ggdiceplot

# Install from local directory
devtools::install_local("path/to/ggdiceplot")

# Or install dependencies manually
install.packages(c("ggplot2", "tibble"))

install.packages(c("ggdiceplot"))

library(ggplot2)
library(ggdiceplot)

# Create sample data
data("sample_dice_data1", package = "ggdiceplot")
toy_data = sample_dice_data1
# Effect size
lo = floor(min(toy_data$lfc, na.rm = TRUE))
up = ceiling(max(toy_data$lfc, na.rm=TRUE))
mid = (lo + up)/2

minsize = floor(min(-log10(toy_data$q), na.rm=TRUE))
maxsize = ceiling(max(-log10(toy_data$q), na.rm=TRUE))
midsize = ceiling(quantile(-log10(toy_data$q), c(0.5), na.rm=TRUE))

#
## PLOT
#

ggplot(toy_data, aes(x=specimen, y=taxon)) +
  geom_dice(aes(dots=disease, fill=lfc, size=-log10(q), 
                # Square dims
                width = 0.5, height = 0.5),
            # For legend display
            show.legend=TRUE, 
            # For legend position calculation
            ndots=length(unique(toy_data$disease)),
            # For aspect.ratio: ensure squares (now automatic with coord_fixed)
            x_length = length(unique(toy_data$specimen)), 
            y_length = length(unique(toy_data$taxon)), 
  )+
  scale_fill_continuous(name="lfc") +
  scale_fill_gradient2(low = "#40004B", high = "#00441B", mid = "white",
                       na.value = "white", 
                       limit = c(lo, up),
                       midpoint = mid, 
                       name = "Log2FC") +
  scale_size_continuous(limits = c(minsize, maxsize),
                        breaks = c(minsize, midsize, maxsize),
                        labels = c(10^minsize, 10^-midsize, 10^-maxsize),
                        name = "q-value")

## ---- SETUP ----
library(ggplot2)
library(ggdiceplot)

## ---- LOAD SAMPLE DATA (from package) ----
# If internal dataset exists, use it; otherwise fall back to df_dice
if ("sample_dice_miRNA" %in% data(package="ggdiceplot")$results[, "Item"]) {
  data("sample_dice_miRNA", package = "ggdiceplot")
  df_dice <- sample_dice_miRNA
} else {
  message("sample_dice_miRNA not found in package — using inline example.")
  
  df_dice <- data.frame(
    miRNA = c("miR-1","miR-1","miR-1","miR-2","miR-2","miR-2","miR-3","miR-3"),
    Compound = c("Compound_1","Compound_1","Compound_2","Compound_1","Compound_2","Compound_3","Compound_3","Compound_1"),
    Organ = c("Lung","Brain","Liver","Lung","Liver","Brain","Liver","Brain"),
    log2FC = c(1.2,-0.8,0.3,-1.1,0.6,2,-0.5,1.5)
  )
  
  df_dice$direction <- ifelse(df_dice$log2FC > 0.5, "Up",
                              ifelse(df_dice$log2FC < -0.5, "Down","Unchanged"))
  
  df_dice$Organ     <- factor(df_dice$Organ, levels = c("Lung","Liver","Brain"))
  df_dice$direction <- factor(df_dice$direction, levels = c("Down","Unchanged","Up"))
  df_dice$Compound  <- factor(df_dice$Compound, levels = c("Compound_1","Compound_2","Compound_3"))
  df_dice$miRNA     <- factor(df_dice$miRNA)
}

## ---- COLORS ----
direction_colors <- c(
  Down      = "#2166ac",
  Unchanged = "grey80",
  Up        = "#b2182b"
)

## ---- PLOT ----
p_dice <- ggplot(df_dice, aes(x = miRNA, y = Compound)) +
  geom_dice(
    aes(
      dots  = Organ,
      fill  = direction,
      width = 0.8,
      height = 0.8
    ),
    show.legend = TRUE,
    ndots       = length(levels(df_dice$Organ)),
    x_length    = length(levels(df_dice$miRNA)),
    y_length    = length(levels(df_dice$Compound))
  ) +
  scale_fill_manual(values = direction_colors, name = "Regulation") +
  theme_dice() +
  theme(
    axis.text.x = element_text(angle = 0, hjust = 0.5, vjust = 0.5),
    axis.text.y = element_text(hjust = 1),
    panel.grid  = element_blank()
  ) +
  labs(
    title = "DicePlot: log2FC direction per miRNA, compound and organ",
    x = "miRNA",
    y = "Compound"
  )

print(p_dice)

library(ggplot2)
library(ggdiceplot)
library(dplyr)
library(tidyr)

# Load ZEBRA dataset
zebra.df <- read.csv("legacy examples/data/ZEBRA_sex_degs_set.csv")

# Select genes of interest
genes <- c("SPP1", "APOE", "SERPINA1", "PINK1", "ANGPT1", "ANGPT2", "APP", "CLU", "ABCA7")

# Filter and prepare data
zebra.df <- zebra.df %>% 
  filter(gene %in% genes) %>%
  filter(contrast %in% c("MS-CT", "AD-CT", "ASD-CT", "FTD-CT", "HD-CT")) %>%
  mutate(
    cell_type = factor(cell_type, levels = sort(unique(cell_type))),
    contrast = factor(contrast, levels = c("MS-CT", "AD-CT", "ASD-CT", "FTD-CT", "HD-CT")),
    gene = factor(gene, levels = genes)
  ) %>%
  filter(PValue < 0.05) %>%
  group_by(gene, cell_type, contrast) %>%
  summarise(
    logFC = mean(logFC, na.rm = TRUE),
    FDR = min(FDR, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  complete(gene, cell_type, contrast, fill = list(logFC = NA, FDR = NA))

# Calculate scale limits
lo <- floor(min(zebra.df$logFC, na.rm = TRUE))
up <- ceiling(max(zebra.df$logFC, na.rm = TRUE))
mid <- (lo + up) / 2

minsize <- floor(min(-log10(zebra.df$FDR), na.rm = TRUE))
maxsize <- ceiling(max(-log10(zebra.df$FDR), na.rm = TRUE))
midsize <- ceiling(quantile(-log10(zebra.df$FDR), c(0.5), na.rm = TRUE))

# Create domino plot
ggplot(zebra.df, aes(x = gene, y = cell_type)) +
  geom_dice(
    aes(
      dots = contrast,
      fill = logFC,
      size = -log10(FDR)
    ),
    na.rm = TRUE,
    show.legend = TRUE,
    ndots = 5,  # We have 5 contrasts
    x_length = length(genes),
    y_length = length(unique(zebra.df$cell_type))
  ) +
  scale_fill_gradient2(
    low = "#40004B",
    high = "#00441B",
    mid = "white",
    na.value = "white",
    limit = c(lo, up),
    midpoint = mid,
    name = "Log2FC"
  ) +
  scale_size_continuous(
    limits = c(minsize, maxsize),
    breaks = c(minsize, midsize, maxsize),
    labels = c(10^minsize, 10^-midsize, 10^-maxsize),
    name = "FDR"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
    axis.text.y = element_text(size = 12),
    legend.text = element_text(size = 12),
    legend.title = element_text(size = 12),
    legend.key = element_blank(),
    legend.key.size = unit(0.8, "cm")
  ) +
  labs(
    x = "Gene",
    y = "Cell Type",
    title = "ZEBRA Sex DEGs Domino Plot"
  )

cd demo_output
Rscript create_demo_plots.R
Rscript usage_examples.R

# Run the ZEBRA domino example
cd examples
Rscript zebra_domino_example.R