#' Extract and Count Descriptions with Specified Color
#'
#' This function filters a data frame for specified descriptions, selects the 'Description' and 'Count' columns,
#' and adds a new column with a specified color.
#'
#' @param df A data frame containing at least 'Description' and 'Count' columns.
#' @param descriptions A vector of descriptions to filter in the data frame.
#' @param color A character string specifying the color to be added as a new column.
#' @return A data frame filtered by descriptions, containing 'Description', 'Count', and a new 'color' column.
#' @export
#'
#' @examples
#' # Generate Sample Input Data for extract_descriptions_counts Function
#'
#' # Create a sample data frame with 'Description' and 'Count' columns
#' data <- data.frame(
#' Description = c(
#' "immunoglobulin production",
#' "B cell mediated immunity",
#' "T cell activation",
#' "antigen processing and presentation",
#' "cytokine signaling",
#' "natural killer cell activity",
#' "phagocytosis",
#' "complement activation",
#' "antibody-dependent cellular cytotoxicity",
#' "regulatory T cell function"
#' ),
#' Count = c(
#' 150, # immunoglobulin production
#' 200, # B cell mediated immunity
#' 175, # T cell activation
#' 125, # antigen processing and presentation
#' 190, # cytokine signaling
#' 160, # natural killer cell activity
#' 140, # phagocytosis
#' 180, # complement activation
#' 130, # antibody-dependent cellular cytotoxicity
#' 170 # regulatory T cell function
#' ),
#' stringsAsFactors = FALSE # Ensure that strings are not converted to factors
#' )
#'
#'
#'
#' descriptions_to_filter <- c("immunoglobulin production", "B cell mediated immunity")
#' specified_color <- "red" # You can specify any color you desire
#' filtered_data_with_color <- extract_descriptions_counts(
#' data, descriptions_to_filter,
#' specified_color)
#' print(filtered_data_with_color)
#'
extract_descriptions_counts <- function(df, descriptions, color) {
# Filter rows where the Description column values are in the descriptions vector
result_df <- df[df$Description %in% descriptions, ]
# Select Description and Count columns
result_df <- result_df[, c("Description", "Count")]
# Add a new column 'color' with the specified color value
result_df$color <- color
return(result_df)
}
#' Combine and Visualize Data with Circular Bar Chart
#'
#' This function combines multiple data frames, arranges them, and visualizes the combined data
#' in a Circular Bar Chart using the 'ggplot2' and 'ggalluvial' packages.
#'
#' @importFrom dplyr bind_rows arrange desc row_number mutate
#' @importFrom ggplot2 ggplot geom_bar geom_text scale_fill_manual scale_y_continuous scale_x_continuous coord_polar labs theme_minimal theme element_rect element_blank
#' @importFrom rlang .data
#' @param data_list A list of data frames to be combined.
#' @return A `ggplot` object representing the Circular Bar Chart.
#' @export
#'
#' @examples
#' # Create sample data frames for each enrichment category
#'
#' # 1. Biological Process (BP)
#' filtered_data_BP <- data.frame(
#' Description = c(
#' "immune response",
#' "cell proliferation",
#' "signal transduction",
#' "apoptotic process",
#' "metabolic process"
#' ),
#' Count = c(120, 85, 150, 60, 95),
#' color = c(
#' "#1f77b4", # blue
#' "#ff7f0e", # orange
#' "#2ca02c", # green
#' "#d62728", # red
#' "#9467bd" # purple
#' ),
#' stringsAsFactors = FALSE
#' )
#'
#' # 2. Cellular Component (CC)
#' filtered_data_CC <- data.frame(
#' Description = c(
#' "nucleus",
#' "cytoplasm",
#' "membrane",
#' "mitochondrion",
#' "extracellular space"
#' ),
#' Count = c(90, 110, 75, 65, 80),
#' color = c(
#' "#1f77b4",
#' "#ff7f0e",
#' "#2ca02c",
#' "#d62728",
#' "#9467bd"
#' ),
#' stringsAsFactors = FALSE
#' )
#'
#' # 3. Molecular Function (MF)
#' filtered_data_MF <- data.frame(
#' Description = c(
#' "protein binding",
#' "DNA binding",
#' "enzyme activity",
#' "transporter activity",
#' "receptor activity"
#' ),
#' Count = c(140, 130, 100, 70, 90),
#' color = c(
#' "#1f77b4",
#' "#ff7f0e",
#' "#2ca02c",
#' "#d62728",
#' "#9467bd"
#' ),
#' stringsAsFactors = FALSE
#' )
#'
#' # 4. Disease Ontology (DO)
#' filtered_data_DO <- data.frame(
#' Description = c(
#' "cancer",
#' "cardiovascular disease",
#' "neurological disorder",
#' "metabolic disease",
#' "infectious disease"
#' ),
#' Count = c(200, 150, 120, 90, 160),
#' color = c(
#' "#1f77b4",
#' "#ff7f0e",
#' "#2ca02c",
#' "#d62728",
#' "#9467bd"
#' ),
#' stringsAsFactors = FALSE
#' )
#'
#' # 5. Reactome Pathways
#' filtered_data_Reactome <- data.frame(
#' Description = c(
#' "Cell Cycle",
#' "Apoptosis",
#' "DNA Repair",
#' "Signal Transduction",
#' "Metabolism of Proteins"
#' ),
#' Count = c(110, 95, 80, 130, 85),
#' color = c(
#' "#1f77b4",
#' "#ff7f0e",
#' "#2ca02c",
#' "#d62728",
#' "#9467bd"
#' ),
#' stringsAsFactors = FALSE
#' )
#'
#' # 6. KEGG Pathways
#' filtered_data_kegg <- data.frame(
#' Description = c(
#' "PI3K-Akt signaling pathway",
#' "MAPK signaling pathway",
#' "NF-kappa B signaling pathway",
#' "JAK-STAT signaling pathway",
#' "Toll-like receptor signaling pathway"
#' ),
#' Count = c(175, 160, 145, 130, 155),
#' color = c(
#' "#1f77b4",
#' "#ff7f0e",
#' "#2ca02c",
#' "#d62728",
#' "#9467bd"
#' ),
#' stringsAsFactors = FALSE
#' )
#'
#' # Combine all filtered data frames into a list
#' data_list <- list(
#' BP = filtered_data_BP,
#' CC = filtered_data_CC,
#' MF = filtered_data_MF,
#' DO = filtered_data_DO,
#' Reactome = filtered_data_Reactome,
#' KEGG = filtered_data_kegg
#' )
#'
#' # Create the Circular Bar Chart
#' combined_and_visualized_data <- enrich_circo_bar(data_list)
#'
enrich_circo_bar <- function(data_list) {
# Combine data frames
combined_data <- dplyr::bind_rows(data_list)
# Sort by 'Count' column in descending order and add an 'id' column
combined_data <- combined_data %>%
dplyr::arrange(.data$Count) %>%
dplyr::mutate(id = dplyr::row_number())
# Ensure 'Description' is a factor with correct level order
combined_data <- combined_data %>%
dplyr::mutate(Description = factor(.data$Description, levels = unique(.data$Description)))
# Extract fill colors, ensuring colors match 'Description' levels
fill_colors <- combined_data$color[match(levels(combined_data$Description), combined_data$Description)]
# Calculate the expanded max values for Count and id
max_count <- max(combined_data$Count) + (max(combined_data$Count) / 5)
max_id <- max(combined_data$id) + 1.5
# Create the plot
p <- ggplot2::ggplot(combined_data, ggplot2::aes(x = .data$id, y = .data$Count, fill = .data$Description)) +
ggplot2::geom_bar(stat = 'identity', width = 0.7) +
ggplot2::geom_text(ggplot2::aes(x = .data$id, y = 0, label = .data$Description), hjust = 1.03, size = 3.5, color = fill_colors) +
ggplot2::scale_fill_manual(values = fill_colors, guide = "none") +
ggplot2::scale_y_continuous(expand = c(0, 0), limits = c(0, max_count), position = 'right') +
ggplot2::scale_x_continuous(expand = c(0, 0), limits = c(-1, max_id)) +
ggplot2::coord_polar(theta = 'y') +
ggplot2::labs(title = 'Enrichment CircularBar Chart', subtitle = "Including: BP/MF/CC/DO/KEGG/Reactome") +
ggplot2::theme_minimal() +
ggplot2::theme(plot.background = ggplot2::element_rect(fill = 'white', color = 'white'),
axis.title = ggplot2::element_blank(),
axis.text = ggplot2::element_blank())
return(p)
}
Datasets
Models
