# Load necessary libraries
library(diceplot)
library(dplyr)
library(ggplot2)
library(tidyr)
# library(diceplot) # Ensure this library is installed or remove if not needed

# Define genes
gene_list <- c("GeneA", "GeneB", "GeneC")

# Define cell types
cell_types <- c("Neuron", "Astrocyte", "Microglia")

# Define Contrasts
contrasts <- c("Clinical", "Pathological")

# Define vars for each Contrast
vars_clinical <- c("MCI-NCI", "AD-MCI", "AD-NCI")
vars_pathological <- c("Amyloid", "Plaq N", "Tangles", "NFT")

# Create a data frame with all combinations
data <- expand.grid(
  gene = gene_list,
  Celltype = cell_types,
  Contrast = contrasts,
  stringsAsFactors = FALSE
)

# Add the appropriate vars to each Contrast
set.seed(123) # Ensure reproducibility
data_clinical <- data %>% 
  filter(Contrast == "Clinical") %>% 
  mutate(var = sample(vars_clinical, n(), replace = TRUE))

data_pathological <- data %>% 
  filter(Contrast == "Pathological") %>% 
  mutate(var = sample(vars_pathological, n(), replace = TRUE))

# Combine the data
data <- bind_rows(data_clinical, data_pathological)

# Assign random values for avg_log2FC and p_val_adj
data <- data %>%
  mutate(
    avg_log2FC = runif(n(), min = -2, max = 2),
    p_val_adj = runif(n(), min = 0.0001, max = 0.05)
  )

# Use the corrected function
p <- domino_plot(
  data = data,
  gene_list = gene_list,
  switch_axis = FALSE,
  min_dot_size = 1,
  max_dot_size = 5,
  output_file = "domino_plot_example.png"
)

# Display the plot
print(p)