Plots for samples each requiring different point sizes

[amoore-vm]

I'm trying to generate spatial plots for a dataset that combines samples from 3 different projects (p21, adult, aged). When creating spatial plots, different point sizes need to be used for each sample. I would like to look at gene expression across all samples, but using the merged dataset, this means generating each plot 3 times for the 3 point sizes, and then using editing software to create a combined image. When looking at a lot of genes, this takes a lot of time. I've tried to subset the data, but then the scales are different in each plot, which makes cross-sample interpretation impossible. Does anyone have any ideas? Here is one iteration of the code. Note that I've tried all 3 options for keep.scale: "feature", "all", and NULL.

`#Define the minimum and maximum quartiles for the plots
global_min = "q05"
global_max = "q95"

for (gene_to_examine in gene_vector) {
adult_img<-SpatialFeaturePlot(adult_subset, features = gene_to_examine, keep.scale=NULL,
min.cutoff = global_min,
max.cutoff = global_max,
pt.size.factor = 2)

p21_img<-SpatialFeaturePlot(p21_subset, features = gene_to_examine, keep.scale=NULL,
min.cutoff = global_min,
max.cutoff = global_max,
pt.size.factor = 475)

aged_img<-SpatialFeaturePlot(aged_subset, features = gene_to_examine, keep.scale=NULL,
min.cutoff = global_min,
max.cutoff = global_max,
pt.size.factor = 3)

uniform_scale <- scale_fill_gradientn(
colors = rev(RColorBrewer::brewer.pal(n=5, name = "YlGnBu")), # Use a consistent color palette
limits = c(global_min, global_max) # Apply the global min and max limits
)

#uses patchwork, a Seurat function
combined_img <- (p21_img | adult_img | aged_img) + plot_layout(ncol = 1, nrow = 3)
ggsave(paste0("./Figures/Spatial_Gene/", gene_to_examine, "_combined.png"), height = 12, width = 6, units = 'in')

#ncol = 1 b/c each image (containing 2 samples) is a single image; need to define both ncol & nrow or won't run.
print(combined_img)
}`