diff --git a/.Rbuildignore b/.Rbuildignore
new file mode 100644
index 0000000..04c946c
--- /dev/null
+++ b/.Rbuildignore
@@ -0,0 +1,15 @@
+^\.pixi
+^pixi\.toml$
+^pixi\.lock$
+^demo_output
+^legacy examples
+^test_simple_dice\.R$
+^test_fill_legend_fix\.R$
+^Rplots\.pdf$
+^\.github
+^\.DS_Store$
+^\.claude
+^CLAUDE\.md$
+^examples
+^README\.html$
+^data-raw$
diff --git a/DESCRIPTION b/DESCRIPTION
index edf85ad..46b635c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: ggdiceplot
 Title: DicePlot Visualization for ggplot2
-Version: 1.0.0
+Version: 1.1.0
 Authors@R: 
     person(
         given = "Matthias", 
@@ -20,15 +20,16 @@ Description: Provides ggplot2 extensions for creating dice-based visualizations
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 URL: https://github.com/maflot/ggdiceplot
 BugReports: https://github.com/maflot/ggdiceplot/issues
-Imports: 
+Imports:
+    dplyr,
     ggplot2 (>= 3.4.0),
     grid,
-    dplyr,
     legendry,
-    scales
+    scales,
+    tibble
 LazyData: true
-Depends: 
-    R (>= 4.0.0)
+Depends:
+    R (>= 4.1.0)
diff --git a/LICENSE b/LICENSE
index 191dd6d..7fbd856 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,21 +1,2 @@
-MIT License
-
-Copyright (c) 2024 Matthias Flotho
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
\ No newline at end of file
+YEAR: 2026
+COPYRIGHT HOLDER: Matthias Flotho
diff --git a/NAMESPACE b/NAMESPACE
index 4c45309..711e5f9 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -5,6 +5,9 @@ export(geom_dice)
 export(make_offsets)
 export(scale_dots_discrete)
 export(theme_dice)
+importFrom(dplyr,bind_rows)
+importFrom(dplyr,distinct)
+importFrom(dplyr,filter)
 importFrom(ggplot2,"%+replace%")
 importFrom(ggplot2,Geom)
 importFrom(ggplot2,aes)
@@ -15,6 +18,7 @@ importFrom(ggplot2,ggproto)
 importFrom(ggplot2,layer)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_grey)
+importFrom(grid,drawDetails)
 importFrom(grid,gpar)
 importFrom(grid,grobTree)
 importFrom(grid,nullGrob)
@@ -23,3 +27,5 @@ importFrom(grid,rectGrob)
 importFrom(grid,unit)
 importFrom(legendry,guide_legend_base)
 importFrom(scales,pal_hue)
+importFrom(tibble,column_to_rownames)
+importFrom(tibble,remove_rownames)
diff --git a/NEWS.md b/NEWS.md
new file mode 100644
index 0000000..ea8e368
--- /dev/null
+++ b/NEWS.md
@@ -0,0 +1,61 @@
+# ggdiceplot 1.1.0
+
+## New features
+
+* **`pip_scale` parameter** — New argument to `geom_dice()` that controls pip
+  diameter as a fraction (0–1) of the maximum available space inside each die
+  face. When `size` is constant (not mapped), all pips are drawn at
+  `pip_scale × max_diameter`. When `size` is mapped to a variable, pips scale
+  between 25% and `pip_scale` of the maximum pip diameter. Set `pip_scale = NULL`
+  to disable auto-scaling and use the raw `size` aesthetic (legacy behaviour).
+  Default: `0.75`.
+
+* **Deferred rendering via `DiceGrob`** — Pip sizing is now calculated at
+  grid draw time rather than ggplot build time, so pip diameters adapt
+  correctly to the final figure dimensions.
+
+* **Scale expansion for edge tiles** — `setup_data` now exposes
+  `xmin`/`xmax`/`ymin`/`ymax` so ggplot2 trains axis scales to include the
+  full tile area, preventing clipping of border tiles.
+
+* **New datasets** — `sample_dice_large` (480 rows, 60 taxa) and
+  `sample_dice_miRNA` (~90 rows, miRNA dysregulation) are included for
+  demonstrating high-density and categorical-fill use cases.
+
+## Breaking changes
+
+* **Minimum R version raised to 4.1.0** (was 4.0.0). R 4.1 is required for
+  the native pipe operator (`|>`) used internally.
+
+* **`tibble` is now a hard dependency** — added to `Imports` for
+  `column_to_rownames()` / `remove_rownames()`.
+
+* **`draw_panel` signature changed** — The method now takes explicit named
+  arguments (`na.rm`, `ndots`, `x_length`, `y_length`, `pip_scale`) instead of
+  `params, ...`. Code that subclassed `GeomDice` or called `draw_panel`
+  directly will need updating.
+
+* **Sample datasets restructured**:
+    - `sample_dice_data1`: 48 rows → 160 rows (8 taxa × 4 diseases × 5
+      specimens). Columns are the same but `lfc` and `q` may now contain `NA`.
+    - `sample_dice_data2`: 48 rows → 160 rows; the `replicate` column has been
+      removed. Column count changed from 6 to 5.
+
+* **Legend key for unmapped fill** — When `fill` is not mapped, legend keys now
+  draw a solid black dot instead of an empty circle, making spatial/dots-only
+  legends more readable.
+
+## Minor changes
+
+* `LICENSE` switched to CRAN-standard two-line format (`YEAR` / `COPYRIGHT
+  HOLDER`).
+* `.Rbuildignore` added to exclude pixi, demo output, test scripts, and other
+  non-package files from the R CMD build tarball.
+* `data-raw/` directory created; dataset-generation scripts moved there from
+  `data/`.
+* `pixi.toml` / `pixi.lock` added for reproducible environment management.
+* Demo scripts (`create_demo_plots.R`, `test_simple_dice.R`) rewritten to
+  exercise the new `pip_scale` feature and run from the project root.
+* README updated with new examples, a parameter table, and the published
+  Bioinformatics citation.
+* RoxygenNote bumped to 7.3.3.
diff --git a/R/geom-dice-ggprotto.R b/R/geom-dice-ggprotto.R
index 90b98b9..33f6d85 100644
--- a/R/geom-dice-ggprotto.R
+++ b/R/geom-dice-ggprotto.R
@@ -3,10 +3,10 @@
 #' @usage NULL
 #' @export
 GeomDice <- ggplot2::ggproto("GeomDice", ggplot2::Geom,
-                             
+
                              extra_aes = c("dots"),
                              required_aes = c("x", "y", "dots"),
-                             
+
                              default_aes = ggplot2::aes(
                                linewidth = 0.1,
                                linetype = 1,
@@ -20,8 +20,8 @@ GeomDice <- ggplot2::ggproto("GeomDice", ggplot2::Geom,
                                width = 0.5,
                                height = 0.5
                              ),
-                             
-                             extra_params = c("na.rm", "ndots", "x_length", "y_length"),
+
+                             extra_params = c("na.rm", "ndots", "x_length", "y_length", "pip_scale"),
                              
                              setup_data = function(data, params, ...) {
                                data$na.rm <- data$na.rm %||% params$na.rm
@@ -32,26 +32,40 @@ GeomDice <- ggplot2::ggproto("GeomDice", ggplot2::Geom,
                                if (is.factor(data$dots)) {
                                  attr(data, "dots_levels") <- levels(data$dots)
                                }
+
+                               # Expose tile extents so ggplot2 trains x/y scales to include the
+                               # full tile area, preventing panel clipping of edge tiles.
+                               # Note: default_aes values (width, height) are applied AFTER
+                               # setup_data in ggplot2 >=3.5, so we must supply the fallback.
+                               w <- data$width  %||% 0.5
+                               h <- data$height %||% 0.5
+                               data$xmin <- data$x - w / 2
+                               data$xmax <- data$x + w / 2
+                               data$ymin <- data$y - h / 2
+                               data$ymax <- data$y + h / 2
                                data
                              },
                              
                              draw_key = function(data, params, size) {
-                               # Always use filled circle (shape 21) to properly display fill colors in legend
-                               # This ensures that when fill is mapped to a discrete variable,
-                               # the legend shows the fill colors correctly
                                data$shape <- 21
-                               # Set stroke color to match fill for clean appearance, but only if fill is actually set
-                               # Check for NULL, NA, and also the string "NA" (which ggplot2 can pass when fill is unmapped)
                                if (!is.null(data$fill) && !is.na(data$fill)) {
+                                 # fill is mapped: match stroke to fill for a solid-coloured pip
                                  data$colour <- data$fill
+                               } else {
+                                 # fill is unmapped (spatial/dots-only legend): show solid black dot
+                                 data$fill   <- "black"
+                                 data$colour <- "black"
                                }
                                ggplot2::draw_key_point(data, params, size)
                              },
                              
-                             draw_panel = function(data, panel_params, coord, params, ...) {
+                             draw_panel = function(data, panel_params, coord,
+                                                      na.rm = FALSE, ndots = NULL,
+                                                      x_length = NULL, y_length = NULL,
+                                                      pip_scale = 0.75) {
                                data$x <- as.numeric(data$x)
                                data$y <- as.numeric(data$y)
-                               
+
                                tile_coords <- dplyr::distinct(data, x, y)
                                dots_levels <- attr(data, "dots_levels")
 
@@ -66,12 +80,12 @@ GeomDice <- ggplot2::ggproto("GeomDice", ggplot2::Geom,
                                  width = unique(data$width)[1],
                                  height = unique(data$height)[1]
                                )
-                               
+
                                offsets_mat <- offsets |>
                                  tibble::remove_rownames() |>
                                  tibble::column_to_rownames("key") |>
                                  as.matrix()
-                               
+
                                point_coords_list <- lapply(seq_len(nrow(tile_coords)), function(i) {
                                  coords <- sweep(offsets_mat, 2, as.matrix(tile_coords)[i, ], FUN = "+")
                                  df <- as.data.frame(coords)
@@ -80,18 +94,18 @@ GeomDice <- ggplot2::ggproto("GeomDice", ggplot2::Geom,
                                  df$key <- present_levels
                                  df
                                })
-                               
+
                                point_coords <- dplyr::bind_rows(point_coords_list)
                                point_coords$id <- paste0(point_coords$key, point_coords$x_coord, point_coords$y_coord)
-                               
+
                                # Use original dots_original to create point_id
                                data$point_id <- paste0(data$dots_original, data$x, data$y)
                                point_df <- dplyr::filter(point_coords, id %in% data$point_id)
-                               
+
                                # Precise attribute matching
                                attr_lookup <- data[!duplicated(data$point_id), ]
                                match_idx <- match(point_df$id, attr_lookup$point_id)
-                               
+
                                point_df$size <- attr_lookup$size[match_idx]
                                point_df$shape <- attr_lookup$shape[match_idx]
                                point_df$stroke <- attr_lookup$stroke[match_idx]
@@ -100,22 +114,22 @@ GeomDice <- ggplot2::ggproto("GeomDice", ggplot2::Geom,
                                point_df$colour <- attr_lookup$fill[match_idx]
                                point_df$group <- attr_lookup$group[match_idx]
                                point_df$PANEL <- 1
-                               
+
                                bad_mask <- is.infinite(point_df$size) | point_df$size <= 0
                                if (any(bad_mask, na.rm = TRUE)) {
                                  warning(sum(bad_mask, na.rm = TRUE),
                                          " zero(s)/negative(s)/infinitive(s) detected in dot size... converted to NA.")
                                  point_df$size[bad_mask] <- NA
                                }
-                               
-                               if (isTRUE(unique(data$na.rm))) {
+
+                               if (isTRUE(na.rm)) {
                                  na_mask <- is.na(point_df$size)
                                  if (any(na_mask)) {
                                    warning(sum(na_mask), " NA's detected in dot size. Removing them...")
                                    point_df <- dplyr::filter(point_df, !is.na(size))
                                  }
                                }
-                               
+
                                tile_df <- tile_coords
                                tile_df$width <- unique(data$width)
                                tile_df$height <- unique(data$height)
@@ -125,12 +139,124 @@ GeomDice <- ggplot2::ggproto("GeomDice", ggplot2::Geom,
                                tile_df$PANEL <- 1
                                tile_df$group <- seq_len(nrow(tile_coords))
                                tile_df$linewidth <- unique(data$linewidth)
-                               
-                               tile_df <- ggplot2::GeomTile$setup_data(tile_df, params)
-                               
-                               grid::gList(
-                                 ggplot2::GeomTile$draw_panel(tile_df, panel_params, coord, ...),
-                                 ggplot2::GeomPoint$draw_panel(point_df, panel_params, coord, ...)
+
+                               tile_df <- ggplot2::GeomTile$setup_data(tile_df, list())
+
+                               tile_width  <- unique(data$width)[1]
+                               tile_height <- unique(data$height)[1]
+                               min_half_tile <- min(tile_width / 2, tile_height / 2)
+
+                               # Compute minimum inter-pip distance and maximum absolute offset.
+                               if (nrow(offsets) > 1) {
+                                 pos_mat        <- as.matrix(offsets[, c("x", "y")])
+                                 dist_mat       <- as.matrix(dist(pos_mat))
+                                 diag(dist_mat) <- Inf
+                                 min_inter_pip  <- min(dist_mat)
+                               } else {
+                                 min_inter_pip  <- Inf
+                               }
+                               max_abs_offset <- if (nrow(offsets) > 0) max(pmax(abs(offsets$x), abs(offsets$y))) else 0
+
+                               # Tight-packing scale factor: at s_tight, pip border clearance equals
+                               # half the inter-pip distance (pips simultaneously touch each other
+                               # and tile borders). s_tight = 1 when inter-pip is the binding constraint.
+                               if (is.finite(min_inter_pip) && max_abs_offset > 0) {
+                                 s_tight <- min(1.0, min_half_tile / (max_abs_offset + min_inter_pip / 2))
+                               } else {
+                                 s_tight <- 1.0
+                               }
+
+                               # Maximum pip radius at tight-packing positions (both constraints).
+                               pip_radius_tight <- min(
+                                 min_half_tile - max_abs_offset * s_tight,
+                                 if (is.finite(min_inter_pip)) min_inter_pip * s_tight / 2 else min_half_tile
+                               )
+
+                               # Auto-scale pip size only when size is constant (not user-mapped).
+                               auto_scale <- length(unique(data$size)) == 1
+
+                               # Shift pip centers toward tile center proportionally to pip_scale,
+                               # so larger pips fit without clipping at tile borders.
+                               if (!is.null(pip_scale) && pip_scale > 0 && s_tight < 1) {
+                                 offset_scale <- 1 - pip_scale * (1 - s_tight)
+                                 point_df$x   <- point_df$x_coord + (point_df$x - point_df$x_coord) * offset_scale
+                                 point_df$y   <- point_df$y_coord + (point_df$y - point_df$y_coord) * offset_scale
+                               }
+
+                               dice_grob(
+                                 point_df       = point_df,
+                                 tile_df        = tile_df,
+                                 panel_params   = panel_params,
+                                 coord          = coord,
+                                 max_pip_radius = pip_radius_tight,
+                                 tile_width     = tile_width,
+                                 pip_scale       = pip_scale,
+                                 auto_scale     = auto_scale
                                )
                              }
 )
+
+# ---------------------------------------------------------------------------
+# DiceGrob: defers pip-size calculation to grid draw time so that we can
+# convert data-unit distances to physical mm via the live panel viewport.
+# ---------------------------------------------------------------------------
+
+dice_grob <- function(point_df, tile_df, panel_params, coord,
+                      max_pip_radius, tile_width, pip_scale, auto_scale) {
+  grid::grob(
+    point_df     = point_df,
+    tile_df      = tile_df,
+    panel_params = panel_params,
+    coord        = coord,
+    max_pip_radius = max_pip_radius,
+    tile_width   = tile_width,
+    pip_scale     = pip_scale,
+    auto_scale   = auto_scale,
+    cl = "DiceGrob"
+  )
+}
+
+#' @importFrom grid drawDetails
+#' @method drawDetails DiceGrob
+drawDetails.DiceGrob <- function(x, recording) {
+  point_df <- x$point_df
+
+  if (!is.null(x$pip_scale)) {
+    # Convert tile width from data units to mm using the live panel viewport.
+    panel_w_mm <- grid::convertUnit(grid::unit(1, "npc"), "mm", valueOnly = TRUE)
+
+    ref_df  <- data.frame(x = c(0, x$tile_width), y = c(0, 0), PANEL = 1L, group = 1L)
+    ref_npc <- x$coord$transform(ref_df, x$panel_params)
+    tile_w_mm <- abs(ref_npc$x[2] - ref_npc$x[1]) * panel_w_mm
+
+    # Maximum pip diameter in mm (accounts for inter-pip and border constraints).
+    max_pip_mm <- 2 * (x$max_pip_radius / x$tile_width) * tile_w_mm
+
+    if (x$auto_scale) {
+      # Constant size: all pips at pip_scale fraction of max.
+      pip_size_mm <- max_pip_mm * x$pip_scale
+      if (is.finite(pip_size_mm) && pip_size_mm > 0) {
+        point_df$size <- pip_size_mm
+      }
+    } else {
+      # Variable size: map to [min_fill, pip_scale] of max pip diameter.
+      # Default: smallest value -> 0.25, largest -> pip_scale (typically 1.0).
+      min_fill   <- 0.25
+      sizes      <- point_df$size
+      size_range <- range(sizes, na.rm = TRUE)
+      if (diff(size_range) > 0) {
+        normalized <- (sizes - size_range[1]) / diff(size_range)
+        fill_fracs <- min_fill + (x$pip_scale - min_fill) * normalized
+      } else {
+        fill_fracs <- rep(x$pip_scale, length(sizes))
+      }
+      point_df$size <- max_pip_mm * fill_fracs
+    }
+  }
+
+  # Drop NA-size rows: ggplot2 >=4.x gpar rejects mixed NA/non-NA fontsize.
+  point_df <- point_df[!is.na(point_df$size), ]
+
+  grid::grid.draw(ggplot2::GeomTile$draw_panel(x$tile_df, x$panel_params, x$coord))
+  grid::grid.draw(ggplot2::GeomPoint$draw_panel(point_df, x$panel_params, x$coord))
+}
diff --git a/R/geom-dice.R b/R/geom-dice.R
index 49f93ce..08edf7a 100644
--- a/R/geom-dice.R
+++ b/R/geom-dice.R
@@ -22,8 +22,14 @@
 #' @param show.legend Whether to include in legend.
 #' @param inherit.aes If `FALSE`, overrides the default aesthetics.
 #' @param ndots Integer (1–6): number of positions shown per dice.
-#' @param x_length, x_length Numeric: used for aspect ratio.
-#' @param y_length, y_length Numeric: used for aspect ratio.
+#' @param x_length Numeric: number of x categories (used for aspect ratio).
+#' @param y_length Numeric: number of y categories (used for aspect ratio).
+#' @param pip_scale Numeric (0–1): controls pip diameter relative to the
+#'   maximum available space. When `size` is constant (not mapped), all pips
+#'   are drawn at `pip_scale` fraction of the die face. When `size` is mapped
+#'   to a variable, pips scale between 0.25 (smallest value) and `pip_scale`
+#'   (largest value) of the maximum pip diameter. Default is `0.75`. Set to
+#'   `NULL` to disable auto-scaling and use the raw `size` aesthetic.
 #' @param ... Additional arguments passed to `layer()`.
 #'
 #' @return A `ggplot2` layer that draws dice with categorical dot encodings.
@@ -43,8 +49,9 @@
 geom_dice <- function(mapping = NULL, data = NULL,
                       stat = "identity", position = "identity",
                       ndots = NULL, x_length = NULL, y_length = NULL,
+                      pip_scale = 0.75,
                       na.rm = FALSE, show.legend = TRUE, inherit.aes = TRUE, ...) {
-  
+
   list(
     ggplot2::layer(
       geom = GeomDice,
@@ -58,7 +65,8 @@ geom_dice <- function(mapping = NULL, data = NULL,
         na.rm = na.rm,
         ndots = ndots,
         x_length = x_length,
-        y_length = y_length
+        y_length = y_length,
+        pip_scale = pip_scale
       )
     ),
     theme_dice(x_length = x_length, y_length = y_length),
@@ -68,7 +76,7 @@ geom_dice <- function(mapping = NULL, data = NULL,
       theme = ggplot2::theme(
         legend.background = ggplot2::element_rect(
           fill = "white", colour = "grey", linewidth = 0.5),
-        legend.key = ggplot2::element_rect(fill = "white"),
+        legend.key = ggplot2::element_rect(fill = NA, colour = NA),
         legend.key.spacing.x = grid::unit(0.1, "cm"),
         legend.key.spacing.y = grid::unit(0.5, "cm"),
         legend.text = ggplot2::element_text(hjust = 0.5),
diff --git a/R/ggdiceplot-package.R b/R/ggdiceplot-package.R
index 12956b2..6b186e9 100644
--- a/R/ggdiceplot-package.R
+++ b/R/ggdiceplot-package.R
@@ -26,6 +26,7 @@
 "_PACKAGE"
 
 ## usethis namespace: start
+#' @importFrom dplyr bind_rows distinct filter
 #' @importFrom ggplot2 aes
 #' @importFrom ggplot2 Geom
 #' @importFrom ggplot2 ggproto
@@ -36,5 +37,6 @@
 #' @importFrom grid pointsGrob
 #' @importFrom grid rectGrob
 #' @importFrom grid unit
+#' @importFrom tibble column_to_rownames remove_rownames
 ## usethis namespace: end
 NULL
diff --git a/R/sample_dice_data1.R b/R/sample_dice_data1.R
index 0f6d113..ea8e2e9 100644
--- a/R/sample_dice_data1.R
+++ b/R/sample_dice_data1.R
@@ -5,13 +5,13 @@
 #' It simulates log fold-change (LFC) and adjusted p-values (q-values) for a range of
 #' oral taxa across disease types and specimen sites.
 #'
-#' @format A data frame with 48 rows and 5 columns:
+#' @format A data frame with 160 rows and 5 columns:
 #' \describe{
-#'   \item{taxon}{Character. Microbial taxon name.}
-#'   \item{disease}{Character. Disease condition (e.g., Caries, Periodontitis, etc.).}
-#'   \item{specimen}{Character. Body site specimen (e.g., Saliva, Plaque).}
-#'   \item{lfc}{Numeric. Simulated log2 fold change value.}
-#'   \item{q}{Numeric. Simulated adjusted p-value (q-value).}
+#'   \item{taxon}{Character. Microbial taxon name (8 taxa).}
+#'   \item{disease}{Character. Disease condition (Caries, Periodontitis, Healthy, Gingivitis).}
+#'   \item{specimen}{Character. Body site specimen (Saliva, Plaque, Tongue, Buccal, Gingival).}
+#'   \item{lfc}{Numeric. Simulated log2 fold change value; may contain NA.}
+#'   \item{q}{Numeric. Simulated adjusted p-value (q-value); may contain NA.}
 #' }
 #'
 #' @usage data(sample_dice_data1)
diff --git a/R/sample_dice_data2.R b/R/sample_dice_data2.R
index 74bdb48..140892e 100644
--- a/R/sample_dice_data2.R
+++ b/R/sample_dice_data2.R
@@ -1,21 +1,16 @@
-#' Sample Dice Dataset 2 for Visualization with Replicates
+#' Sample Dice Dataset 2 for Visualization
 #'
 #' @description
-#' `sample_dice_data2` is a toy dataset designed to demonstrate the usage of `geom_dice()` 
-#' in more advanced scenarios. It simulates log2 fold-change (LFC) and adjusted p-values 
-#' (q-values) for common oral taxa across disease conditions and specimen types, 
-#' including replicates and missing values.
+#' `sample_dice_data2` is a toy dataset designed to demonstrate `geom_dice()`
+#' with multiple specimen types. It simulates log2 fold-change (LFC) and
+#' adjusted p-values (q-values) for oral taxa across disease conditions and
+#' specimen sites, with some missing values.
 #'
-#' This version filters for `replicate == 1` but retains structure from a more complex
-#' design, making it useful for testing visualizations that account for filtering,
-#' grouping, or handling of `NA` values.
-#'
-#' @format A data frame with 48 rows and 6 columns:
+#' @format A data frame with 160 rows and 5 columns:
 #' \describe{
-#'   \item{taxon}{Character. Microbial taxon name.}
-#'   \item{disease}{Character. Disease condition (e.g., Caries, Periodontitis).}
-#'   \item{specimen}{Character. Body site specimen (e.g., Saliva, Plaque).}
-#'   \item{replicate}{Integer. Experimental replicate ID. Only replicate 1 is included here.}
+#'   \item{taxon}{Character. Microbial taxon name (8 taxa).}
+#'   \item{disease}{Character. Disease condition (Caries, Periodontitis, Healthy, Gingivitis).}
+#'   \item{specimen}{Character. Body site specimen (Saliva, Plaque, Tongue, Buccal, Gingival).}
 #'   \item{lfc}{Numeric. Simulated log2 fold change; may contain NA.}
 #'   \item{q}{Numeric. Simulated adjusted p-value (q-value); may contain NA.}
 #' }
diff --git a/R/sample_dice_large.R b/R/sample_dice_large.R
new file mode 100644
index 0000000..134c4ac
--- /dev/null
+++ b/R/sample_dice_large.R
@@ -0,0 +1,23 @@
+#' Large Sample Dice Dataset
+#'
+#' @description
+#' `sample_dice_large` is a larger toy dataset with 60 taxa, designed to test
+#' `geom_dice()` at higher density. It simulates log2 fold-change and q-values
+#' with approximately 50\% missing data.
+#'
+#' @format A data frame with 480 rows and 6 columns:
+#' \describe{
+#'   \item{taxon}{Character. Taxon name (Taxon_1 through Taxon_60).}
+#'   \item{disease}{Character. Disease condition (Caries, Periodontitis, Healthy, Gingivitis).}
+#'   \item{specimen}{Character. Specimen type (Saliva, Plaque).}
+#'   \item{replicate}{Integer. Replicate identifier.}
+#'   \item{lfc}{Numeric. Simulated log2 fold change; may contain NA.}
+#'   \item{q}{Numeric. Simulated adjusted p-value; may contain NA.}
+#' }
+#'
+#' @usage data(sample_dice_large)
+#' @keywords datasets
+#' @examples
+#' data(sample_dice_large)
+#' head(sample_dice_large)
+"sample_dice_large"
diff --git a/R/sample_dice_miRNA.R b/R/sample_dice_miRNA.R
new file mode 100644
index 0000000..807dbea
--- /dev/null
+++ b/R/sample_dice_miRNA.R
@@ -0,0 +1,22 @@
+#' Sample miRNA Dice Dataset
+#'
+#' @description
+#' `sample_dice_miRNA` is a toy dataset for demonstrating `geom_dice()` with
+#' categorical fill mapping. It simulates miRNA dysregulation across compounds
+#' and organs, with direction (Up, Down, Unchanged) as the fill variable.
+#'
+#' @format A data frame with approximately 90 rows and 5 columns:
+#' \describe{
+#'   \item{miRNA}{Factor. miRNA identifier (miR-1 through miR-5).}
+#'   \item{Compound}{Factor. Treatment compound (Control, Compound_1 through Compound_4).}
+#'   \item{Organ}{Factor. Target organ (Lung, Liver, Brain, Kidney).}
+#'   \item{log2FC}{Numeric. Simulated log2 fold change.}
+#'   \item{direction}{Factor. Regulation direction (Down, Unchanged, Up).}
+#' }
+#'
+#' @usage data(sample_dice_miRNA)
+#' @keywords datasets
+#' @examples
+#' data(sample_dice_miRNA)
+#' head(sample_dice_miRNA)
+"sample_dice_miRNA"
diff --git a/README.md b/README.md
index 4e93664..16213c5 100644
--- a/README.md
+++ b/README.md
@@ -15,111 +15,70 @@ A ggplot2 extension for creating dice plot visualizations, where each dice repre
 ```r
 # Install from local directory
 devtools::install_local("path/to/ggdiceplot")
-
-# Or install dependencies manually
-install.packages(c("ggplot2", "tibble"))
 ```
 
 ```r
 install.packages(c("ggdiceplot"))
 ```
 
-## Example: Taxonomy 
+## Example: Taxonomy
 
 ```r
 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")
+data("sample_dice_data2", package = "ggdiceplot")
+toy_data <- sample_dice_data2
+
+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$q), 0.5, na.rm = TRUE))
+
+ggplot(toy_data, aes(x = specimen, y = taxon)) +
+  geom_dice(
+    aes(dots = disease, fill = lfc, size = -log10(q), width = 0.9, height = 0.9),
+    na.rm       = TRUE,
+    show.legend = TRUE,
+    pip_scale    = 0.9,
+    ndots       = length(unique(toy_data$disease)),
+    x_length    = length(unique(toy_data$specimen)),
+    y_length    = length(unique(toy_data$taxon))
+  ) +
+  scale_fill_gradient2(
+    low = "#40004B", high = "#00441B", mid = "white",
+    na.value = "white", limit = c(lo, up), midpoint = mid,
+    name = "Log2FC"
+  ) +
+  scale_size_continuous(
+    range  = c(2, 8),
+    limits = c(minsize, maxsize),
+    breaks = c(minsize, midsize, maxsize),
+    labels = c(10^minsize, 10^-midsize, 10^-maxsize),
+    name   = "q-value"
+  )
 ```
 
-![](demo_output/example1.png)
+![](demo_output/example2.png)
 
 ## Example: miRNA dysregulation
 
 ```r
-## ---- 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)
-}
+data("sample_dice_miRNA", package = "ggdiceplot")
+df_dice <- sample_dice_miRNA
 
-## ---- COLORS ----
-direction_colors <- c(
-  Down      = "#2166ac",
-  Unchanged = "grey80",
-  Up        = "#b2182b"
-)
+direction_colors <- c(Down = "#2166ac", Unchanged = "grey80", Up = "#b2182b")
 
-## ---- PLOT ----
-p_dice <- ggplot(df_dice, aes(x = miRNA, y = Compound)) +
+ggplot(df_dice, aes(x = miRNA, y = Compound)) +
   geom_dice(
-    aes(
-      dots  = Organ,
-      fill  = direction,
-      width = 0.8,
-      height = 0.8
-    ),
+    aes(dots = Organ, fill = direction, width = 0.8, height = 0.8),
     show.legend = TRUE,
+    pip_scale    = 1.0,
     ndots       = length(levels(df_dice$Organ)),
     x_length    = length(levels(df_dice$miRNA)),
     y_length    = length(levels(df_dice$Compound))
@@ -136,12 +95,11 @@ p_dice <- ggplot(df_dice, aes(x = miRNA, y = Compound)) +
     x = "miRNA",
     y = "Compound"
   )
-
-print(p_dice)
 ```
-![](demo_output/example2.png)
 
-## Example 3: ZEBRA Domino Plot
+![](demo_output/example_miRNA.png)
+
+## Example: ZEBRA Domino Plot
 
 This example demonstrates using `geom_dice()` to create a domino plot for gene expression analysis across multiple diseases and cell types.
 
@@ -151,82 +109,61 @@ 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 %>% 
+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)
+    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
+  summarise(logFC = mean(logFC, na.rm = TRUE), FDR = min(FDR, na.rm = TRUE), .groups = "drop") %>%
+  complete(gene, cell_type, contrast, fill = list(logFC = NA_real_, FDR = NA_real_))
 
+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$FDR), c(0.5), na.rm = TRUE))
+midsize <- ceiling(quantile(-log10(zebra.df$FDR), 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,
+    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$cell_type))
+    ndots       = 5,
+    x_length    = length(genes),
+    y_length    = length(unique(zebra.df$cell_type))
   ) +
   scale_fill_gradient2(
-    low = "#40004B",
-    high = "#00441B",
-    mid = "white",
-    na.value = "white",
-    limit = c(lo, up),
-    midpoint = mid,
+    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"
+    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(),
+    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"
-  )
+  labs(x = "Gene", y = "Cell Type", title = "ZEBRA Sex DEGs Domino Plot")
+
+ggsave("ZEBRA_domino_example.png", width = 12, height = 14, dpi = 300)
 ```
 
 ![](demo_output/ZEBRA_domino_example.png)
@@ -234,40 +171,35 @@ ggplot(zebra.df, aes(x = gene, y = cell_type)) +
 ## Features
 
 - **1:1 Aspect Ratio**: Dice automatically appear as perfect squares using `coord_fixed(ratio = 1)`
-- **Automatic Sizing**: Dice automatically scale based on grid density
-- **Boundary Safety**: Dice stay within plot boundaries
-- **Flexible Mapping**: Map any categories to dice positions 1-6
+- **Automatic Pip Scaling**: `pip_scale` (0–1) controls pip diameter as a fraction of the maximum available space. `pip_scale = 1.0` fills the die face fully; set to `NULL` to use a fixed size
+- **Boundary Safety**: Pips never exceed tile borders; positions shift inward as density increases
+- **Flexible Mapping**: Map any categories to dice positions 1–6
 - **Multiple Applications**: Gene expression, survey data, clinical trials, market research
-- **Customizable**: Control dice size, colors, and positioning
+- **Customizable**: Control dice size, colors, pip density, and positioning
 
-## Important Update
+## Key Parameters
 
-**Automatic Aspect Ratio Control**: As of the latest version, `geom_dice()` automatically applies `coord_fixed(ratio = 1)` to ensure dice appear as perfect squares. The `x_length` and `y_length` parameters are maintained for compatibility but no longer affect aspect ratio.
+| Parameter | Description |
+|-----------|-------------|
+| `dots` | Aesthetic mapping — which category occupies which pip position |
+| `pip_scale` | Pip diameter as fraction of max space (default `0.75`; `1.0` = tight fill; `NULL` = fixed size) |
+| `ndots` | Number of pip positions on each die face (1–6) |
+| `x_length`, `y_length` | Grid dimensions (used for aspect ratio) |
+| `na.rm` | Drop observations with missing size/fill values |
 
 ## Key Functions
 
 - `geom_dice()`: Main geom for creating dice plots with automatic 1:1 aspect ratio
 - `theme_dice()`: Minimal theme optimized for dice plots
-- `create_dice_positions()`: Generate standard dice dot positions
-- `make_offsets()`: Calculate dice dot positions for rendering
+- `create_dice_positions()`: Generate standard dice dot position layouts
+- `make_offsets()`: Calculate pip positions for rendering
 
-## Demo Examples
+## Running the Examples
 
-See the `demo_output/` directory for:
-- Basic functionality examples
-- Real-world usage scenarios (ZEBRA gene expression analysis)
-- Boundary validation tests
-- Custom sizing demonstrations
-
-Run the demo scripts:
 ```bash
-cd demo_output
-Rscript create_demo_plots.R
-Rscript usage_examples.R
-
-# Run the ZEBRA domino example
-cd examples
-Rscript zebra_domino_example.R
+# From the project root
+Rscript demo_output/create_demo_plots.R
+Rscript test_simple_dice.R
 ```
 
 ## Package Structure
@@ -275,31 +207,27 @@ Rscript zebra_domino_example.R
 - `R/`: Core package functions
 - `data/`: Sample datasets
 - `demo_output/`: Example plots and output images
-- `examples/`: Real-world usage examples (ZEBRA domino plot)
+- `examples/`: Real-world usage examples
 - `legacy examples/`: Legacy examples and data files
 - `man/`: Documentation files
-- `inst/`: Package installation files
-
-## Documentation
-
-- Package functions: `help(package = "ggdiceplot")`
-- Main function: `?geom_dice`
-- Vignette: `vignette("introduction", package = "ggdiceplot")`
 
 ## Citation
 
 If you use this code or the R and Python packages for your own work, please cite diceplot as:
-  
-> M. Flotho, P. Flotho, A. Keller, "Diceplot: A package for high dimensional categorical data visualization," arxiv, 2024. [doi:10.48550/arXiv.2410.23897](https://doi.org/10.48550/arXiv.2410.23897)
+
+> M. Flotho, P. Flotho, A. Keller, "DicePlot: a package for high-dimensional categorical data visualization," *Bioinformatics*, vol. 42, no. 2, btaf337, 2026.
 
 BibTeX entry:
-```
-@article{flotea2024,
-    author = {Flotho, M. and Flotho, P. and Keller, A.},
-    title = {Diceplot: A package for high dimensional categorical data visualization},
-    year = {2024},
-    journal = {arXiv preprint},
-    doi = {https://doi.org/10.48550/arXiv.2410.23897}
+```bibtex
+@article{flotho2026diceplot,
+    title     = {DicePlot: a package for high-dimensional categorical data visualization},
+    author    = {Flotho, Matthias and Flotho, Philipp and Keller, Andreas},
+    journal   = {Bioinformatics},
+    volume    = {42},
+    number    = {2},
+    pages     = {btaf337},
+    year      = {2026},
+    publisher = {Oxford University Press}
 }
 ```
 
diff --git a/data/sample_dice_data1.R b/data-raw/sample_dice_data1.R
similarity index 100%
rename from data/sample_dice_data1.R
rename to data-raw/sample_dice_data1.R
diff --git a/data/sample_dice_data2.R b/data-raw/sample_dice_data2.R
similarity index 100%
rename from data/sample_dice_data2.R
rename to data-raw/sample_dice_data2.R
diff --git a/data/sample_dice_large.R b/data-raw/sample_dice_large.R
similarity index 100%
rename from data/sample_dice_large.R
rename to data-raw/sample_dice_large.R
diff --git a/data/sample_dice_miRNA.R b/data-raw/sample_dice_miRNA.R
similarity index 100%
rename from data/sample_dice_miRNA.R
rename to data-raw/sample_dice_miRNA.R
diff --git a/data/sample_dice_data1.rda b/data/sample_dice_data1.rda
index 18bf1d7..a22e0d1 100644
Binary files a/data/sample_dice_data1.rda and b/data/sample_dice_data1.rda differ
diff --git a/data/sample_dice_data2.rda b/data/sample_dice_data2.rda
index 6c1d7b9..894cd6f 100644
Binary files a/data/sample_dice_data2.rda and b/data/sample_dice_data2.rda differ
diff --git a/demo_output/ZEBRA_domino_example.pdf b/demo_output/ZEBRA_domino_example.pdf
deleted file mode 100644
index 93d414d..0000000
Binary files a/demo_output/ZEBRA_domino_example.pdf and /dev/null differ
diff --git a/demo_output/ZEBRA_domino_example.png b/demo_output/ZEBRA_domino_example.png
index a17db37..1454132 100644
Binary files a/demo_output/ZEBRA_domino_example.png and b/demo_output/ZEBRA_domino_example.png differ
diff --git a/demo_output/ZEBRA_domino_example_custom_labels.png b/demo_output/ZEBRA_domino_example_custom_labels.png
deleted file mode 100644
index d3bec81..0000000
Binary files a/demo_output/ZEBRA_domino_example_custom_labels.png and /dev/null differ
diff --git a/demo_output/create_demo_plots.R b/demo_output/create_demo_plots.R
index e79565e..1dcfb39 100644
--- a/demo_output/create_demo_plots.R
+++ b/demo_output/create_demo_plots.R
@@ -1,162 +1,159 @@
 #!/usr/bin/env Rscript
 # Demo script for ggdiceplot package
-# This script creates demonstration plots showcasing the package capabilities
+# Run from the project root: Rscript demo_output/create_demo_plots.R
 
 library(ggplot2)
-
-# Load the ggdiceplot functions
-library(ggdiceplot)
-
-# Already in demo_output directory
-cat("Working from demo_output directory...\n")
-
-cat("Creating ggdiceplot demonstration plots...\n")
-
-# Demo 1: Basic dice plot functionality
-cat("1. Creating example1 plot demo...\n")
-
-# Load dataset sample_dice_data1 from package data folder and assign to toy_data1
-load("../data/sample_dice_data1.rda")  # loads object sample_dice_data1 in environment
+library(legendry)
+library(scales)
+library(grid)
+
+source("R/utils.R")
+source("R/geom-dice-ggprotto.R")
+source("R/geom-dice.R")
+
+make_dice_plot <- function(toy_data) {
+  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$q), 0.5, na.rm = TRUE))
+
+  ggplot(toy_data, aes(x = specimen, y = taxon)) +
+    geom_dice(
+      aes(dots = disease, fill = lfc, size = -log10(q), width = 0.9, height = 0.9),
+      na.rm       = TRUE,
+      show.legend = TRUE,
+      pip_scale    = 0.9,
+      ndots       = length(unique(toy_data$disease)),
+      x_length    = length(unique(toy_data$specimen)),
+      y_length    = length(unique(toy_data$taxon))
+    ) +
+    scale_fill_gradient2(
+      low      = "#40004B", high = "#00441B", mid = "white",
+      na.value = "white", limit = c(lo, up), midpoint = mid,
+      name     = "Log2FC"
+    ) +
+    scale_size_continuous(
+      range  = c(2, 8),
+      limits = c(minsize, maxsize),
+      breaks = c(minsize, midsize, maxsize),
+      labels = c(10^minsize, 10^-midsize, 10^-maxsize),
+      name   = "q-value"
+    )
+}
+
+# ---- Example 1 --------------------------------------------------------------
+cat("1. Creating example1...\n")
+load("data/sample_dice_data1.rda")
+ggsave("demo_output/example1.png", make_dice_plot(sample_dice_data1),
+       width = 12, height = 12, dpi = 300)
+
+# ---- Example 2 --------------------------------------------------------------
+cat("2. Creating example2...\n")
+load("data/sample_dice_data2.rda")
+ggsave("demo_output/example2.png", make_dice_plot(sample_dice_data2),
+       width = 10, height = 10, dpi = 300)
+
+# ---- Example large ----------------------------------------------------------
+cat("3. Creating example_large...\n")
+load("data/sample_dice_large.rda")
+ggsave("demo_output/example_large.png", make_dice_plot(sample_dice_large),
+       width = 10, height = 30, dpi = 300)
+
+# ---- Example 4: fill-only, pip_scale = 1.0 -----------------------------------
+# No size mapping → auto-scaling active; pip_scale = 1.0 fills die face fully.
+cat("4. Creating example4_fill_only...\n")
+load("data/sample_dice_data1.rda")
 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$q), c(0.5), na.rm=TRUE))
-
-#
-## PLOT
-#
-
-pex1 <- ggplot(toy_data, aes(x=specimen, y=taxon)) +
-  geom_dice(aes(dots=disease, fill=lfc, size=-log10(q), 
-                # Square dims
-                width = 0.5, height = 0.5),
-            # For missing info
-            na.rm = TRUE,
-            # For legend display
-            show.legend=TRUE, 
-            # For legend position calculation
-            ndots=length(unique(toy_data$disease)),
-            # For aspect.ratio: ensure squares
-            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")
-
-ggsave("example1.png", pex1, width = 8, height = 8, dpi = 300)
-
-
-# Demo 2: Dense grid showcase
-cat("2. Creating example2 plot demo...\n")
-# Load dataset sample_dice_data2 from package data folder and assign to toy_data2
-load("../data/sample_dice_data2.rda")
-toy_data <- sample_dice_data2
-
-# 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$q), c(0.5), na.rm=TRUE))
-
-#
-## PLOT
-#
-
-pex2 <- ggplot(toy_data, aes(x=specimen, y=taxon)) +
-  geom_dice(aes(dots=disease, fill=lfc, size=-log10(q), 
-                # Square dims
-                width = 0.5, height = 0.5),
-            # For missing info
-            na.rm = TRUE,
-            # For legend display
-            show.legend=TRUE, 
-            # For legend position calculation
-            ndots=length(unique(toy_data$disease)),
-            # For aspect.ratio: ensure squares
-            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")
-
-ggsave("example2.png", pex1, width = 10, height = 6, dpi = 300)
-
-
-# Demo 3: Large Y axis case
-cat("2. Creating largecase plot demo...\n")
-# Load dataset sample_dice_data2 from package data folder and assign to toy_data2
-load("../data/sample_dice_large.rda")
-toy_data <- sample_dice_large
-
-# 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$q), c(0.5), na.rm=TRUE))
-
-#
-## PLOT
-#
-
-pex3 <- ggplot(toy_data, aes(x=specimen, y=taxon)) +
-  geom_dice(aes(dots=disease, fill=lfc, size=-log10(q), 
-                # Square dims
-                width = 0.5, height = 0.5),
-            # For missing info
-            na.rm = TRUE,
-            # For legend display
-            show.legend=TRUE, 
-            # For legend position calculation
-            ndots=length(unique(toy_data$disease)),
-            # For aspect.ratio: ensure squares
-            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")
-
-ggsave("example_large.png", pex3, width = 10, height = 6, dpi = 300)
-
-
+lo  <- floor(min(toy_data$lfc, na.rm = TRUE))
+up  <- ceiling(max(toy_data$lfc, na.rm = TRUE))
+mid <- (lo + up) / 2
+
+pex4 <- ggplot(toy_data, aes(x = specimen, y = taxon)) +
+  geom_dice(
+    aes(dots = disease, fill = lfc, width = 0.9, height = 0.9),
+    na.rm       = TRUE,
+    show.legend = TRUE,
+    pip_scale    = 1.0,
+    ndots       = length(unique(toy_data$disease)),
+    x_length    = length(unique(toy_data$specimen)),
+    y_length    = length(unique(toy_data$taxon))
+  ) +
+  scale_fill_gradient2(
+    low = "#40004B", high = "#00441B", mid = "white",
+    na.value = "white", limit = c(lo, up), midpoint = mid,
+    name = "Log2FC"
+  ) +
+  labs(
+    title    = "Fill-only dice plot  (pip_scale = 1.0)",
+    subtitle = "Pips fill the die face at maximal density"
+  )
+
+ggsave("demo_output/example4_fill_only.png", pex4, width = 10, height = 10, dpi = 300)
+
+# ---- ZEBRA domino example ---------------------------------------------------
+cat("5. Creating ZEBRA_domino_example...\n")
+library(dplyr)
+library(tidyr)
+
+zebra.df <- read.csv("legacy examples/data/ZEBRA_sex_degs_set.csv")
+genes <- c("SPP1", "APOE", "SERPINA1", "PINK1", "ANGPT1", "ANGPT2", "APP", "CLU", "ABCA7")
+
+zebra.df <- zebra.df %>%
+  filter(gene %in% genes) %>%
+  filter(contrast %in% c("MS-CT", "AD-CT", "ASD-CT", "FTD-CT", "HD-CT")) %>%
+  mutate(
+    cell_type = factor(cell_type, levels = sort(unique(cell_type))),
+    contrast  = factor(contrast, levels = c("MS-CT", "AD-CT", "ASD-CT", "FTD-CT", "HD-CT")),
+    gene      = factor(gene, levels = genes)
+  ) %>%
+  filter(PValue < 0.05) %>%
+  group_by(gene, cell_type, contrast) %>%
+  summarise(logFC = mean(logFC, na.rm = TRUE), FDR = min(FDR, na.rm = TRUE), .groups = "drop") %>%
+  complete(gene, cell_type, contrast, fill = list(logFC = NA_real_, FDR = NA_real_))
+
+lo      <- floor(min(zebra.df$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), 0.5, na.rm = TRUE))
+
+pzebra <- 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,
+    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")
+
+ggsave("demo_output/ZEBRA_domino_example.png", pzebra,
+       width = 12, height = 14, dpi = 300)
 
 cat("All demonstration plots created successfully!\n")
 cat("Generated files in demo_output/:\n")
-cat("- example1.png\n")
-cat("- example2.png\n")
-cat("- example_large.png\n")
\ No newline at end of file
+cat("- example1.png\n- example2.png\n- example_large.png\n- example4_fill_only.png\n- ZEBRA_domino_example.png\n")
diff --git a/demo_output/example1.png b/demo_output/example1.png
index 6939851..da9ca18 100644
Binary files a/demo_output/example1.png and b/demo_output/example1.png differ
diff --git a/demo_output/example2.png b/demo_output/example2.png
index 5a97e74..d3d45cf 100644
Binary files a/demo_output/example2.png and b/demo_output/example2.png differ
diff --git a/demo_output/example4_fill_only.png b/demo_output/example4_fill_only.png
new file mode 100644
index 0000000..e579c44
Binary files /dev/null and b/demo_output/example4_fill_only.png differ
diff --git a/demo_output/example_large.png b/demo_output/example_large.png
index 87ed25a..e28a190 100644
Binary files a/demo_output/example_large.png and b/demo_output/example_large.png differ
diff --git a/demo_output/example_miRNA.png b/demo_output/example_miRNA.png
new file mode 100644
index 0000000..375a313
Binary files /dev/null and b/demo_output/example_miRNA.png differ
diff --git a/demo_output/ndots_comparison.png b/demo_output/ndots_comparison.png
new file mode 100644
index 0000000..add36b0
Binary files /dev/null and b/demo_output/ndots_comparison.png differ
diff --git a/demo_output/pip_scale_comparison.png b/demo_output/pip_scale_comparison.png
new file mode 100644
index 0000000..932799a
Binary files /dev/null and b/demo_output/pip_scale_comparison.png differ
diff --git a/demo_output/size_mapped_example.png b/demo_output/size_mapped_example.png
new file mode 100644
index 0000000..a03312f
Binary files /dev/null and b/demo_output/size_mapped_example.png differ
diff --git a/demo_output/tile_size_comparison.png b/demo_output/tile_size_comparison.png
new file mode 100644
index 0000000..a829267
Binary files /dev/null and b/demo_output/tile_size_comparison.png differ
diff --git a/examples/pnas_style_example.R b/examples/pnas_style_example.R
index 5ecc615..3a5c82b 100644
--- a/examples/pnas_style_example.R
+++ b/examples/pnas_style_example.R
@@ -226,5 +226,112 @@ print(cell_summary)
 print("PNAS-style dice plots created successfully!")
 print("Generated files:")
 print("- pnas_style_example.png")
-print("- pnas_style_example.pdf") 
-print("- pnas_inflammatory_subset.png")
\ No newline at end of file
+print("- pnas_style_example.pdf")
+print("- pnas_inflammatory_subset.png")
+
+# ---------------------------------------------------------------------------
+# pip_scale demonstration: fill-only version (no size mapping)
+# ---------------------------------------------------------------------------
+# When size is not mapped to a variable, geom_dice auto-scales each pip to
+# fill pip_scale * (available space per pip) at render time.
+# Use pip_scale = NULL to revert to the fixed size = 3 mm legacy behaviour.
+
+# Aggregate to one row per gene/cell_type (collapse demographics)
+gene_data_agg <- gene_data %>%
+  group_by(gene, cell_type) %>%
+  summarise(
+    expression_level = mean(expression_level, na.rm = TRUE),
+    demographic      = first(demographic),
+    .groups = "drop"
+  ) %>%
+  mutate(
+    gene      = factor(gene, levels = top_genes),
+    cell_type = factor(cell_type, levels = unname(cell_type_names))
+  )
+
+# -- Auto-scaled (default pip_scale = 0.75) -----------------------------------
+p_auto <- ggplot(gene_data_agg, aes(x = gene, y = cell_type)) +
+  geom_dice(
+    aes(dots = cell_type, fill = expression_level, width = 0.85, height = 0.85),
+    na.rm       = TRUE,
+    show.legend = TRUE,
+    ndots       = length(unique(gene_data_agg$cell_type)),
+    x_length    = length(top_genes),
+    y_length    = length(cell_types)
+    # pip_scale defaults to 0.75
+  ) +
+  scale_dots_discrete(guide = "none") +
+  scale_fill_gradient2(
+    low = "#2166AC", high = "#762A83", mid = "white",
+    na.value = "grey90", limit = c(lo, up), midpoint = mid,
+    name = "Expression\nLevel"
+  ) +
+  theme_minimal() +
+  theme(
+    axis.text.x   = element_text(angle = 45, hjust = 1, size = 9),
+    axis.text.y   = element_text(size = 10),
+    legend.text   = element_text(size = 9),
+    legend.title  = element_text(size = 10),
+    panel.grid    = element_blank()
+  ) +
+  labs(
+    x        = "Gene",
+    y        = "Cell Type",
+    title    = "pip_scale = 0.75  (auto-scaled, default)",
+    subtitle = "No size mapping — pip diameter adapts to figure size"
+  )
+
+# -- Fixed legacy size (pip_scale = NULL) -------------------------------------
+p_fixed <- p_auto +
+  geom_dice(
+    aes(dots = cell_type, fill = expression_level, width = 0.85, height = 0.85),
+    na.rm       = TRUE,
+    show.legend = FALSE,
+    ndots       = length(unique(gene_data_agg$cell_type)),
+    x_length    = length(top_genes),
+    y_length    = length(cell_types),
+    pip_scale    = NULL   # disables auto-scaling; falls back to size = 3 mm
+  ) +
+  labs(
+    title    = "pip_scale = NULL  (fixed size = 3 mm)",
+    subtitle = "Legacy behaviour — may leave excess whitespace at small figure sizes"
+  )
+
+# Actually rebuild separately so they render independently
+p_fixed <- ggplot(gene_data_agg, aes(x = gene, y = cell_type)) +
+  geom_dice(
+    aes(dots = cell_type, fill = expression_level, width = 0.85, height = 0.85),
+    na.rm       = TRUE,
+    show.legend = TRUE,
+    ndots       = length(unique(gene_data_agg$cell_type)),
+    x_length    = length(top_genes),
+    y_length    = length(cell_types),
+    pip_scale    = NULL
+  ) +
+  scale_dots_discrete(guide = "none") +
+  scale_fill_gradient2(
+    low = "#2166AC", high = "#762A83", mid = "white",
+    na.value = "grey90", limit = c(lo, up), midpoint = mid,
+    name = "Expression\nLevel"
+  ) +
+  theme_minimal() +
+  theme(
+    axis.text.x   = element_text(angle = 45, hjust = 1, size = 9),
+    axis.text.y   = element_text(size = 10),
+    legend.text   = element_text(size = 9),
+    legend.title  = element_text(size = 10),
+    panel.grid    = element_blank()
+  ) +
+  labs(
+    x        = "Gene",
+    y        = "Cell Type",
+    title    = "pip_scale = NULL  (fixed size = 3 mm)",
+    subtitle = "Legacy behaviour — may leave excess whitespace at small figure sizes"
+  )
+
+ggsave("pnas_pip_scale_auto.png",  p_auto,  width = 14, height = 5, dpi = 300)
+ggsave("pnas_pip_scale_fixed.png", p_fixed, width = 14, height = 5, dpi = 300)
+
+print("pip_scale comparison plots created:")
+print("- pnas_pip_scale_auto.png   (pip_scale = 0.75, auto-scaled)")
+print("- pnas_pip_scale_fixed.png  (pip_scale = NULL, fixed size = 3 mm)")
\ No newline at end of file
diff --git a/man/geom_dice.Rd b/man/geom_dice.Rd
index 23639ee..feebec8 100644
--- a/man/geom_dice.Rd
+++ b/man/geom_dice.Rd
@@ -14,6 +14,7 @@ geom_dice(
   ndots = NULL,
   x_length = NULL,
   y_length = NULL,
+  pip_scale = 0.75,
   na.rm = FALSE,
   show.legend = TRUE,
   inherit.aes = TRUE,
@@ -36,9 +37,16 @@ Must include:
 
 \item{ndots}{Integer (1–6): number of positions shown per dice.}
 
-\item{x_length, }{x_length Numeric: used for aspect ratio.}
+\item{x_length}{Numeric: number of x categories (used for aspect ratio).}
 
-\item{y_length, }{y_length Numeric: used for aspect ratio.}
+\item{y_length}{Numeric: number of y categories (used for aspect ratio).}
+
+\item{pip_scale}{Numeric (0–1): controls pip diameter relative to the
+maximum available space. When \code{size} is constant (not mapped), all pips
+are drawn at \code{pip_scale} fraction of the die face. When \code{size} is mapped
+to a variable, pips scale between 0.25 (smallest value) and \code{pip_scale}
+(largest value) of the maximum pip diameter. Default is \code{0.75}. Set to
+\code{NULL} to disable auto-scaling and use the raw \code{size} aesthetic.}
 
 \item{na.rm}{Remove missing values if \code{TRUE}.}
 
diff --git a/man/sample_dice_data1.Rd b/man/sample_dice_data1.Rd
index 96a1b70..7967f2d 100644
--- a/man/sample_dice_data1.Rd
+++ b/man/sample_dice_data1.Rd
@@ -5,13 +5,13 @@
 \alias{sample_dice_data1}
 \title{Sample Dice Dataset for Visualization}
 \format{
-A data frame with 48 rows and 5 columns:
+A data frame with 160 rows and 5 columns:
 \describe{
-\item{taxon}{Character. Microbial taxon name.}
-\item{disease}{Character. Disease condition (e.g., Caries, Periodontitis, etc.).}
-\item{specimen}{Character. Body site specimen (e.g., Saliva, Plaque).}
-\item{lfc}{Numeric. Simulated log2 fold change value.}
-\item{q}{Numeric. Simulated adjusted p-value (q-value).}
+\item{taxon}{Character. Microbial taxon name (8 taxa).}
+\item{disease}{Character. Disease condition (Caries, Periodontitis, Healthy, Gingivitis).}
+\item{specimen}{Character. Body site specimen (Saliva, Plaque, Tongue, Buccal, Gingival).}
+\item{lfc}{Numeric. Simulated log2 fold change value; may contain NA.}
+\item{q}{Numeric. Simulated adjusted p-value (q-value); may contain NA.}
 }
 }
 \usage{
diff --git a/man/sample_dice_data2.Rd b/man/sample_dice_data2.Rd
index 815616a..f918ab6 100644
--- a/man/sample_dice_data2.Rd
+++ b/man/sample_dice_data2.Rd
@@ -3,14 +3,13 @@
 \docType{data}
 \name{sample_dice_data2}
 \alias{sample_dice_data2}
-\title{Sample Dice Dataset 2 for Visualization with Replicates}
+\title{Sample Dice Dataset 2 for Visualization}
 \format{
-A data frame with 48 rows and 6 columns:
+A data frame with 160 rows and 5 columns:
 \describe{
-\item{taxon}{Character. Microbial taxon name.}
-\item{disease}{Character. Disease condition (e.g., Caries, Periodontitis).}
-\item{specimen}{Character. Body site specimen (e.g., Saliva, Plaque).}
-\item{replicate}{Integer. Experimental replicate ID. Only replicate 1 is included here.}
+\item{taxon}{Character. Microbial taxon name (8 taxa).}
+\item{disease}{Character. Disease condition (Caries, Periodontitis, Healthy, Gingivitis).}
+\item{specimen}{Character. Body site specimen (Saliva, Plaque, Tongue, Buccal, Gingival).}
 \item{lfc}{Numeric. Simulated log2 fold change; may contain NA.}
 \item{q}{Numeric. Simulated adjusted p-value (q-value); may contain NA.}
 }
@@ -19,14 +18,10 @@ A data frame with 48 rows and 6 columns:
 data(sample_dice_data2)
 }
 \description{
-\code{sample_dice_data2} is a toy dataset designed to demonstrate the usage of \code{geom_dice()}
-in more advanced scenarios. It simulates log2 fold-change (LFC) and adjusted p-values
-(q-values) for common oral taxa across disease conditions and specimen types,
-including replicates and missing values.
-
-This version filters for \code{replicate == 1} but retains structure from a more complex
-design, making it useful for testing visualizations that account for filtering,
-grouping, or handling of \code{NA} values.
+\code{sample_dice_data2} is a toy dataset designed to demonstrate \code{geom_dice()}
+with multiple specimen types. It simulates log2 fold-change (LFC) and
+adjusted p-values (q-values) for oral taxa across disease conditions and
+specimen sites, with some missing values.
 }
 \examples{
 data(sample_dice_data2)
diff --git a/man/sample_dice_large.Rd b/man/sample_dice_large.Rd
new file mode 100644
index 0000000..016f67a
--- /dev/null
+++ b/man/sample_dice_large.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sample_dice_large.R
+\docType{data}
+\name{sample_dice_large}
+\alias{sample_dice_large}
+\title{Large Sample Dice Dataset}
+\format{
+A data frame with 480 rows and 6 columns:
+\describe{
+\item{taxon}{Character. Taxon name (Taxon_1 through Taxon_60).}
+\item{disease}{Character. Disease condition (Caries, Periodontitis, Healthy, Gingivitis).}
+\item{specimen}{Character. Specimen type (Saliva, Plaque).}
+\item{replicate}{Integer. Replicate identifier.}
+\item{lfc}{Numeric. Simulated log2 fold change; may contain NA.}
+\item{q}{Numeric. Simulated adjusted p-value; may contain NA.}
+}
+}
+\usage{
+data(sample_dice_large)
+}
+\description{
+\code{sample_dice_large} is a larger toy dataset with 60 taxa, designed to test
+\code{geom_dice()} at higher density. It simulates log2 fold-change and q-values
+with approximately 50\\% missing data.
+}
+\examples{
+data(sample_dice_large)
+head(sample_dice_large)
+}
+\keyword{datasets}
diff --git a/man/sample_dice_miRNA.Rd b/man/sample_dice_miRNA.Rd
new file mode 100644
index 0000000..d4a93c5
--- /dev/null
+++ b/man/sample_dice_miRNA.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sample_dice_miRNA.R
+\docType{data}
+\name{sample_dice_miRNA}
+\alias{sample_dice_miRNA}
+\title{Sample miRNA Dice Dataset}
+\format{
+A data frame with approximately 90 rows and 5 columns:
+\describe{
+\item{miRNA}{Factor. miRNA identifier (miR-1 through miR-5).}
+\item{Compound}{Factor. Treatment compound (Control, Compound_1 through Compound_4).}
+\item{Organ}{Factor. Target organ (Lung, Liver, Brain, Kidney).}
+\item{log2FC}{Numeric. Simulated log2 fold change.}
+\item{direction}{Factor. Regulation direction (Down, Unchanged, Up).}
+}
+}
+\usage{
+data(sample_dice_miRNA)
+}
+\description{
+\code{sample_dice_miRNA} is a toy dataset for demonstrating \code{geom_dice()} with
+categorical fill mapping. It simulates miRNA dysregulation across compounds
+and organs, with direction (Up, Down, Unchanged) as the fill variable.
+}
+\examples{
+data(sample_dice_miRNA)
+head(sample_dice_miRNA)
+}
+\keyword{datasets}
diff --git a/pixi.lock b/pixi.lock
new file mode 100644
index 0000000..d94a394
--- /dev/null
+++ b/pixi.lock
@@ -0,0 +1,2926 @@
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diff --git a/pixi.toml b/pixi.toml
new file mode 100644
index 0000000..855fe53
--- /dev/null
+++ b/pixi.toml
@@ -0,0 +1,21 @@
+[workspace]
+name = "ggdiceplot"
+version = "1.0.0"
+description = "DicePlot Visualization for ggplot2"
+channels = ["conda-forge"]
+platforms = ["osx-arm64"]
+
+[dependencies]
+r-base = ">=4.5"
+r-ggplot2 = ">=3.4.0"
+r-dplyr = "*"
+r-tibble = "*"
+r-scales = "*"
+r-legendry = "*"
+r-patchwork = "*"
+r-tidyr = "*"
+r-devtools = "*"
+r-rcmdcheck = "*"
+
+[tasks]
+demo = "Rscript test_simple_dice.R"
diff --git a/test_simple_dice.R b/test_simple_dice.R
index ebae758..ea6900c 100644
--- a/test_simple_dice.R
+++ b/test_simple_dice.R
@@ -1,40 +1,168 @@
 #!/usr/bin/env Rscript
-# Simple test of geom_dice
+# Demonstration of pip_scale auto-scaling in geom_dice
 
 library(ggplot2)
 library(dplyr)
 library(legendry)
 library(scales)
 library(grid)
+library(patchwork)
 
-# Load ggdiceplot functions
 source("R/utils.R")
 source("R/geom-dice-ggprotto.R")
 source("R/geom-dice.R")
 
-# Create simple test data
+# ---- shared toy data -------------------------------------------------------
+
+set.seed(42)
 test_data <- data.frame(
-  x = rep(1:3, each = 4),
-  y = rep(1:2, times = 6),
-  category = rep(c("A", "B", "C", "D"), times = 3),
-  value = runif(12, -2, 2),
-  size_val = runif(12, 0.1, 1)
+  x        = rep(1:4, each = 4),
+  y        = rep(1:2, times = 8),
+  category = rep(c("A", "B", "C", "D"), times = 4),
+  value    = runif(16, -2, 2)
 )
 
-print("Test data:")
-print(test_data)
+# ---- Plot 1: pip_scale comparison (0.5 / 0.75 / 0.9 / 1.0 / NULL) ----------
+
+make_base_plot <- function(pip_scale_val, title_label) {
+  ggplot(test_data, aes(x = x, y = y)) +
+    geom_dice(
+      aes(dots = category, fill = value),
+      ndots     = 4,
+      x_length  = 4,
+      y_length  = 2,
+      pip_scale  = pip_scale_val
+    ) +
+    scale_fill_gradient2(
+      low = "#2166AC", high = "#762A83", mid = "white",
+      midpoint = 0, name = "value"
+    ) +
+    labs(title = title_label) +
+    theme(plot.title = element_text(size = 10))
+}
+
+p_050 <- make_base_plot(0.50, "pip_scale = 0.50")
+p_075 <- make_base_plot(0.75, "pip_scale = 0.75  (default)")
+p_090 <- make_base_plot(0.90, "pip_scale = 0.90")
+p_100 <- make_base_plot(1.00, "pip_scale = 1.00  (tight)")
+p_off <- make_base_plot(NULL, "pip_scale = NULL  (fixed 3 mm)")
+
+comparison <- (p_050 + p_075 + p_090) / (p_100 + p_off + plot_spacer()) +
+  plot_annotation(
+    title    = "pip_scale comparison",
+    subtitle = "pip_scale scales both pip size and positions; NULL = legacy fixed size"
+  )
+
+ggsave("demo_output/pip_scale_comparison.png", comparison, width = 14, height = 9, dpi = 300)
+
+# ---- Plot 2: tile size vs pip_scale = 0.75 and 1.0 -------------------------
+
+make_tile_size_plot <- function(tile_w, title_label, pip_scale_val = 0.75) {
+  ggplot(test_data, aes(x = x, y = y)) +
+    geom_dice(
+      aes(dots = category, fill = value, width = tile_w, height = tile_w),
+      ndots    = 4,
+      x_length = 4,
+      y_length = 2,
+      pip_scale = pip_scale_val
+    ) +
+    scale_fill_gradient2(
+      low = "#2166AC", high = "#762A83", mid = "white",
+      midpoint = 0, name = "value"
+    ) +
+    labs(title = title_label) +
+    theme(plot.title = element_text(size = 9))
+}
+
+# row 1: pip_scale = 0.75
+p_75_04  <- make_tile_size_plot(0.40, "pf=0.75, tile=0.40", 0.75)
+p_75_07  <- make_tile_size_plot(0.70, "pf=0.75, tile=0.70", 0.75)
+p_75_095 <- make_tile_size_plot(0.95, "pf=0.75, tile=0.95", 0.75)
+
+# row 2: pip_scale = 1.0
+p_10_04  <- make_tile_size_plot(0.40, "pf=1.00, tile=0.40", 1.00)
+p_10_07  <- make_tile_size_plot(0.70, "pf=1.00, tile=0.70", 1.00)
+p_10_095 <- make_tile_size_plot(0.95, "pf=1.00, tile=0.95", 1.00)
+
+tile_comparison <- (p_75_04 + p_75_07 + p_75_095) /
+                   (p_10_04 + p_10_07 + p_10_095) +
+  plot_annotation(
+    title    = "Auto-scaling adapts to tile width",
+    subtitle = "Row 1: pip_scale=0.75  |  Row 2: pip_scale=1.00 (tight packing)"
+  )
 
-# Create simple plot
-p <- ggplot(test_data, aes(x = x, y = y)) +
+ggsave("demo_output/tile_size_comparison.png", tile_comparison, width = 14, height = 9, dpi = 300)
+
+# ---- Plot 3: ndots 2 to 6 at pip_scale = 0.75 and 1.0 ---------------------
+
+make_single_tile <- function(n, pip_scale_val) {
+  d <- data.frame(
+    x        = rep(1, n),
+    y        = rep(1, n),
+    category = LETTERS[1:n],
+    value    = rep(c(-1, 1, -0.6, 0.6, -0.8, 0.8), length.out = n)  # no zeros
+  )
+  ggplot(d, aes(x = x, y = y)) +
+    geom_dice(
+      aes(dots = category, fill = value),
+      ndots    = n,
+      x_length = 1,
+      y_length = 1,
+      pip_scale = pip_scale_val
+    ) +
+    scale_fill_gradient2(
+      low = "#2166AC", high = "#762A83", mid = "white",
+      midpoint = 0, guide = "none"
+    ) +
+    labs(title = paste0("n=", n)) +
+    theme_minimal() +
+    theme(
+      axis.text    = element_blank(),
+      axis.title   = element_blank(),
+      axis.ticks   = element_blank(),
+      panel.grid   = element_blank(),
+      plot.title   = element_text(size = 10, hjust = 0.5)
+    )
+}
+
+row_75 <- lapply(2:6, make_single_tile, pip_scale_val = 0.75)
+row_10 <- lapply(2:6, make_single_tile, pip_scale_val = 1.00)
+
+ndots_comparison <- wrap_plots(c(row_75, row_10), nrow = 2) +
+  plot_annotation(
+    title    = "ndots 2–6: pip positioning and sizing",
+    subtitle = "Top row: pip_scale=0.75  |  Bottom row: pip_scale=1.00"
+  )
+
+ggsave("demo_output/ndots_comparison.png", ndots_comparison, width = 14, height = 6, dpi = 300)
+
+# ---- Plot 4: size mapping bypasses auto-scale (existing use case) ----------
+
+set.seed(1)
+test_data_sized <- test_data
+test_data_sized$sig <- runif(16, 0.5, 4)
+
+p_size_mapped <- ggplot(test_data_sized, aes(x = x, y = y)) +
   geom_dice(
-    aes(
-      dots = category,
-      fill = value,
-      size = size_val
-    ),
-    ndots = 4,  # 4 categories
-    x_length = 3,
+    aes(dots = category, fill = value, size = sig),
+    ndots    = 4,
+    x_length = 4,
     y_length = 2
+  ) +
+  scale_fill_gradient2(
+    low = "#2166AC", high = "#762A83", mid = "white",
+    midpoint = 0, name = "value"
+  ) +
+  scale_size_continuous(range = c(1, 5), name = "size") +
+  labs(
+    title    = "aes(size = ...) bypasses auto-scaling",
+    subtitle = "pip_scale is ignored; user-mapped sizes are respected"
   )
 
-print(p)
\ No newline at end of file
+ggsave("demo_output/size_mapped_example.png", p_size_mapped, width = 8, height = 5, dpi = 300)
+
+cat("Generated files:\n")
+cat("- demo_output/pip_scale_comparison.png\n")
+cat("- demo_output/tile_size_comparison.png\n")
+cat("- demo_output/ndots_comparison.png\n")
+cat("- demo_output/size_mapped_example.png\n")