scplotter to work with 10x Visium data prepared by Seurat¶

See: https://satijalab.org/seurat/articles/spatial_vignette#x-visium

Go back to scplotter documentation: https://pwwang.github.io/scplotter/

Mouse brain¶

In [1]:
suppressPackageStartupMessages({
    library(Seurat)
    library(SeuratData)
})
# Load the scplotter package
# library(scplotter)
devtools::load_all()

ℹ Loading scplotter
In [2]:
suppressWarnings(suppressMessages({
    brain <- LoadData("stxBrain", type = "anterior1")
    brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)
    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)
}))
brain

An object of class Seurat 
48721 features across 2696 samples within 2 assays 
Active assay: SCT (17668 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: Spatial
 2 dimensional reductions calculated: pca, umap
 1 spatial field of view present: anterior1
In [10]:
options(repr.plot.width = 12, repr.plot.height = 6)
devtools::load_all()
plot1 <- FeatureStatPlot(brain, features = "nCount_Spatial",
    ident = "orig.ident", add_point = TRUE, legend.position = "none")
plot2 <- SpatFeaturePlot(brain, features = "nCount_Spatial")

plot1 + plot2

ℹ Loading scplotter
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In [11]:
options(repr.plot.width = 10, repr.plot.height = 5)

SpatFeaturePlot(brain, features = c("Hpca", "Ttr"),
    color_name = "Expression", # alias for `points_color_name`
    size = 1.2  # alias for `points_size`
)
No description has been provided for this image
In [12]:
# Plot without image
options(repr.plot.width = 10, repr.plot.height = 10)

p1 <- SpatFeaturePlot(brain, image = FALSE, features = c("Hpca", "Ttr"),
    color_name = "Expression"  # alias for `points_color_name`
)

# or provide only points layers, and with a different palette
p2 <- SpatFeaturePlot(brain, layers = "points", features = c("Hpca", "Ttr"),
    color_name = "Expression", palette = "Reds")

p1 / p2
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In [14]:
options(repr.plot.width = 10, repr.plot.height = 5)

p1 <- SpatFeaturePlot(brain, features = "Ttr",
    shape = 21,  # alias of points_shape
    size = 1.2,  # alias of points_size
    border_size = 0.1  # alias of points_border_size
)
p2 <- SpatFeaturePlot(brain, features = "Ttr",
    points_shape = 21, points_border_alpha = 0.1, points_border_size = 0.1,
    points_size = 1.2)

p1 + p2

ℹ Loading scplotter
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In [15]:
options(repr.plot.width = 10, repr.plot.height = 5)

p1 <- CellDimPlot(brain, label_insitu = TRUE)
p2 <- SpatDimPlot(brain, points_size = 0.8, label = TRUE)
p1 + p2

Forcing label to be TRUE when label_repel or label_insitu is TRUE.
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In [16]:
options(repr.plot.width = 6, repr.plot.height = 5)

SpatDimPlot(brain, points_size = 0.8,
    label = TRUE, label_insitu = TRUE,
    highlight_color = "red", highlight_stroke = 1.6,
    highlight = 'seurat_clusters %in% c("2", "5", "8")')
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In [17]:
de_markers <- FindMarkers(brain, ident.1 = 5, ident.2 = 6)

options(repr.plot.width = 15, repr.plot.height = 5)

SpatFeaturePlot(brain, features = rownames(de_markers)[1:3])
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In [18]:
# brain <- suppressMessages({
#     FindSpatiallyVariableFeatures(
#         brain, assay = "SCT", features = VariableFeatures(brain)[1:1000],
#         selection.method = "moransi")
# })
# top.features <- head(SpatiallyVariableFeatures(brain, selection.method = "moransi"), 6)
top.features <- c("Calb2", "Gng4", "Ttr", "S100a5", "Nrgn", "Fabp7")

options(repr.plot.width = 15, repr.plot.height = 10)

SpatFeaturePlot(brain, features = top.features)
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In [19]:
coords <- GetTissueCoordinates(brain, image = "anterior1")
scale_factor <- brain@images$anterior1@scale.factors$lowres
coords$x <- coords$x * scale_factor
coords$y <- coords$y * scale_factor
cells <- coords[coords$y > 150 & coords$y < 500 & coords$x > 0 & coords$x < 400, "cell"]

suppressWarnings({
    cortex <- subset(brain, cells = cells)
    cortex <- subset(cortex, idents = c(1, 2, 3, 4, 6, 7))
})

options(repr.plot.width = 14, repr.plot.height = 6)
p1 <- SpatDimPlot(cortex, label = TRUE, label_size = 6, points_size = 2.5)
p2 <- SpatDimPlot(cortex, label = TRUE, points_size = 1, crop = FALSE)

p1 + p2
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In [20]:
options(repr.plot.width = 6, repr.plot.height = 5)

# Or use extent
# We are using brain (uncropped) object
brain123467 <- suppressWarnings(subset(brain, idents = c(1, 2, 3, 4, 6, 7)))
SpatDimPlot(brain123467, ext = c(140, 500, 120, 400), label = TRUE, label_size = 6,
    points_size = 2.5, theme = "theme_this")
No description has been provided for this image

Mouse kidney¶

In [21]:
# https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/49797231/Visium_FFPE_Mouse_Kidney.tar.gz?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20250602/eu-west-1/s3/aws4_request&X-Amz-Date=20250602T214211Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=c71d8aca8e60a009323d7dfefa7df0dc08e9769df54ab2f3c6800a17ba045afd
kidney <- Seurat::Load10X_Spatial("data/Visium_FFPE_Mouse_Kidney/")
In [22]:
options(repr.plot.width = 10, repr.plot.height = 5)

SpatFeaturePlot(kidney, features = c("nCount_Spatial", "nFeature_Spatial"))
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In [23]:
options(repr.plot.width = 10, repr.plot.height = 5)

kidney$log_nCount_Spatial <- log(kidney$nCount_Spatial)
kidney$log_nFeature_Spatial <- log(kidney$nFeature_Spatial)

SpatFeaturePlot(kidney, features = c("log_nCount_Spatial", "log_nFeature_Spatial"))
No description has been provided for this image

Visium V1¶

In [24]:
# https://figshare.com/articles/dataset/10X_visium_data_for_spatial-informed_cell-cell_communication/23621151?file=41446839

load("data/visium_mouse_cortex_annotated.RData")
visium.brain

An object of class Seurat 
1320 features across 1073 samples within 3 assays 
Active assay: Spatial (648 features, 0 variable features)
 2 layers present: counts, data
 2 other assays present: SCT, predictions
 2 dimensional reductions calculated: pca, umap
 1 image present: anterior1
In [25]:
options(repr.plot.width = 14, repr.plot.height = 5)

p1 <- SpatDimPlot(visium.brain, theme = "theme_this")
p2 <- SpatDimPlot(visium.brain, theme = "theme_this", image = "black")
p1 + p2
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In [2]:
x <- sessionInfo()
x <- capture.output(print(x))
# hide the BLAS/LAPACK paths
x <- x[!startsWith(x, "BLAS/LAPACK:")]
cat(paste(x, collapse = "\n"))

R version 4.3.3 (2024-02-29)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.10 (Ootpa)

Matrix products: default

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Chicago
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] scplotter_0.4.0            stxKidney.SeuratData_0.1.0
 [3] stxBrain.SeuratData_0.1.2  ssHippo.SeuratData_3.1.4  
 [5] pbmc3k.SeuratData_3.1.4    bmcite.SeuratData_0.3.0   
 [7] SeuratData_0.2.2.9001      Seurat_5.3.0              
 [9] SeuratObject_5.0.2         sp_2.1-4                  

loaded via a namespace (and not attached):
  [1] cubature_2.1.1              RcppAnnoy_0.0.22           
  [3] splines_4.3.3               later_1.3.2                
  [5] pbdZMQ_0.3-11               bitops_1.0-7               
  [7] tibble_3.2.1                polyclip_1.10-6            
  [9] fastDummies_1.7.3           lifecycle_1.0.4            
 [11] rprojroot_2.0.4             globals_0.16.3             
 [13] lattice_0.22-6              MASS_7.3-60.0.1            
 [15] magrittr_2.0.3              plotly_4.10.4              
 [17] remotes_2.5.0               httpuv_1.6.15              
 [19] sctransform_0.4.1           spam_2.11-0                
 [21] sessioninfo_1.2.2           pkgbuild_1.4.4             
 [23] spatstat.sparse_3.1-0       reticulate_1.38.0          
 [25] cowplot_1.1.3               pbapply_1.7-2              
 [27] RColorBrewer_1.1-3          abind_1.4-5                
 [29] pkgload_1.3.4               zlibbioc_1.48.0            
 [31] Rtsne_0.17                  GenomicRanges_1.54.1       
 [33] purrr_1.0.2                 ggraph_2.2.1               
 [35] BiocGenerics_0.48.1         RCurl_1.98-1.13            
 [37] tweenr_2.0.3                evmix_2.12                 
 [39] rappdirs_0.3.3              circlize_0.4.16            
 [41] GenomeInfoDbData_1.2.11     IRanges_2.36.0             
 [43] S4Vectors_0.40.2            ggrepel_0.9.6              
 [45] irlba_2.3.5.1               listenv_0.9.1              
 [47] spatstat.utils_3.1-1        iNEXT_3.0.1                
 [49] MatrixModels_0.5-3          goftest_1.2-3              
 [51] RSpectra_0.16-1             scRepertoire_2.2.1         
 [53] spatstat.random_3.2-3       fitdistrplus_1.1-11        
 [55] parallelly_1.38.0           codetools_0.2-20           
 [57] DelayedArray_0.28.0         xml2_1.3.6                 
 [59] ggforce_0.4.2               shape_1.4.6.1              
 [61] tidyselect_1.2.1            farver_2.1.2               
 [63] viridis_0.6.5               matrixStats_1.1.0          
 [65] stats4_4.3.3                base64enc_0.1-3            
 [67] spatstat.explore_3.2-6      jsonlite_1.8.8             
 [69] tidygraph_1.3.0             ellipsis_0.3.2             
 [71] progressr_0.14.0            ggalluvial_0.12.5          
 [73] ggridges_0.5.6              survival_3.7-0             
 [75] ggnewscale_0.5.0            tools_4.3.3                
 [77] stringdist_0.9.12           ica_1.0-3                  
 [79] Rcpp_1.0.13                 glue_1.8.0                 
 [81] gridExtra_2.3               SparseArray_1.2.2          
 [83] MatrixGenerics_1.14.0       usethis_2.2.3              
 [85] GenomeInfoDb_1.38.1         IRdisplay_1.1              
 [87] dplyr_1.1.4                 withr_3.0.1                
 [89] fastmap_1.2.0               fansi_1.0.6                
 [91] SparseM_1.84                digest_0.6.37              
 [93] R6_2.5.1                    mime_0.12                  
 [95] colorspace_2.1-1            scattermore_1.2            
 [97] tensor_1.5                  spatstat.data_3.1-2        
 [99] utf8_1.2.4                  tidyr_1.3.1                
[101] generics_0.1.3              data.table_1.15.4          
[103] graphlayouts_1.1.0          httr_1.4.7                 
[105] htmlwidgets_1.6.4           S4Arrays_1.2.0             
[107] uwot_0.1.16                 pkgconfig_2.0.3            
[109] gtable_0.3.5                lmtest_0.9-40              
[111] SingleCellExperiment_1.24.0 XVector_0.42.0             
[113] htmltools_0.5.8.1           profvis_0.3.8              
[115] dotCall64_1.2               scales_1.3.0               
[117] Biobase_2.62.0              png_0.1-8                  
[119] ggdendro_0.2.0              rstudioapi_0.16.0          
[121] rjson_0.2.21                reshape2_1.4.4             
[123] uuid_1.2-0                  nlme_3.1-165               
[125] GlobalOptions_0.1.2         repr_1.1.7                 
[127] cachem_1.1.0                zoo_1.8-12                 
[129] stringr_1.5.1               KernSmooth_2.23-24         
[131] parallel_4.3.3              miniUI_0.1.1.1             
[133] desc_1.4.3                  pillar_1.9.0               
[135] grid_4.3.3                  vctrs_0.6.5                
[137] RANN_2.6.1                  VGAM_1.1-12                
[139] urlchecker_1.0.1            promises_1.3.0             
[141] xtable_1.8-4                cluster_2.1.6              
[143] evaluate_0.24.0             truncdist_1.0-2            
[145] cli_3.6.3                   compiler_4.3.3             
[147] rlang_1.1.4                 crayon_1.5.3               
[149] future.apply_1.11.2         forcats_1.0.0              
[151] plyr_1.8.9                  fs_1.6.4                   
[153] stringi_1.8.7               viridisLite_0.4.2          
[155] deldir_2.0-4                assertthat_0.2.1           
[157] gsl_2.1-8                   munsell_0.5.1              
[159] lazyeval_0.2.2              devtools_2.4.5             
[161] spatstat.geom_3.2-9         quantreg_5.98              
[163] Matrix_1.6-5                IRkernel_1.3.2             
[165] RcppHNSW_0.6.0              patchwork_1.3.0            
[167] future_1.34.0               ggplot2_3.5.1              
[169] shiny_1.8.1.1               plotthis_0.7.0             
[171] SummarizedExperiment_1.32.0 evd_2.3-7.1                
[173] ROCR_1.0-11                 gridtext_0.1.5             
[175] igraph_1.5.1                memoise_2.0.1