scplotter to work with SlideSeq data prepared by Seurat¶

See: https://satijalab.org/seurat/articles/spatial_vignette#slide-seq

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

In [1]:
options(future.globals.maxSize = 512 * 1024^3) # 512 GB
suppressPackageStartupMessages({
    library(Seurat)
    library(SeuratData)
})
# Load the scplotter package
# library(scplotter)
devtools::load_all()
# devtools::load_all("../../../plotthis")

suppressWarnings(suppressMessages({
    # InstallData("ssHippo")

    slide.seq <- LoadData("ssHippo")
    slide.seq <- SCTransform(slide.seq, assay = "Spatial", ncells = 3000, verbose = FALSE)
    slide.seq <- RunPCA(slide.seq)
    slide.seq <- RunUMAP(slide.seq, dims = 1:30)
    slide.seq <- FindNeighbors(slide.seq, dims = 1:30)
    slide.seq <- FindClusters(slide.seq, resolution = 0.3, verbose = FALSE)
}))
# slide.seq <- qs2::qs_read("data/slide.seq.qs")
slide.seq

ℹ Loading scplotter

An object of class Seurat 
42639 features across 53173 samples within 2 assays 
Active assay: SCT (19375 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: Spatial
 2 dimensional reductions calculated: pca, umap
 1 image present: image
In [2]:
options(repr.plot.width = 12, repr.plot.height = 6)

slide.seq$log_nCount_Spatial <- log(slide.seq$nCount_Spatial)
p1 <- FeatureStatPlot(slide.seq, features = "log_nCount_Spatial",
    ident = "orig.ident", add_point = TRUE, legend.position = "none")
p2 <- SpatFeaturePlot(slide.seq, features = "log_nCount_Spatial", points_size = 0.5)

p1 + p2
No description has been provided for this image
In [3]:
options(repr.plot.width = 12, repr.plot.height = 6)

p1 <- CellDimPlot(slide.seq, reduction = "umap", label = TRUE)
p2 <- SpatDimPlot(slide.seq, points_size = 0.5)

p1 + p2
No description has been provided for this image
In [4]:
options(repr.plot.width = 6, repr.plot.height = 5)

SpatDimPlot(slide.seq, highlight = "seurat_clusters == 5",
    highlight_color = "red", points_size = 0.5, highlight_size = 0.2)
No description has been provided for this image
In [7]:
options(repr.plot.width = 12, repr.plot.height = 8)

SpatFeaturePlot(slide.seq, size = 0.1,
    features = c("PCP4", "TTR", "PRKCD", "GM5741", "NWD2", "DDN"))
No description has been provided for this image
In [8]:
options(repr.plot.width = 12, repr.plot.height = 8)

SpatFeaturePlot(slide.seq, size = 0.1, upper_quantile = 0.95,
    features = c("PCP4", "TTR", "PRKCD", "GM5741", "NWD2", "DDN"))
No description has been provided for this image
In [6]:
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] fs_1.6.4                    matrixStats_1.1.0          
  [3] spatstat.sparse_3.1-0       bitops_1.0-7               
  [5] sf_1.0-20                   devtools_2.4.5             
  [7] httr_1.4.7                  RColorBrewer_1.1-3         
  [9] repr_1.1.7                  profvis_0.3.8              
 [11] tools_4.3.3                 sctransform_0.4.1          
 [13] utf8_1.2.4                  R6_2.5.1                   
 [15] lazyeval_0.2.2              uwot_0.1.16                
 [17] urlchecker_1.0.1            withr_3.0.1                
 [19] gridExtra_2.3               progressr_0.14.0           
 [21] quantreg_5.98               cli_3.6.3                  
 [23] Biobase_2.62.0              Cairo_1.6-2                
 [25] spatstat.explore_3.2-6      fastDummies_1.7.3          
 [27] iNEXT_3.0.1                 labeling_0.4.3             
 [29] spatstat.data_3.1-2         proxy_0.4-27               
 [31] ggridges_0.5.6              pbapply_1.7-2              
 [33] pbdZMQ_0.3-11               stringdist_0.9.12          
 [35] parallelly_1.38.0           sessioninfo_1.2.2          
 [37] VGAM_1.1-12                 rstudioapi_0.16.0          
 [39] generics_0.1.3              shape_1.4.6.1              
 [41] ica_1.0-3                   spatstat.random_3.2-3      
 [43] dplyr_1.1.4                 Matrix_1.6-5               
 [45] fansi_1.0.6                 S4Vectors_0.40.2           
 [47] abind_1.4-5                 terra_1.8-42               
 [49] lifecycle_1.0.4             SummarizedExperiment_1.32.0
 [51] SparseArray_1.2.2           Rtsne_0.17                 
 [53] glmGamPoi_1.14.0            grid_4.3.3                 
 [55] promises_1.3.0              crayon_1.5.3               
 [57] miniUI_0.1.1.1              lattice_0.22-6             
 [59] cowplot_1.1.3               pillar_1.9.0               
 [61] GenomicRanges_1.54.1        rjson_0.2.21               
 [63] future.apply_1.11.2         codetools_0.2-20           
 [65] glue_1.8.0                  data.table_1.15.4          
 [67] remotes_2.5.0               vctrs_0.6.5                
 [69] png_0.1-8                   spam_2.11-0                
 [71] gtable_0.3.5                assertthat_0.2.1           
 [73] cachem_1.1.0                S4Arrays_1.2.0             
 [75] mime_0.12                   tidygraph_1.3.0            
 [77] survival_3.7-0              SingleCellExperiment_1.24.0
 [79] units_0.8-5                 ellipsis_0.3.2             
 [81] fitdistrplus_1.1-11         scRepertoire_2.2.1         
 [83] ROCR_1.0-11                 nlme_3.1-165               
 [85] usethis_2.2.3               RcppAnnoy_0.0.22           
 [87] evd_2.3-7.1                 GenomeInfoDb_1.38.1        
 [89] rprojroot_2.0.4             irlba_2.3.5.1              
 [91] KernSmooth_2.23-24          DBI_1.2.3                  
 [93] plotthis_0.7.1              colorspace_2.1-1           
 [95] BiocGenerics_0.48.1         tidyselect_1.2.1           
 [97] compiler_4.3.3              SparseM_1.84               
 [99] xml2_1.3.6                  desc_1.4.3                 
[101] ggdendro_0.2.0              DelayedArray_0.28.0        
[103] plotly_4.10.4               scales_1.3.0               
[105] classInt_0.4-10             lmtest_0.9-40              
[107] rappdirs_0.3.3              stringr_1.5.1              
[109] digest_0.6.37               goftest_1.2-3              
[111] spatstat.utils_3.1-1        XVector_0.42.0             
[113] htmltools_0.5.8.1           pkgconfig_2.0.3            
[115] base64enc_0.1-3             sparseMatrixStats_1.14.0   
[117] MatrixGenerics_1.14.0       fastmap_1.2.0              
[119] rlang_1.1.4                 GlobalOptions_0.1.2        
[121] htmlwidgets_1.6.4           shiny_1.8.1.1              
[123] DelayedMatrixStats_1.24.0   farver_2.1.2               
[125] zoo_1.8-12                  jsonlite_1.8.8             
[127] RCurl_1.98-1.13             magrittr_2.0.3             
[129] GenomeInfoDbData_1.2.11     dotCall64_1.2              
[131] patchwork_1.3.0             IRkernel_1.3.2             
[133] munsell_0.5.1               Rcpp_1.0.13                
[135] evmix_2.12                  ggnewscale_0.5.0           
[137] viridis_0.6.5               reticulate_1.38.0          
[139] truncdist_1.0-2             stringi_1.8.7              
[141] ggalluvial_0.12.5           ggraph_2.2.1               
[143] zlibbioc_1.48.0             MASS_7.3-60.0.1            
[145] plyr_1.8.9                  pkgbuild_1.4.4             
[147] parallel_4.3.3              listenv_0.9.1              
[149] ggrepel_0.9.6               forcats_1.0.0              
[151] deldir_2.0-4                graphlayouts_1.1.0         
[153] IRdisplay_1.1               splines_4.3.3              
[155] gridtext_0.1.5              tensor_1.5                 
[157] circlize_0.4.16             igraph_1.5.1               
[159] uuid_1.2-0                  spatstat.geom_3.2-9        
[161] cubature_2.1.1              RcppHNSW_0.6.0             
[163] reshape2_1.4.4              stats4_4.3.3               
[165] pkgload_1.3.4               evaluate_0.24.0            
[167] tweenr_2.0.3                httpuv_1.6.15              
[169] MatrixModels_0.5-3          RANN_2.6.1                 
[171] tidyr_1.3.1                 purrr_1.0.2                
[173] polyclip_1.10-6             future_1.34.0              
[175] scattermore_1.2             ggplot2_3.5.1              
[177] ggforce_0.4.2               xtable_1.8-4               
[179] e1071_1.7-14                RSpectra_0.16-1            
[181] later_1.3.2                 class_7.3-22               
[183] viridisLite_0.4.2           gsl_2.1-8                  
[185] tibble_3.2.1                memoise_2.0.1              
[187] IRanges_2.36.0              cluster_2.1.6              
[189] globals_0.16.3