This vignette demonstrates how to load the stxBrain
spatial transcriptomics dataset from SeuratData, preprocess
it with Seurat (Hao et al.
2023), convert the resulting object into a
GraphSpace object using RGraphSpace (Sysbiolab Team 2026), align the graph
coordinates to the tissue image, and save the normalized
GraphSpace object.
This vignette aims to save the stxBrain for the further
analysis of cell-cell communication visualization. Please see the
vignette: Plotting
cell-cell communication in spatially resolved transcriptomics. This
framework follows the same procedures presented in RGraphSpace: Spatial
Data vignette.
# Check required versions
if (packageVersion("RGraphSpace") < "1.4.1") {
message("Need to update 'RGraphSpace' for this vignette")
remotes::install_github("sysbiolab/RGraphSpace")
}
if (packageVersion("PathwaySpace") < "1.4.1") {
message("Need to update 'PathwaySpace' for this vignette")
remotes::install_github("sysbiolab/PathwaySpace")
}
if (packageVersion("Seurat") < "5.5.0") {
message("Need to update 'Seurat' for this vignette")
remotes::install_github("satijalab/Seurat")
}library("RGraphSpace")## Carregando pacotes exigidos: ggplot2library("PathwaySpace")
library("Seurat")## Carregando pacotes exigidos: SeuratObject## Carregando pacotes exigidos: sp##
## Anexando pacote: 'SeuratObject'## Os seguintes objetos são mascarados por 'package:base':
##
## intersect, tlibrary("SeuratObject")
library("SeuratData")## ── Installed datasets ──────────────────────────────── SeuratData v0.2.2.9002 ──## ✔ stxBrain 0.1.2## ────────────────────────────────────── Key ─────────────────────────────────────## ✔ Dataset loaded successfully
## ❯ Dataset built with a newer version of Seurat than installed
## ❓ Unknown version of Seurat installedlibrary("patchwork")
library("dplyr")##
## Anexando pacote: 'dplyr'## Os seguintes objetos são mascarados por 'package:stats':
##
## filter, lag## Os seguintes objetos são mascarados por 'package:base':
##
## intersect, setdiff, setequal, unionlibrary("igraph")##
## Anexando pacote: 'igraph'## Os seguintes objetos são mascarados por 'package:dplyr':
##
## as_data_frame, groups, union## O seguinte objeto é mascarado por 'package:Seurat':
##
## components## Os seguintes objetos são mascarados por 'package:stats':
##
## decompose, spectrum## O seguinte objeto é mascarado por 'package:base':
##
## unionThe stxBrain dataset only needs to be installed once. To
check which datasets are already installed, run:
SeuratData::InstalledData()## Dataset Version Summary
## stxBrain.SeuratData stxBrain 0.1.2 10X Genomics Visium Mouse Brain Dataset
## species system ncells tech seurat default.dataset
## stxBrain.SeuratData mouse brain 12167 visium <NA> NA
## disk.datasets other.datasets
## stxBrain.SeuratData <NA> posterior1, posterior2, anterior1, anterior2
## notes
## stxBrain.SeuratData One sample split across four datasets as paired anterior/posterior slices
## Installed InstalledVersion
## stxBrain.SeuratData TRUE 0.1.2if("stxBrain" %in% SeuratData::InstalledData()$Dataset ){
message("stxBrain is already installed, no need to install it again.")
}else{
SeuratData::InstallData("stxBrain")
}## stxBrain is already installed, no need to install it again.LoadData() may print conversion warnings when loading
older SeuratData objects.
seurat_obj <- LoadData("stxBrain", type = "anterior1")## Validating object structure## Updating object slots## Ensuring keys are in the proper structure
## Ensuring keys are in the proper structure## Ensuring feature names don't have underscores or pipes## Updating slots in Spatial## Updating slots in anterior1## Warning: Not validating Centroids objects## Updated Centroids object 'centroids' in FOV 'anterior1'## Updated boundaries in FOV 'anterior1'## Validating object structure for Assay5 'Spatial'## Validating object structure for VisiumV2 'anterior1'## Object representation is consistent with the most current Seurat versionThe dataset is normalized using SCTransform(), reduced
using PCA, and clustered using the standard Seurat workflow. Please see
the spatial
transcriptomics vignette from Seurat.
seurat_obj <- SCTransform(seurat_obj, assay = "Spatial", verbose = FALSE)
seurat_obj <- RunPCA(seurat_obj, assay = "SCT", verbose = FALSE)
seurat_obj <- FindNeighbors(
object = seurat_obj,
reduction = "pca",
dims = 1:30
)## Computing nearest neighbor graph## Computing SNNseurat_obj <- FindClusters(
object = seurat_obj,
verbose = FALSE
)The as.GraphSpace() function converts the spatial
coordinates and associated metadata from the Seurat object into a
GraphSpace object.
gs <- as.GraphSpace(seurat_obj, space = "spatial",scale = "lowres")## Seurat object converted to GraphSpace:
## ℹ space=spatial, layer=default, features=17668, samples=2696, scale="lowres"
## Node spatial boundaries:
## ℹ x: [76, 493] (cols)
## ℹ y: [138, 541] (rows)If available, the tissue image can be extracted from the Seurat
object and assigned to the GraphSpace object.
gs_image(gs) <- SeuratObject::GetImage(seurat_obj, mode = "raster")## Image spatial boundaries:
## ℹ x: [1, 600] (cols)
## ℹ y: [1, 599] (rows)The graph coordinates are normalized to the image coordinate system. By default, this attempts to align the graph’s bottom-up coordinates with the image’s top-down matrix layout.
gs <- normalizeGraphSpace(gs)## Normalizing node coordinates to image space...
## Flipping y-coordinates...gs## A GraphSpace-class object for:
## IGRAPH d7bbd08 UN-- 2696 0 --
## + attr: x (v/n), y (v/n), name (v/c), nodeLabel (v/c), nodeSize (v/n),
## | cell (v/c), orig.ident (v/x), nCount_Spatial (v/n), nFeature_Spatial
## | (v/n), slice (v/n), region (v/c), nCount_SCT (v/n), nFeature_SCT
## | (v/n), SCT_snn_res.0.8 (v/x), seurat_clusters (v/x), arrowType (e/n)
## + features: 17668 (Xkr4, Sox17, Mrpl15, Lypla1, ...)The final object is saved as an .RData file.
save(gs, file = "stxbrain_Normalized_GraphSpace.RData")sessionInfo()## R version 4.5.3 (2026-03-11 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
## [3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
## [5] LC_TIME=Portuguese_Brazil.utf8
##
## time zone: America/Sao_Paulo
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] future_1.70.0 igraph_2.3.3
## [3] dplyr_1.2.1 patchwork_1.3.2
## [5] stxBrain.SeuratData_0.1.2 SeuratData_0.2.2.9002
## [7] Seurat_5.5.1 SeuratObject_5.4.0
## [9] sp_2.2-1 PathwaySpace_1.4.1
## [11] RGraphSpace_1.4.1 ggplot2_4.0.3
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.19.0
## [3] jsonlite_2.0.0 magrittr_2.0.5
## [5] spatstat.utils_3.2-3 ggbeeswarm_0.7.3
## [7] farver_2.1.2 rmarkdown_2.31
## [9] vctrs_0.7.3 ROCR_1.0-12
## [11] DelayedMatrixStats_1.32.0 spatstat.explore_3.8-1
## [13] S4Arrays_1.10.1 htmltools_0.5.9
## [15] SparseArray_1.10.10 sass_0.4.10
## [17] sctransform_0.4.3 parallelly_1.48.0
## [19] KernSmooth_2.23-26 bslib_0.11.0
## [21] htmlwidgets_1.6.4 ica_1.0-3
## [23] plyr_1.8.9 plotly_4.12.0
## [25] zoo_1.8-15 cachem_1.1.0
## [27] mime_0.13 lifecycle_1.0.5
## [29] pkgconfig_2.0.3 Matrix_1.7-4
## [31] R6_2.6.1 fastmap_1.2.0
## [33] MatrixGenerics_1.22.0 fitdistrplus_1.2-6
## [35] shiny_1.14.0 digest_0.6.39
## [37] colorspace_2.1-2 ggnewscale_0.5.2
## [39] S4Vectors_0.48.1 tensor_1.5.1
## [41] RSpectra_0.16-2 irlba_2.3.7
## [43] GenomicRanges_1.62.1 beachmat_2.26.0
## [45] progressr_1.0.0 spatstat.sparse_3.2-0
## [47] httr_1.4.8 polyclip_1.10-7
## [49] abind_1.4-8 compiler_4.5.3
## [51] withr_3.0.3 S7_0.2.2
## [53] fastDummies_1.7.6 MASS_7.3-65
## [55] DelayedArray_0.36.1 rappdirs_0.3.4
## [57] tools_4.5.3 vipor_0.4.7
## [59] lmtest_0.9-40 otel_0.2.0
## [61] beeswarm_0.4.0 httpuv_1.6.17
## [63] future.apply_1.20.2 goftest_1.2-3
## [65] glmGamPoi_1.22.0 glue_1.8.1
## [67] nlme_3.1-168 promises_1.5.0
## [69] grid_4.5.3 Rtsne_0.17
## [71] cluster_2.1.8.2 reshape2_1.4.5
## [73] generics_0.1.4 gtable_0.3.6
## [75] spatstat.data_3.1-9 tidyr_1.3.2
## [77] data.table_1.18.4 XVector_0.50.0
## [79] tidygraph_1.3.1 BiocGenerics_0.56.0
## [81] spatstat.geom_3.8-1 RcppAnnoy_0.0.23
## [83] ggrepel_0.9.8 RANN_2.6.2
## [85] pillar_1.11.1 stringr_1.6.0
## [87] spam_2.11-4 RcppHNSW_0.7.0
## [89] later_1.4.8 splines_4.5.3
## [91] lattice_0.22-9 survival_3.8-6
## [93] deldir_2.0-4 tidyselect_1.2.1
## [95] miniUI_0.1.2 pbapply_1.7-4
## [97] knitr_1.51 gridExtra_2.3.1
## [99] Seqinfo_1.0.0 IRanges_2.44.0
## [101] SummarizedExperiment_1.40.0 scattermore_1.2
## [103] stats4_4.5.3 xfun_0.59
## [105] Biobase_2.70.0 matrixStats_1.5.0
## [107] stringi_1.8.7 lazyeval_0.2.3
## [109] yaml_2.3.12 evaluate_1.0.5
## [111] codetools_0.2-20 tibble_3.3.1
## [113] cli_3.6.6 uwot_0.2.4
## [115] xtable_1.8-8 reticulate_1.46.0
## [117] jquerylib_0.1.4 Rcpp_1.1.2
## [119] globals_0.19.1 spatstat.random_3.5-0
## [121] png_0.1-9 ggrastr_1.0.2
## [123] spatstat.univar_3.2-0 parallel_4.5.3
## [125] dotCall64_1.2 sparseMatrixStats_1.22.0
## [127] listenv_1.0.0 viridisLite_0.4.3
## [129] scales_1.4.0 ggridges_0.5.7
## [131] purrr_1.2.2 crayon_1.5.3
## [133] rlang_1.2.0 cowplot_1.2.0