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| library(ggplot2)
library(dplyr)
library(ggforce)
library(tidyr)
library(ggsci)
merge_meta <- function(df, species_name){
df %>%
mutate(
species = species_name,
comparison_group = case_when(
region_lev1 == "fovea_central" ~ "Central(Fovea)",
region == "peripheral1" ~ "Peripheral(1)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(comparison_group)) %>%
group_by(species, comparison_group, cluster) %>%
summarise(cell_count = n(), .groups = "drop") %>%
select(species, comparison_group, cluster, cell_count)
}
meta_info_Larg <- read.table("./Larg_meta_info.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE)
meta_info_Pcarbo <- read.table(".Pcarbo_meta_info.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE)
plot_data <- rbind(
merge_meta(meta_info_Larg, "Larg"),
merge_meta(meta_info_Pcarbo, "Pcarbo")
) %>%
complete(species, comparison_group, cluster, fill = list(cell_count = 0))
p <- ggplot(plot_data, aes(x = cluster, y = cell_count, fill = species)) +
geom_bar(stat = "identity",
position = position_dodge(width = 0.8),
width = 0.7,
color = "white",
linewidth = 0.2) +
geom_text(
aes(label = cell_count),
position = position_dodge(width = 0.8),
vjust = -0.5,
size = 3,
fontface = "bold"
) +
facet_wrap(~comparison_group, scales = "free_y", ncol = 1) +
scale_fill_npg() +
labs(
title = "Cross-Species Cell Count Comparison",
subtitle = "Central(Fovea) vs. Peripheral(1)",
x = "Cell Cluster",
y = "Cell Count",
fill = "Species Name"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 10, color = "black"),
axis.title = element_text(face = "bold", size = 12),
strip.text = element_text(face = "bold", size = 12, color = "white"),
strip.background = element_rect(fill = "#4575b4", color = NA),
panel.grid.major.x = element_blank(),
legend.position = "top",
plot.title = element_text(hjust = 0.5, face = "bold", size = 15)
)
dev.off()
ggsave("Larg_Pcarbo_comparison.pdf", p, width = 10, height = 10)
|
