Package 'diceplot'

Calculate Dynamic Dot Size

Description

Calculates the dot size based on the number of variables.

Usage

calculate_dot_size(num_vars)

Arguments

num_vars

Number of variables.

Value

A numeric value representing the dot size.

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Create Custom Legends

Description

Creates custom legend plots for cat_c and group.

Usage

create_custom_legends(
  data,
  cat_c,
  group,
  cat_c_colors,
  group_colors,
  var_positions,
  num_vars,
  dot_size
)

Arguments

data	The original data frame.
cat_c	The name of the cat_c variable.
group	The name of the group variable.
cat_c_colors	A named vector of colors for cat_c.
group_colors	A named vector of colors for the group variable.
var_positions	Data frame with variable positions.
num_vars	Number of variables in cat_c.
dot_size	The size of the dots used in the plot.

Value

A combined ggplot object of the custom legends.

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Create Variable Positions

Description

Generates a data frame containing variable names from cat_c_colors and corresponding x and y offsets based on the number of variables.

Usage

create_var_positions(cat_c_colors, num_vars)

Arguments

cat_c_colors	A named vector of colors for variables in category C. The names correspond to variable names.
num_vars	The number of variables. Supported values are "3", "4", "5", or "6".

Value

A data frame with columns:

var

Factor of variable names from cat_c_colors.

x_offset

Numeric x-axis offset for plotting.

y_offset

Numeric y-axis offset for plotting.

Examples

library(dplyr)
cat_c_colors <- c("Var1" = "red", "Var2" = "blue", "Var3" = "green")
create_var_positions(cat_c_colors, 3)

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Dice Plot Visualization

Description

This function generates a custom plot based on three categorical variables and a group variable. It adapts to the number of unique categories in cat_c and allows customization of various plot aesthetics.

Usage

dice_plot(
  data,
  cat_a,
  cat_b,
  cat_c,
  group,
  plot_path = NULL,
  output_str = NULL,
  group_alpha = 0.5,
  title = NULL,
  cat_c_colors = NULL,
  group_colors = NULL,
  format = ".pdf",
  custom_theme = theme_minimal()
)

Arguments

data	A data frame containing the categorical and group variables for plotting.
cat_a	A string representing the column name in data for the first categorical variable.
cat_b	A string representing the column name in data for the second categorical variable.
cat_c	A string representing the column name in data for the third categorical variable.
group	A string representing the column name in data for the grouping variable.
plot_path	A string specifying the file path where the plot will be saved. Defaults to NULL (no file output).
output_str	An optional string to customize the filename if plot_path is specified. Defaults to NULL.
group_alpha	A numeric value for the transparency level of the group rectangles. Default is 0.5.
title	An optional string for the plot title. Defaults to NULL.
cat_c_colors	A named vector of colors for cat_c categories. Defaults to NULL (automatically generated).
group_colors	A named vector of colors for the group variable. Defaults to NULL (automatically generated).
format	A string specifying the output file format if saving the plot. Defaults to ".pdf".
custom_theme	A ggplot2 theme for customizing the plot's appearance. Defaults to theme_minimal().

Value

A ggplot object representing the dice plot.

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Domino Plot Visualization

Description

This function generates a plot to visualize gene expression levels for a given list of genes. The size of the dots can be customized, and the plot can be saved to an output file if specified.

Usage

domino_plot(
  data,
  gene_list,
  switch_axis = FALSE,
  min_dot_size = 1,
  max_dot_size = 5,
  spacing_factor = 3,
  var_id = "var",
  feature_col = "gene",
  celltype_col = "Celltype",
  contrast_col = "Contrast",
  contrast_levels = c("Clinical", "Pathological"),
  contrast_labels = c("Clinical", "Pathological"),
  logfc_col = "avg_log2FC",
  pval_col = "p_val_adj",
  logfc_limits = c(-1.5, 1.5),
  logfc_colors = c(low = "blue", mid = "white", high = "red"),
  color_scale_name = "Log2 Fold Change",
  size_scale_name = "-log10(adj. p-value)",
  axis_text_size = 8,
  aspect_ratio = NULL,
  base_width = 5,
  base_height = 4,
  output_file = NULL
)

Arguments

data	A data frame containing gene expression data.
gene_list	A character vector of gene names to include in the plot.
switch_axis	A logical value indicating whether to switch the x and y axes. Default is FALSE.
min_dot_size	A numeric value indicating the minimum dot size in the plot. Default is 1.
max_dot_size	A numeric value indicating the maximum dot size in the plot. Default is 5.
spacing_factor	A numeric value indicating the spacing between gene pairs. Default is 3.
var_id	A string representing the column name in data for the variable identifier. Default is "var".
feature_col	A string representing the column name in data for the feature variable (e.g., genes). Default is "gene".
celltype_col	A string representing the column name in data for the cell type variable. Default is "Celltype".
contrast_col	A string representing the column name in data for the contrast variable. Default is "Contrast".
contrast_levels	A character vector specifying the levels of the contrast variable. Default is c("Clinical", "Pathological").
contrast_labels	A character vector specifying the labels for the contrasts in the plot. Default is c("Clinical", "Pathological").
logfc_col	A string representing the column name in data for the log fold change values. Default is "avg_log2FC".
pval_col	A string representing the column name in data for the adjusted p-values. Default is "p_val_adj".
logfc_limits	A numeric vector of length 2 specifying the limits for the log fold change color scale. Default is c(-1.5, 1.5).
logfc_colors	A named vector specifying the colors for the low, mid, and high values in the color scale. Default is c(low = "blue", mid = "white", high = "red").
color_scale_name	A string specifying the name of the color scale in the legend. Default is "Log2 Fold Change".
size_scale_name	A string specifying the name of the size scale in the legend. Default is "-log10(adj. p-value)".
axis_text_size	A numeric value specifying the size of the axis text. Default is 8.
aspect_ratio	A numeric value specifying the aspect ratio of the plot. If NULL, it's calculated automatically. Default is NULL.
base_width	A numeric value specifying the base width for saving the plot. Default is 5.
base_height	A numeric value specifying the base height for saving the plot. Default is 4.
output_file	An optional string specifying the path to save the plot. If NULL, the plot is not saved. Default is NULL.

Value

A ggplot object representing the domino plot.

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Order Category B

Description

Determines the ordering of category B based on the counts within each group, ordered by group and count.

Usage

order_cat_b(data, group, cat_b, group_colors)

Arguments

data	A data frame containing the variables.
group	The name of the column representing the grouping variable.
cat_b	The name of the column representing category B.
group_colors	A named vector of colors for each group. The names correspond to group names.

Value

A vector of category B labels ordered according to group and count.

Examples

library(dplyr)
data <- data.frame(
  group = rep(c("G1", "G2"), each = 5),
  cat_b = sample(LETTERS[1:3], 10, replace = TRUE)
)
group_colors <- c("G1" = "red", "G2" = "blue")
order_cat_b(data, "group", "cat_b", group_colors)

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Perform Hierarchical Clustering on Category A

Description

Performs hierarchical clustering on category A based on the binary presence of combinations of categories B and C.

Usage

perform_clustering(data, cat_a, cat_b, cat_c)

Arguments

data	A data frame containing the variables.
cat_a	The name of the column representing category A.
cat_b	The name of the column representing category B.
cat_c	The name of the column representing category C.

Value

A vector of category A labels ordered according to the hierarchical clustering.

Examples

library(dplyr)
library(tidyr)
library(tibble)
data <- data.frame(
  cat_a = rep(letters[1:5], each = 4),
  cat_b = rep(LETTERS[1:2], times = 10),
  cat_c = sample(c("Var1", "Var2", "Var3"), 20, replace = TRUE)
)
perform_clustering(data, "cat_a", "cat_b", "cat_c")

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Prepare Box Data

Description

Prepares data for plotting boxes by calculating box boundaries based on category positions.

Usage

prepare_box_data(data, cat_a, cat_b, group, cat_a_order, cat_b_order)

Arguments

data	A data frame containing the variables.
cat_a	The name of the column representing category A.
cat_b	The name of the column representing category B.
group	The name of the column representing the grouping variable.
cat_a_order	A vector specifying the order of category A.
cat_b_order	A vector specifying the order of category B.

Value

A data frame with box boundaries for plotting.

Examples

library(dplyr)
data <- data.frame(
  cat_a = rep(letters[1:3], each = 2),
  cat_b = rep(LETTERS[1:2], times = 3),
  group = rep(c("G1", "G2"), times = 3)
)
cat_a_order <- c("a", "b", "c")
cat_b_order <- c("A", "B")
prepare_box_data(data, "cat_a", "cat_b", "group", cat_a_order, cat_b_order)

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Prepare Plot Data

Description

Prepares data for plotting by calculating positions based on provided variable positions and orders.

Usage

prepare_plot_data(
  data,
  cat_a,
  cat_b,
  cat_c,
  group,
  var_positions,
  cat_a_order,
  cat_b_order
)

Arguments

data	A data frame containing the variables.
cat_a	The name of the column representing category A.
cat_b	The name of the column representing category B.
cat_c	The name of the column representing category C.
group	The name of the column representing the grouping variable.
var_positions	A data frame with variable positions, typically output from create_var_positions.
cat_a_order	A vector specifying the order of category A.
cat_b_order	A vector specifying the order of category B.

Value

A data frame ready for plotting with added x_pos and y_pos columns.

Examples

library(dplyr)
data <- data.frame(
  cat_a = rep(letters[1:3], each = 4),
  cat_b = rep(LETTERS[1:2], times = 6),
  cat_c = rep(c("Var1", "Var2"), times = 6),
  group = rep(c("G1", "G2"), times = 6)
)
var_positions <- data.frame(
  var = c("Var1", "Var2"),
  x_offset = c(0.1, -0.1),
  y_offset = c(0.1, -0.1)
)
cat_a_order <- c("a", "b", "c")
cat_b_order <- c("A", "B")
prepare_plot_data(data, "cat_a", "cat_b", "cat_c", "group", var_positions, cat_a_order, cat_b_order)

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