pos_Sec1 <- importVisium("Sagittal_Posterior/Section1/", sample_name = "Posterior1") + +# merge datasets +MBrain_Sec <- merge(Ant_Sec1, Pos_Sec1, samples = c("Anterior", "Posterior")) +MBrain_Sec</code></pre> +<pre><code>VoltRon Object +Anterior: + Layers: Section1 +Posterior: + Layers: Section1 +Assays: Visium(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/cellspot_visium_firstplot.png" class="center"></p> +<p><br></p> +</div> +<div id="feature-selection" class="section level2"> +<h2>Feature Selection</h2> +<p>VoltRon captures the nearly full transcriptome of the Visium data +which then can be filtered from a list of features ranked by their +variance and importance. We use the <strong>variance stabilization +transformation (vst)</strong> on each individual assay using the +<strong>getFeatures</strong> function and combine these ranked list to +capture features important for all assay of the Visium data later with +<strong>getVariableFeatures</strong> function.</p> +<pre class="r watch-out"><code>head(vrFeatures(MBrain_Sec))</code></pre> +<pre><code>[1] "Xkr4" "Gm1992" "Gm19938" "Gm37381" "Rp1" "Sox17" </code></pre> +<pre class="r watch-out"><code>length(vrFeatures(MBrain_Sec))</code></pre> +<pre><code>[1] 33502</code></pre> +<pre class="r watch-out"><code>MBrain_Sec <- normalizeData(MBrain_Sec) +MBrain_Sec <- getFeatures(MBrain_Sec, n = 3000) +head(vrFeatureData(MBrain_Sec))</code></pre> +<pre><code> mean var adj_var rank +Xkr4 0.0248608534 0.0249941807 0.02800216 14114 +Gm1992 0.0000000000 0.0000000000 0.00000000 0 +Gm19938 0.0285714286 0.0322197476 0.03224908 13889 +Gm37381 0.0000000000 0.0000000000 0.00000000 0 +Rp1 0.0003710575 0.0003710575 0.00000000 0 +Sox17 0.1907235622 0.2219629135 0.23715920 10304</code></pre> +<pre class="r watch-out"><code>selected_features <- getVariableFeatures(MBrain_Sec) +head(selected_features, 20)</code></pre> +<pre><code>[1] "Bc1" "mt-Co1" "mt-Co3" "mt-Atp6" "mt-Co2" "mt-Cytb" "mt-Nd4" "mt-Nd1" "mt-Nd2" +[2] "Fth1" "Hbb-bs" "Cst3" "Gapdh" "Tmsb4x" "Mbp" "Rplp1" "Ttr" "Ppia" +[3] "Ckb" "mt-Nd3" </code></pre> +</div> +<div id="embedding" class="section level2"> +<h2>Embedding</h2> +<p>Now we can learn and visualize PCA and UMAP embeddings on this +smaller number of selected features</p> +<pre class="r watch-out"><code>MBrain_Sec <- getPCA(MBrain_Sec, features = selected_features, dims = 30) +MBrain_Sec <- getUMAP(MBrain_Sec, dims = 1:30) +vrEmbeddingPlot(MBrain_Sec, embedding = "umap")</code></pre> +<p><img width="65%" height="65%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_visium_umap.png" class="center"></p> +<p><br></p> +</div> +<div id="clustering-1" class="section level2"> +<h2>Clustering</h2> +<pre class="r watch-out"><code>MBrain_Sec <- getProfileNeighbors(MBrain_Sec, dims = 1:30, k = 10, 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 = 0.5, label = "Clusters", graph = "SNN") +vrEmbeddingPlot(MBrain_Sec, embedding = "umap", group.by = "Clusters")</code></pre> +<p><img width="65%" height="65%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_visium_umap_clusters.png" class="center"></p> +<p><br></p> +<pre class="r watch-out"><code>vrSpatialPlot(MBrain_Sec, group.by = "Clusters")</code></pre> +<p><img width="65%" height="65%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_visium_spatial_clusters1.png" class="center"></p> +<p><br></p> +</div> +</div> +<div id="melc-data-analysis" class="section level1"> +<h1>MELC Data Analysis</h1> +<p>VoltRon also provides support for imaging based proteomics assays. In +this next use case, we analyze cells characterized by +<strong>multi-epitope ligand cartography (MELC)</strong> with a panel of +44 parameters. We use the already segmented cells on which expression of +<strong>43 protein features</strong> (excluding DAPI) were mapped to +these cells.</p> +<p>We use the segmented cells over microscopy images collected from +<strong>control</strong> and <strong>COVID-19</strong> lung tissues of +donors categorized based on disease durations (<strong>control</strong>, +<strong>acute</strong>, <strong>chronic</strong> and +<strong>prolonged</strong>). Each image is associated with one of few +field of views (FOVs) from a single tissue section of a donor. See <a +href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190732">GSE190732</a> +for more information. You can download the <strong>IFdata.csv</strong> +file and the folder with the <strong>DAPI</strong> images <a +href="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Cellanalysis/MELC/GSE190732.zip">here</a>.</p> +<p>We import the <strong>protein intensities</strong>, +<strong>metadata</strong> and <strong>coordinates</strong> associated +with segmented cells across FOVs of samples.</p> +<pre class="r watch-out"><code>library(VoltRon) +IFdata <- read.csv("IFdata.csv") +data <- IFdata[,c(2:43)] +metadata <- IFdata[,c("disease_state", "object_id", "cluster", "Clusters", + "SourceID", "Sample", "FOV", "Section")] +coordinates <- as.matrix(IFdata[,c("posX","posY")], rownames.force = TRUE)</code></pre> +<p><br></p> +<div id="importing-melc-data" class="section level2"> +<h2>Importing MELC data</h2> +<p>Before analyzing MELC assays across FOVs, we should <strong>build a +VoltRon object</strong> for each individual FOV/Section by using the +<strong>formVoltron</strong> function. We then merge these sections to +respective tissue blocks by defining their samples of origins. We can +also define <strong>assay names</strong>, <strong>assay types</strong> +and <strong>sample (i.e. block) names</strong> of these objects.</p> +<pre class="r watch-out"><code>library(dplyr) +library(magick) +vr_list <- list() +sample_metadata <- metadata %>% select(Sample, FOV, Section) %>% distinct() +for(i in 1:nrow(sample_metadata)){ + vrassay <- sample_metadata[i,] + cells <- rownames(metadata)[metadata$Section == vrassay$Section] + image <- image_read(paste0("DAPI/", vrassay$Sample, "/DAPI_", vrassay$FOV, ".tif")) + vr_list[[vrassay$Section]] <- formVoltRon(data = t(data[cells,]), + metadata = metadata[cells,], + image = image, + coords = coordinates[cells,], + main.assay = "MELC", + assay.type = "cell", + sample_name = vrassay$Section) +}</code></pre> +<p>Before moving forward with merging FOVs, we should <strong>flip +coordinates</strong> of cells and perhaps also then +<strong>resize</strong> these images. The main reason for this +coordinate flipping is that the y-axis of most digital images are of the +opposite direction to the commonly used coordinate spaces.</p> +<pre class="r watch-out"><code>for(i in 1:nrow(sample_metadata)){ + vrassay <- sample_metadata[i,] + vr_list[[vrassay$Section]] <- flipCoordinates(vr_list[[vrassay$Section]]) + vr_list[[vrassay$Section]] <- resizeImage(vr_list[[vrassay$Section]], size = 600) +}</code></pre> +<p>Finally, we merge these assays into one VoltRon object. The +<strong>samples</strong> arguement in the merge function determines +which assays are layers of a single tissue sample/block.</p> +<pre class="r watch-out"><code>vr_merged <- merge(vr_list[[1]], vr_list[-1], samples = sample_metadata$Sample) +vr_merged </code></pre> +<pre><code>VoltRon Object +control_case_3: + Layers: Section1 Section2 +control_case_2: + Layers: Section1 Section2 +control_case_1: + Layers: Section1 Section2 Section3 +acute_case_3: + Layers: Section1 Section2 +acute_case_1: + Layers: Section1 Section2 +... +There are 13 samples in total +Assays: MELC(Main) </code></pre> +<p><br></p> +<p>The prolonged case 4 has two fields of views (FOVs). By subsetting on +the sample of a prolonged case, we can visualize only these two +sections, and visualize the protein expression of CD31 and +Pancytokeratin which are markers of endothelial and epithelial +cells.</p> +<pre class="r watch-out"><code>vr_subset <- subset(vr_merged, samples = "prolonged_case_4") +g1 <- vrSpatialFeaturePlot(vr_subset, features = c("CD31", "Pancytokeratin"), alpha = 1, + pt.size = 0.7, background.color = "black")</code></pre> +<p><img width="90%" height="90%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_spatialfeature.png" class="center"></p> +<p><br></p> +</div> +<div id="dimensionality-reduction" class="section level2"> +<h2>Dimensionality Reduction</h2> +<p>We can utilize dimensional reduction of the available protein markers +using the getPCA and getUMAP functions, but now with relatively lower +numbers of principal components which are enough to capture the +information across 44 features.</p> +<pre class="r watch-out"><code>vr_merged <- getPCA(vr_merged, dims = 10) +vr_merged <- getUMAP(vr_merged, dims = 1:10) +vrEmbeddingFeaturePlot(vr_merged, features = c("CD31", "Pancytokeratin"), embedding = "umap")</code></pre> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_embedding.png" class="center"></p> +<p><br></p> +</div> +<div id="clustering-2" class="section level2"> +<h2>Clustering</h2> +<p>Now we can visualize the clusters across these sections and perhaps +also check for clusters that may reside in only specific disease +conditions.</p> +<pre class="r watch-out"><code># SNN graph and clusters +vr_merged <- getProfileNeighbors(vr_merged, dims = 1:10, k = 10, method = "SNN") +vrGraphNames(vr_merged)</code></pre> +<pre><code>[1] "SNN"</code></pre> +<pre class="r watch-out"><code>vr_merged <- getClusters(vr_merged, resolution = 0.8, label = "MELC_Clusters", graph = "SNN") + +# install patchwork package +if (!requireNamespace("patchwork", quietly = TRUE)) + install.packages("patchwork") +library(patchwork) + +# visualize conditions and clusters +vr_merged$Condition <- gsub("_[0-9]$", "", vr_merged$Sample) +g1 <- vrEmbeddingPlot(vr_merged, group.by = c("Condition"), embedding = "umap") +g2 <- vrEmbeddingPlot(vr_merged, group.by = c("MELC_Clusters"), embedding = "umap", + label = TRUE) +g1 | g2</code></pre> +<p><img width="100%" height="100%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_embeddingclusters.png" class="center"></p> +<p><br></p> +</div> +<div id="visualization-of-markers" class="section level2"> +<h2>Visualization of Markers</h2> +<p>VoltRon provides both violin plots (<strong>vrViolinPlot</strong>) +and heatmaps (<strong>vrHeatmapPlot</strong>) to further investigate the +enrichment of markers across newly clustered datasets. +<strong>Note:</strong> the vrHeatmapPlot function would require you to +have the <strong>ComplexHeatmap</strong> package in your namespace.</p> +<pre class="r watch-out"><code># install patchwork package +if (!requireNamespace("ComplexHeatmap", quietly = TRUE)) + BiocManager::install("ComplexHeatmap") +library(ComplexHeatmap) + +# Visualize Markers +vrHeatmapPlot(vr_merged, features = vrFeatures(vr_merged), + group.by = "MELC_Clusters", show_row_names = TRUE)</code></pre> +<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_heatmapclusters.png" class="center"></p> +<p><br></p> +<pre class="r watch-out"><code>vrViolinPlot(vr_merged, features = c("CD3", "SMA", "Pancytokeratin", "CCR2"), + group.by = "MELC_Clusters", ncol = 2)</code></pre> +<p><img width="80%" height="80%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_violinclusters.png" class="center"></p> +<p><br></p> +</div> +<div id="neighborhood-analysis" class="section level2"> +<h2>Neighborhood Analysis</h2> +<p>We use the <strong>vrNeighbourhoodEnrichment</strong> function to +detect cell type pairs that co occur within each others’ neighborhoods. +First, we establish <strong>spatial neighborhood graphs</strong> that +determine the neighbors of each cell on tissue sections.</p> +<p><a +href="https://en.wikipedia.org/wiki/Delaunay_triangulation">Delaunay +tesselations</a> or graphs are commonly used to determine neighbors of +spatial entities. The function <strong>getSpatialNeighbors</strong> +builds a delaunay graph of all assays of a certain type and detects +neighbors of cells in a VoltRon object.</p> +<pre class="r watch-out"><code>vr_merged <- getSpatialNeighbors(vr_merged, method = "delaunay")</code></pre> +<p>The graph <strong>delaunay</strong>, which we will use for +spatially-aware neightborhood analysis, is now the second graph +available in the VoltRon object along with <strong>SNN</strong>.</p> +<pre class="r watch-out"><code>vrGraphNames(vr_merged)</code></pre> +<pre><code>[1] "SNN" "delaunay"</code></pre> +<p>Once neighbors are founds, we can apply a <strong>permutation +test</strong> that compares the number of cell type occurances with an +expected number of these occurances under multiple permutations of +labels in the tissue (fixed coordinates but cells are randomly +labelled). A similar approach is used to by several spatial analysis +frameworks and packages (<a +href="https://www.nature.com/articles/nmeth.4391">Schapiro et. al +2017</a>, <a +href="https://www.nature.com/articles/s41592-021-01358-2">Palla et. al +2022</a>).</p> +<p>Here, we will use the original cell type labels annotated by <a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9922044/">Mothes et. +al 2023</a>.</p> +<pre class="r watch-out"><code>neighborhood_results <- vrNeighbourhoodEnrichment(vr_merged, group.by = "Clusters")</code></pre> +<p>The neighborhood analysis provides the results of:</p> +<ul> +<li>the <strong>association</strong> tests (whether cell types are +within each other’s neighborhood)</li> +<li>the <strong>segregation</strong> tests (whether cell types are +clustered separately)</li> +</ul> +<p>between all cell type pairs across each layers and assay.</p> +<p>The number of each cell in a pair in each section is reported to +assess the impact of the results of the test (i.e. low number of +abundance in one cell type may indicate low impact).</p> +<pre class="r watch-out"><code>head(neighborhood_results)</code></pre> +<div> +<pre><code style="font-size: 10px;"> from_value to_value p_assoc p_segreg p_assoc_adj p_segreg_adj n_from n_to AssayID Assay Layer Sample +Assay1.1 CD163+ macs CD163+ macs 0.0000000 1.00000000 0.0000 1.00000000 41 41 Assay1 MELC Section1 control_case_3 +Assay1.2 CD163+ macs CD4+ T cells 0.9380000 0.03300000 0.9980 0.09762866 41 48 Assay1 MELC Section1 control_case_3 +Assay1.3 CD163+ macs CD8+ Tcells 0.8779011 0.04339051 0.9980 0.09762866 41 11 Assay1 MELC Section1 control_case_3 +Assay1.4 CD163+ macs NK cells 0.8190000 0.08700000 0.9980 0.15660000 41 15 Assay1 MELC Section1 control_case_3 +Assay1.5 CD163+ macs endothelia 0.1230000 0.85100000 0.5535 0.95737500 41 139 Assay1 MELC Section1 control_case_3 +Assay1.6 CD163+ macs epithelia 0.9320000 0.03600000 0.9980 0.09762866 41 39 Assay1 MELC Section1 control_case_3</code></pre> +</div> +<p><br></p> +<pre class="r watch-out"><code>vrNeighbourhoodEnrichmentPlot(neighborhood_results, assay = "Assay1", type = "assoc")</code></pre> +<p><img width="70%" height="70%" src="https://bimsbstatic.mdc-berlin.de/landthaler/VoltRon/Package/images/cellspot_neighenrichment.png" class="center"></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>