Visualization Gallery
Introduction
CytoSPACER provides comprehensive visualization functions built on
ggplot2. This vignette showcases the various plotting
options available for exploring and presenting your results.
Generate Example Data
First, let’s create a simulated dataset:
set.seed(123)
# Parameters
n_genes <- 100
n_cells <- 500
n_spots <- 50
# Create scRNA-seq data
sc_data <- matrix(rpois(n_genes * n_cells, lambda = 5), nrow = n_genes, ncol = n_cells)
rownames(sc_data) <- paste0("Gene", seq_len(n_genes))
colnames(sc_data) <- paste0("Cell", seq_len(n_cells))
# Define cell types
cell_types <- rep(c("TypeA", "TypeB", "TypeC", "TypeD", "TypeE"), each = 100)
names(cell_types) <- colnames(sc_data)
# Add cell type-specific expression
for (i in 1:5) {
marker_genes <- ((i-1)*20 + 1):(i*20)
type_cells <- which(cell_types == unique(cell_types)[i])
sc_data[marker_genes, type_cells] <- sc_data[marker_genes, type_cells] + 20
}
# Create ST data
st_data <- matrix(rpois(n_genes * n_spots, lambda = 50), nrow = n_genes, ncol = n_spots)
rownames(st_data) <- paste0("Gene", seq_len(n_genes))
colnames(st_data) <- paste0("Spot", seq_len(n_spots))
# Create spatial coordinates (grid pattern)
coordinates <- data.frame(
row = rep(1:10, each = 5),
col = rep(1:5, times = 10),
row.names = colnames(st_data)
)
# Create cell type fractions
unique_types <- unique(cell_types)
n_types <- length(unique_types)
cell_type_fractions <- as.data.frame(matrix(
1/n_types, nrow = n_spots, ncol = n_types,
dimnames = list(colnames(st_data), unique_types)
))
# Run CytoSPACER
results <- run_cytospace(
sc_data = sc_data,
cell_types = cell_types,
st_data = st_data,
coordinates = coordinates,
cell_type_fractions = cell_type_fractions,
mean_cells_per_spot = 5,
seed = 42,
verbose = FALSE
)Basic Spatial Plots
Cell Type Distribution
The primary visualization shows the spatial distribution of assigned cell types:
Customizing Point Size and Transparency
Adding Jitter
For dense regions where points overlap, add jitter:
Cell Type Composition Plots
Advanced Visualizations
Combining with ggplot2
Since all plots are ggplot2 objects, you can easily customize them:
p <- plot_cytospace(results, type = "cell_types", colors = my_colors)
# Add custom theme
p +
theme_minimal() +
theme(
legend.position = "bottom",
legend.title = element_text(face = "bold"),
plot.title = element_text(hjust = 0.5, size = 16, face = "bold")
) +
guides(color = guide_legend(nrow = 1, override.aes = list(size = 4)))
Faceted by Cell Type
# Get the assigned locations data
locs <- results$assigned_locations
locs$Y_plot <- -locs$col
# Create faceted plot
ggplot(locs, aes(x = row, y = Y_plot)) +
geom_point(aes(color = CellType), size = 1.5, alpha = 0.8) +
facet_wrap(~ CellType, ncol = 3) +
scale_color_manual(values = my_colors) +
coord_equal() +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
strip.text = element_text(face = "bold", size = 11),
legend.position = "none"
) +
labs(title = "Cell Type Distribution by Type")
Saving Plots
# Save individual plot
p <- plot_cytospace(results, type = "cell_types", colors = my_colors)
# Using CytoSPACER function
save_cytospace_plot(
plot = p,
output_dir = "figures/",
prefix = "spatial_",
formats = c("png", "pdf"),
width = 10,
height = 8,
dpi = 300
)
# Using ggplot2 directly
ggsave("figures/cell_types.png", p, width = 10, height = 8, dpi = 300)Color Palettes
CytoSPACER includes a built-in color palette optimized for distinguishing cell types:
# Default CytoSPACER colors (first 10)
default_colors <- c(
"#222222", "#F3C300", "#875692", "#F38400", "#A1CAF1",
"#BE0032", "#C2B280", "#848482", "#008856", "#E68FAC"
)
# Display palette
barplot(rep(1, 10), col = default_colors, border = NA, axes = FALSE,
main = "CytoSPACER Default Color Palette", names.arg = 1:10)
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] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_4.0.3 future_1.70.0 Matrix_1.7-5 CytoSPACER_1.0.0
#> [5] rmarkdown_2.31
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 future.apply_1.20.2 jsonlite_2.0.0
#> [4] dplyr_1.2.1 compiler_4.6.1 Rcpp_1.1.1-1.1
#> [7] tidyselect_1.2.1 parallel_4.6.1 jquerylib_0.1.4
#> [10] globals_0.19.1 scales_1.4.0 yaml_2.3.12
#> [13] fastmap_1.2.0 lattice_0.22-9 R6_2.6.1
#> [16] labeling_0.4.3 generics_0.1.4 knitr_1.51
#> [19] tibble_3.3.1 maketools_1.3.2 pillar_1.11.1
#> [22] bslib_0.11.0 RColorBrewer_1.1-3 rlang_1.2.0
#> [25] cachem_1.1.0 xfun_0.59 sass_0.4.10
#> [28] sys_3.4.3 S7_0.2.2 otel_0.2.0
#> [31] cli_3.6.6 withr_3.0.3 progressr_1.0.0
#> [34] magrittr_2.0.5 digest_0.6.39 grid_4.6.1
#> [37] lifecycle_1.0.5 vctrs_0.7.3 evaluate_1.0.5
#> [40] glue_1.8.1 data.table_1.18.4 farver_2.1.2
#> [43] listenv_1.0.0 codetools_0.2-20 buildtools_1.0.0
#> [46] parallelly_1.48.0 pkgconfig_2.0.3 tools_4.6.1
#> [49] htmltools_0.5.9