R Under development (unstable) (2024-10-19 r87247 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> # Load necessary libraries
> library(diceplot)
> library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

> library(ggplot2)
> library(tidyr)
> 
> # Define genes
> gene_list <- c("GeneA", "GeneB", "GeneC")
> 
> # Define cell types
> cell_types <- c("Neuron", "Astrocyte", "Microglia")
> 
> # Define Contrasts
> contrasts <- c("Type1", "Type2")  # Changed for demonstration
> 
> # Define vars for each Contrast
> vars_type1 <- c("MCI-NCI", "AD-MCI", "AD-NCI")
> vars_type2 <- c("Amyloid", "Plaq N", "Tangles", "NFT")
> 
> # Create a data frame with all combinations
> data <- expand.grid(
+   gene = gene_list,
+   Cell_Type = cell_types,  # Renamed column
+   Group = contrasts,       # Renamed column
+   stringsAsFactors = FALSE
+ )
> 
> # Add the appropriate vars to each Contrast
> set.seed(123) # Ensure reproducibility
> data_type1 <- data %>% 
+   filter(Group == "Type1") %>% 
+   mutate(var = sample(vars_type1, n(), replace = TRUE))
> 
> data_type2 <- data %>% 
+   filter(Group == "Type2") %>% 
+   mutate(var = sample(vars_type2, n(), replace = TRUE))
> 
> # Combine the data
> data <- bind_rows(data_type1, data_type2)
> 
> # Assign random values for logFC and adjusted p-values
> data <- data %>%
+   mutate(
+     logFC = runif(n(), min = -2, max = 2),  # Renamed column
+     adj_p_value = runif(n(), min = 0.0001, max = 0.05)
+   )
> 
> # Use the modified function with custom parameters
> p <- domino_plot(
+   data = data,
+   gene_list = gene_list,
+   feature_col = "gene",
+   celltype_col = "Cell_Type",
+   contrast_col = "Group",
+   contrast_levels = c("Type1", "Type2"),
+   contrast_labels = c("Type 1", "Type 2"),
+   logfc_col = "logFC",
+   pval_col = "adj_p_value",
+   switch_axis = FALSE,
+   min_dot_size = 1,
+   max_dot_size = 5,
+   output_file = "domino_plot_example.png"
+ )
Warning messages:
1: Removed 90 rows containing missing values or values outside the scale range
(`geom_point()`). 
2: Removed 90 rows containing missing values or values outside the scale range
(`geom_point()`). 
> 
> # Display the plot
> print(p)
Warning messages:
1: Removed 90 rows containing missing values or values outside the scale range
(`geom_point()`). 
2: Removed 90 rows containing missing values or values outside the scale range
(`geom_point()`). 
> 
> proc.time()
   user  system elapsed 
   2.20    0.21    2.40