SpaTalk Visualization Guide
Introduction
SpaTalk provides a comprehensive suite of visualization functions for exploring spatial transcriptomics data and cell-cell communication results. This vignette demonstrates the key plotting functions with real examples.
Setup
library(SpaTalk)
library(ggplot2)
# Load demo data
load(system.file("extdata", "starmap_data.rda", package = "SpaTalk"))
load(system.file("extdata", "starmap_meta.rda", package = "SpaTalk"))
data(lrpairs)
data(pathways)
# Create SpaTalk object
st_meta <- data.frame(
cell = starmap_meta$cell,
x = starmap_meta$x,
y = starmap_meta$y
)
obj <- createSpaTalk(
st_data = starmap_data,
st_meta = st_meta,
species = "Mouse",
if_st_is_sc = TRUE,
spot_max_cell = 1,
celltype = starmap_meta$celltype
)
obj <- find_lr_path(obj, lrpairs, pathways, if_doParallel = FALSE)
#> Checking input data
#> Begin to filter lrpairs and pathways
#> ***Done***
# Show available cell types
cat("Available cell types:", paste(unique(starmap_meta$celltype), collapse = ", "), "\n")
#> Available cell types: eL2_3, eL6, Astro, PVALB, Endo, VIP, SST, Smc, eL4, Micro, Oligo, eL5, Reln, HPCSpatial Cell Type Visualization
plot_st_celltype_all
Display all cell types in a single spatial plot. This is one of the most commonly used visualizations.
All cell types in spatial context
plot_st_celltype
Visualize specific cell type distributions in spatial coordinates.
Spatial distribution of eL6 cells
Gene Expression Visualization
plot_st_gene
Visualize gene expression patterns in spatial coordinates.
# Get available genes
genes <- rownames(obj@data$rawdata)
cat("Total genes:", length(genes), "\n")
#> Total genes: 996
# Plot first available gene
plot_st_gene(
object = obj,
gene = genes[1],
size = 1.5
)
Spatial gene expression
Advanced Visualizations (After CCI Analysis)
The following visualizations require running dec_cci()
or dec_cci_all() first:
# Run CCI analysis for demonstration
obj <- dec_cci(
object = obj,
celltype_sender = "eL6",
celltype_receiver = "PVALB",
if_doParallel = FALSE
)
#> Begin to find LR pairs
# Check results
if(nrow(obj@lrpair) > 0) {
cat("Found", nrow(obj@lrpair), "significant LR pairs\n")
print(head(obj@lrpair[, c("ligand", "receptor", "lr_co_ratio", "score")]))
}
#> Found 1 significant LR pairs
#> ligand receptor lr_co_ratio score
#> 5 Inhba Acvr1c 0.1666667 0.8550196plot_ccdist
Distribution of distances between interacting cell types.
Cell-cell distance distribution between eL6 and PVALB
plot_lrpair
Spatial visualization of specific ligand-receptor pair interactions (if significant pairs found).
if(nrow(obj@lrpair) > 0) {
lr <- obj@lrpair[1, ]
plot_lrpair(
object = obj,
ligand = lr$ligand,
receptor = lr$receptor,
celltype_sender = "eL6",
celltype_receiver = "PVALB",
size = 1.2
)
} else {
cat("No significant LR pairs found for visualization\n")
}
Spatial LR pair visualization
Customization Tips
Custom Themes
SpaTalk uses ggplot2 for all visualizations. You can easily customize:
p <- plot_st_celltype_all(obj, size = 1)
p +
theme_minimal() +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5, size = 14, face = "bold")
) +
labs(title = "STARmap Cell Type Distribution")
Customized plot with different theme
Summary of Visualization Functions
| Function | Description | Input Required |
|---|---|---|
plot_st_celltype |
Single cell type spatial | SpaTalk object |
plot_st_celltype_all |
All cell types spatial | SpaTalk object |
plot_st_celltype_percent |
Pie chart per spot | Deconvolved object |
plot_st_celltype_density |
Density heatmap | SpaTalk object |
plot_st_gene |
Gene expression spatial | SpaTalk object |
plot_st_pie |
Pie chart composition | Deconvolved object |
plot_cci_lrpairs |
CCI chord diagram | After dec_cci |
plot_lrpair |
LR pair spatial | After dec_cci |
plot_lrpair_vln |
LR violin plot | After dec_cci |
plot_lr_path |
LR-pathway network | After dec_cci |
plot_path2gene |
Pathway heatmap | After dec_cci |
plot_st_cor_heatmap |
Correlation heatmap | SpaTalk object |
plot_ccdist |
Distance distribution | SpaTalk object |
Session Info
sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [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: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] parallel stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] SpaTalk_2.0.0 doParallel_1.0.17 iterators_1.0.14 foreach_1.5.2
#> [5] ggalluvial_0.12.6 ggplot2_4.0.3 rmarkdown_2.31
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 sys_3.4.3 jsonlite_2.0.0
#> [4] magrittr_2.0.5 spatstat.utils_3.2-3 farver_2.1.2
#> [7] fs_2.1.0 vctrs_0.7.3 ROCR_1.0-12
#> [10] spatstat.explore_3.8-1 rstatix_0.7.3 htmltools_0.5.9
#> [13] progress_1.2.3 broom_1.0.13 Formula_1.2-5
#> [16] sass_0.4.10 sctransform_0.4.3 parallelly_1.48.0
#> [19] KernSmooth_2.23-26 bslib_0.11.0 htmlwidgets_1.6.4
#> [22] ica_1.0-3 plyr_1.8.9 plotly_4.12.0
#> [25] zoo_1.8-15 cachem_1.1.0 buildtools_1.0.0
#> [28] igraph_2.3.3 mime_0.13 lifecycle_1.0.5
#> [31] pkgconfig_2.0.3 Matrix_1.7-5 R6_2.6.1
#> [34] fastmap_1.2.0 fitdistrplus_1.2-6 future_1.70.0
#> [37] shiny_1.14.0 digest_0.6.39 patchwork_1.3.2
#> [40] Seurat_5.5.1 tensor_1.5.1 RSpectra_0.16-2
#> [43] irlba_2.3.7 ggpubr_0.6.3 labeling_0.4.3
#> [46] progressr_0.19.0 spatstat.sparse_3.2-0 httr_1.4.8
#> [49] polyclip_1.10-7 abind_1.4-8 compiler_4.6.1
#> [52] withr_3.0.3 backports_1.5.1 S7_0.2.2
#> [55] carData_3.0-6 fastDummies_1.7.6 ggforce_0.5.0
#> [58] ggsignif_0.6.4 MASS_7.3-65 rappdirs_0.3.4
#> [61] ggsci_5.1.0 tools_4.6.1 lmtest_0.9-40
#> [64] otel_0.2.0 scatterpie_0.2.6 httpuv_1.6.17
#> [67] future.apply_1.20.2 goftest_1.2-3 glue_1.8.1
#> [70] nlme_3.1-169 promises_1.5.0 grid_4.6.1
#> [73] Rtsne_0.17 cluster_2.1.8.2 reshape2_1.4.5
#> [76] generics_0.1.4 isoband_0.3.0 gtable_0.3.6
#> [79] spatstat.data_3.1-9 tzdb_0.5.0 tidyr_1.3.2
#> [82] data.table_1.18.4 hms_1.1.4 car_3.1-5
#> [85] sp_2.2-1 spatstat.geom_3.8-1 RcppAnnoy_0.0.23
#> [88] ggrepel_0.9.8 RANN_2.6.2 pillar_1.11.1
#> [91] stringr_1.6.0 yulab.utils_0.2.4 ggExtra_0.11.0
#> [94] spam_2.11-4 RcppHNSW_0.7.0 later_1.4.8
#> [97] splines_4.6.1 tweenr_2.0.3 dplyr_1.2.1
#> [100] lattice_0.22-9 survival_3.8-6 deldir_2.0-4
#> [103] tidyselect_1.2.1 maketools_1.3.2 miniUI_0.1.2
#> [106] pbapply_1.7-4 knitr_1.51 gridExtra_2.3.1
#> [109] scattermore_1.2 xfun_0.59 matrixStats_1.5.0
#> [112] pheatmap_1.0.13 stringi_1.8.7 ggfun_0.2.1
#> [115] lazyeval_0.2.3 yaml_2.3.12 evaluate_1.0.5
#> [118] codetools_0.2-20 tibble_3.3.1 cli_3.6.6
#> [121] uwot_0.2.4 xtable_1.8-8 reticulate_1.46.0
#> [124] jquerylib_0.1.4 Rcpp_1.1.1-1.1 globals_0.19.1
#> [127] spatstat.random_3.5-0 png_0.1-9 spatstat.univar_3.2-0
#> [130] readr_2.2.0 prettyunits_1.2.0 dotCall64_1.2
#> [133] listenv_1.0.0 viridisLite_0.4.3 scales_1.4.0
#> [136] ggridges_0.5.7 SeuratObject_5.4.0 purrr_1.2.2
#> [139] crayon_1.5.3 rlang_1.2.0 cowplot_1.2.0