1 load libraries

2 Load 10x Visium dataset


InstallData("stxBrain")
trying URL 'http://seurat.nygenome.org/src/contrib/stxBrain.SeuratData_0.1.2.tar.gz'
Content type 'application/octet-stream' length 142661912 bytes (136.1 MB)
==================================================
downloaded 136.1 MB


The downloaded source packages are in
    ‘/tmp/RtmpsJDVnL/downloaded_packages’
brain <- LoadData("stxBrain", type = "anterior1")

3 Data preprocessing

plot1 <- VlnPlot(brain, features = "nCount_Spatial", pt.size = 0.1) + NoLegend()

plot2 <- SpatialFeaturePlot(brain, features = "nCount_Spatial") + theme(legend.position = "right")

wrap_plots(plot1, plot2)

4 Normalization

brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)

5 Gene expression visualization

SpatialFeaturePlot(brain, features = c("Hpca", "Ttr", "Mki67"))

custom_plot <- SpatialFeaturePlot(brain, features = c("Ttr")) + 
                theme(legend.text = element_text(size = 0),
                      legend.title = element_text(size = 20),
                      legend.key.size = unit(1, "cm"))

custom_plot

p1 <- SpatialFeaturePlot(brain, features = "Ttr", pt.size.factor = 1)

p2 <- SpatialFeaturePlot(brain, features = "Ttr", alpha = c(0.1, 1))

p1 + p2

6 Dimensionality reduction, clustering, and visualization

brain <- RunPCA(brain, assay = "SCT", verbose = FALSE)
brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30)
brain <- FindClusters(brain, verbose = FALSE)
brain <- RunUMAP(brain, reduction = "pca", dims = 1:30)
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

7 visualize the results of the clustering either in UMAP space

p1 <- DimPlot(brain, reduction = "umap", label = TRUE) 

p2 <- SpatialDimPlot(brain, label = TRUE, label.size = 3) 

p1 + p2

8 visualize the results of the clustering either in UMAP space

SpatialDimPlot(brain,
               cells.highlight = CellsByIdentities(object = brain, idents = c(2, 1, 4, 3, 5, 8)), 
               facet.highlight = TRUE, 
               ncol = 3) & theme(plot.margin = unit(c(1, 1, 1, 1), "mm"))

9 Interactive plotting

SpatialDimPlot(brain, interactive = TRUE)

10 visualize the results of the clustering either in UMAP space

SpatialFeaturePlot(brain, features = "Ttr", interactive = TRUE)

11 

LinkedDimPlot(brain)

12 Identification of Spatially Variable Features

de_markers <- FindMarkers(brain, ident.1 = 5, ident.2 = 7)

SpatialFeaturePlot(object = brain, features = rownames(de_markers)[1:3], alpha = c(0.1, 1), ncol = 3)

13 FindSpatiallyVariableFeatures

brain <- FindSpatiallyVariableFeatures(brain,
                                       features = VariableFeatures(brain)[1:1000],
                                       selection.method = "moransi")

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top.features <- head(SpatiallyVariableFeatures(brain, method = "moransi"), 6)

SpatialFeaturePlot(brain, features = top.features, ncol = 3, alpha = c(0.1, 1)) & 
  theme(plot.margin = unit(c(1, 1, 1, 1), "mm"))

14 Subset out anatomical regions

brain_sub <- subset(brain, idents = c(1, 2, 3, 4, 6, 7))

SpatialDimPlot(brain_sub, label = T, crop = T, label.size = 3) + NoLegend() + 
  SpatialDimPlot(brain_sub, label = T, crop = F, label.size = 3)

theme_axis_labels <- theme(axis.text.x = element_text(size = 10), 
                           axis.text.y = element_text(size = 10),
                           axis.title.x = element_text(size = 10),
                           axis.title.y = element_text(size = 10))

SpatialDimPlot(brain_sub, label = T, label.size = 3, crop = T) + theme_axis_labels

brain_sub[["anterior1"]] <- Crop(brain_sub[["anterior1"]],
                              x = c(150, 500),
                              y = c(0, 425))

SpatialDimPlot(brain_sub, label = T) + theme_axis_labels

m <- 0.6; b <- -550

cortex_coords <- GetTissueCoordinates(brain_sub, scale = "lowres") %>%
                  subset(-y >= (m * x + b))

cortex <- subset(brain_sub, 
                 cells = cortex_coords$cell)

SpatialDimPlot(cortex, label = T, label.size = 3, crop = F)

c1 <- SpatialDimPlot(cortex, images = "anterior1", crop = T, label = T)
c2 <- SpatialDimPlot(cortex, images = "anterior1", crop = F, label = T, pt.size.factor = 1, label.size = 3)

c1 + c2

15 Integration with single-cell data

allen_reference <- readRDS("/brahms/shared/vignette-data/allen_cortex.rds")
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