#' Create a base plot with gene expression data on a phylogenetic tree

#'

#' This function creates a base plot using 'ggtree' and 'ggtreeExtra' libraries, adding gene expression

#' data as colored tiles to the plot. It allows for dynamic coloring of the genes and includes

#' adjustments for alpha transparency based on the expression value.

#'

#' @importFrom ggplot2 aes scale_fill_manual scale_alpha_continuous guide_legend

#' @param p A ggtree plot object to which the data will be added.

#' @param data A data frame containing gene expression data with columns for Samples, Genes, and Values.

#' @param gene_colors A named vector of colors for genes.

#' @param gene_label A character string used as a label in the legend for the genes. Default is "Gene".

#' @return A `ggtree` plot object with the gene expression data added.

#' @export

#'

#' @examples

#' \donttest{

#' # Check and load required packages

#' if (requireNamespace("ggtreeExtra", quietly = TRUE) &&

#'  requireNamespace("ggplot2", quietly = TRUE)) {

#'   library(ggtreeExtra)

#'   library(ggplot2)

#'

#'   file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR")

#'   p <- readRDS(file_path)

#'

#'   # Create gene expression data frame

#'   expression_data <- data.frame(

#'     Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 5),

#'     Gene = rep(paste0("Gene", 1:5), times = 4),

#'     Value = runif(20, min = 0, max = 1)  # Randomly generate expression values between 0 and 1

#'   )

#'

#'   # Define gene colors (named vector)

#'   gene_colors <- c(

#'     Gene1 = "#491588",

#'     Gene2 = "#301b8d",

#'     Gene3 = "#1a237a",

#'     Gene4 = "#11479c",

#'     Gene5 = "#0a5797"

#'   )

#'

#'   # Call create_base_plot function to add gene expression data

#'   p <- create_base_plot(p, expression_data, gene_colors)

#' } else {

#'   message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.")

#' }

#' }

#'

create_base_plot <- function(p, data, gene_colors, gene_label="Gene") {

  # Define local variables

  Sample <- data$Sample

  value <- data$value

  Gene <- data$Gene

  if (!requireNamespace("ggtreeExtra", quietly = TRUE)) {

    stop("ggtreeExtra is required for using create_base_plot. Please install it.", call. = FALSE)

  }

  if (!requireNamespace("ggplot2", quietly = TRUE)) {

    stop("ggplot2 is required to use geom_tile. Please install it.", call. = FALSE)

  }

  p <- p +

    ggtreeExtra::geom_fruit(

      data=data,

      geom="geom_tile",

      mapping=ggplot2::aes(y=Sample, alpha=value, x=Gene, fill=Gene),

      offset=0.001,

      pwidth=2

    ) +

    ggplot2::scale_fill_manual(

      name=gene_label,

      values=gene_colors,

      guide=ggplot2::guide_legend(keywidth=0.65, keyheight=0.35, order=1)

    ) +

    # Assuming the function 'adjust_alpha_scale' is defined elsewhere to adjust alpha scale based on the expression values

    adjust_alpha_scale(data, gene_label)

  return(p)

}

#' Add a boxplot layer to a `ggtree` plot

#'

#' This function adds a boxplot layer to an existing `ggtree` plot object using ggtreeExtra's geom_fruit for boxplots.

#' It is primarily used to display statistical summaries of the data related to gene expressions or other metrics.

#'

#' @importFrom ggplot2 aes

#' @param p An existing ggtree plot object.

#' @param data A data frame containing the data to be plotted. Expected to have columns for 'Sample' and 'value'.

#' @param fill_color A character string specifying the fill color for the boxplots. Default is "#f28131".

#' @param alpha Numeric value for the transparency of the boxplots. Default is 0.6.

#' @param offset Numeric value, the position of the boxplot on the x-axis relative to its gene name. Default is 0.22.

#' @param pwidth Numeric value, the width of the boxplot. Default is 0.5.

#' @return A `ggtree` plot object with the added boxplot layer.

#' @export

#'

#' @examples

#' \donttest{

#' # Check and load required packages

#' if (requireNamespace("ggtreeExtra", quietly = TRUE) &&

#'  requireNamespace("ggplot2", quietly = TRUE)) {

#'   library(ggtreeExtra)

#'   library(ggplot2)

#'

#'   file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR")

#'   p <- readRDS(file_path)

#'

#'   # Create boxplot data frame

#'   boxplot_data <- data.frame(

#'     Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 30),

#'     value = c(

#'       rnorm(30, mean = 5, sd = 1),   # Data for Species_A

#'       rnorm(30, mean = 7, sd = 1.5), # Data for Species_B

#'       rnorm(30, mean = 6, sd = 1.2), # Data for Species_C

#'       rnorm(30, mean = 8, sd = 1.3)  # Data for Species_D

#'     )

#'   )

#'

#'   # Call add_boxplot function to add boxplot layer

#'   p_with_boxplot <- add_boxplot(p, boxplot_data)

#' } else {

#'   message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.")

#' }

#' }

#'

add_boxplot <- function(p, data, fill_color="#f28131", alpha=0.6, offset=0.22, pwidth=0.5) {

  # Define local variables

  Sample <- data$Sample

  value <- data$value

  if (!requireNamespace("ggtreeExtra", quietly = TRUE)) {

    stop("ggtreeExtra is required for using create_base_plot. Please install it.", call. = FALSE)

  }

  if (!requireNamespace("ggplot2", quietly = TRUE)) {

    stop("ggplot2 is required to use geom_boxplot. Please install it.", call. = FALSE)

  }

  p + ggtreeExtra::geom_fruit(

    data=data,

    geom="geom_boxplot",

    mapping=ggplot2::aes(y=Sample, x=value),

    fill=fill_color,

    alpha=alpha,

    offset=offset,

    pwidth=pwidth,

    size=0.05,

    outlier.size=0.3,

    outlier.stroke=0.06,

    outlier.shape=21,

    show.legend=FALSE

  )

}

#' Add a new tile layer with dynamic scales to a `ggtree` plot

#'

#' This function adds a new tile layer to an existing `ggtree` plot object, allowing for separate scales for fill

#' and alpha transparency. This is useful when you want to add additional data layers without interfering with

#' the existing scales in the plot. It utilizes the 'ggnewscale' package to reset scales for new layers.

#'

#' @importFrom ggplot2 aes scale_fill_manual guide_legend

#' @importFrom ggnewscale new_scale

#' @param p An existing ggtree plot object.

#' @param data A data frame containing the data to be plotted. Expected to have columns for 'Sample', 'Gene', and 'value'.

#' @param gene_colors A named vector of colors for genes.

#' @param gene_label A character string used as a label in the legend for the genes.

#' @param alpha_value A numeric or named vector for setting the alpha scale based on values.

#' @param offset Numeric value, the position of the tile on the x-axis relative to its gene name. Default is 0.02.

#' @param pwidth Numeric value, the width of the tile. Default is 2.

#' @return A `ggtree` plot object with the added tile layer and new scales.

#' @export

#'

#' @examples

#' \donttest{

#' # Check and load required packages

#' if (requireNamespace("ggtreeExtra", quietly = TRUE) &&

#'  requireNamespace("ggplot2", quietly = TRUE) &&

#'   requireNamespace("ggnewscale", quietly = TRUE)) {

#'   library(ggtreeExtra)

#'   library(ggplot2)

#'   library(ggnewscale)

#'

#'   file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR")

#'   p <- readRDS(file_path)

#'

#'   # Create new expression data

#'   new_expression_data <- data.frame(

#'     Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 3),

#'     Gene = rep(c("Gene6", "Gene7", "Gene8"), times = 4),

#'     Value = runif(12, min = 0, max = 1)  # Randomly generate expression values between 0 and 1

#'   )

#'

#'   # Define new gene colors

#'   new_gene_colors <- c(

#'     Gene6 = "#0b5f63",

#'     Gene7 = "#074d41",

#'     Gene8 = "#1f5e27"

#'   )

#'

#'   # Define gene label and alpha values

#'   gene_label <- "New Genes"

#'   alpha_value <- c(0.3, 0.9)

#'

#'   # Add new tile layer

#'   p_with_new_layer <- add_new_tile_layer(

#'     p,

#'     new_expression_data,

#'     new_gene_colors,

#'     gene_label,

#'     alpha_value,

#'     offset = 0.02,

#'     pwidth = 2

#'   )

#' } else {

#'   message("Required packages 'ggtreeExtra', 'ggplot2', and 'ggnewscale' are not installed.")

#' }

#' }

#'

add_new_tile_layer <- function(p, data, gene_colors, gene_label, alpha_value=c(0.3, 0.9), offset=0.02, pwidth=2) {

  # Define local variables

  Sample <- data$Sample

  value <- data$value

  Gene <- data$Gene

  if (!requireNamespace("ggtreeExtra", quietly = TRUE)) {

    stop("ggtreeExtra is required for using create_base_plot. Please install it.", call. = FALSE)

  }

  if (!requireNamespace("ggplot2", quietly = TRUE)) {

    stop("ggplot2 is required to use geom_tile. Please install it.", call. = FALSE)

  }

  p + ggnewscale::new_scale("alpha") + ggnewscale::new_scale("fill") +

    ggtreeExtra::geom_fruit(

      data=data,

      geom="geom_tile",

      mapping=ggplot2::aes(y=Sample, alpha=value, x=Gene, fill=Gene),

      offset=offset,

      pwidth=pwidth

    ) +

    ggplot2::scale_fill_manual(

      name=gene_label,

      values=gene_colors,

      guide=ggplot2::guide_legend(keywidth=0.65, keyheight=0.35, order=1)

    ) +

    adjust_alpha_scale(data, gene_label, alpha_value)  # Assuming function signature and usage

}

#' Add multiple layers to a `ggtree` plot for visualizing gene expression and enrichment data

#'

#' This function sequentially adds multiple layers to a `ggtree` plot, including gene expression data, boxplots for statistical

#' summaries, and additional tile layers for pathway enrichment scores from SSGSEA and GSVA analyses. It utilizes separate

#' functions for adding each type of layer and allows for the specification of gene colors as well as adjustments in aesthetics

#' for each layer. The function is designed to work with specific data structures and assumes all functions for adding layers

#' are defined and available.

#'

#' @param p A `ggtree` plot object to which the data and layers will be added.

#' @param long_format_HeatdataDeseq A data frame containing gene expression data with columns for `Samples`, `Genes`, and `Values`.

#' @param ssgsea_kegg_HeatdataDeseq A data frame containing SSGSEA analysis results with columns for `Samples`, `Genes`, and `Values`.

#' @param gsva_kegg_HeatdataDeseq A data frame containing GSVA analysis results with columns for `Samples`, `Genes`, and `Values`.

#' @param gene_colors A named vector of colors for genes, used for coloring tiles in different layers.

#' @return A `ggtree` plot object with multiple layers added for comprehensive visualization.

#' @export

#'

#' @examples

#' \donttest{

#' # Check and load required packages

#' if (requireNamespace("ggtreeExtra", quietly = TRUE) &&

#'  requireNamespace("ggplot2", quietly = TRUE)) {

#'   library(ggtreeExtra)

#'   library(ggplot2)

#'

#'   # Example data for gene expression, SSGSEA, and GSVA

#'   file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR")

#'   p <- readRDS(file_path)

#'

#'   # Create gene expression data frame (long_format_HeatdataDeseq)

#'   long_format_HeatdataDeseq <- data.frame(

#'     Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 5),

#'     Genes = rep(paste0("Gene", 1:5), times = 4),

#'     Value = runif(20, min = 0, max = 1)  # Randomly generate expression values between 0 and 1

#'   )

#'

#'   # Create SSGSEA analysis results data frame (ssgsea_kegg_HeatdataDeseq)

#'   ssgsea_kegg_HeatdataDeseq <- data.frame(

#'     Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 3),

#'     Genes = rep(c("Pathway1", "Pathway2", "Pathway3"), times = 4),

#'     Value = runif(12, min = 0, max = 1)  # Randomly generate enrichment scores between 0 and 1

#'   )

#'

#'   # Create GSVA analysis results data frame (gsva_kegg_HeatdataDeseq)

#'   gsva_kegg_HeatdataDeseq <- data.frame(

#'     Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 4),

#'     Genes = rep(c("PathwayA", "PathwayB", "PathwayC", "PathwayD"), times = 4),

#'     Value = runif(16, min = 0, max = 1)  # Randomly generate enrichment scores between 0 and 1

#'   )

#'

#'   # Define gene and pathway colors (named vector), including all genes and pathways

#'   gene_colors <- c(

#'     # Genes for gene expression

#'     Gene1 = "#491588",

#'     Gene2 = "#301b8d",

#'     Gene3 = "#1a237a",

#'     Gene4 = "#11479c",

#'     Gene5 = "#0a5797",

#'     # Pathways for SSGSEA

#'     Pathway1 = "#0b5f63",

#'     Pathway2 = "#074d41",

#'     Pathway3 = "#1f5e27",

#'     # Pathways for GSVA

#'     PathwayA = "#366928",

#'     PathwayB = "#827729",

#'     PathwayC = "#a1d99b",

#'     PathwayD = "#c7e9c0"

#'   )

#'

#'   # Call circos_fruits function to add multiple layers

#'   final_plot <- circos_fruits(

#'     p,

#'     long_format_HeatdataDeseq,

#'     ssgsea_kegg_HeatdataDeseq,

#'     gsva_kegg_HeatdataDeseq,

#'     gene_colors

#'   )

#' } else {

#'   message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.")

#' }

#' }

#'

circos_fruits <- function(p, long_format_HeatdataDeseq, ssgsea_kegg_HeatdataDeseq, gsva_kegg_HeatdataDeseq, gene_colors) {

  if (!requireNamespace("ggtreeExtra", quietly = TRUE)) {

    stop("ggtreeExtra is required for using create_base_plot. Please install it.", call. = FALSE)

  }

  if (!requireNamespace("ggplot2", quietly = TRUE)) {

    stop("ggplot2 is required to use geom_tile. Please install it.", call. = FALSE)

  }

  # Create the base plot with gene expression data

  p1 <- create_base_plot(p, long_format_HeatdataDeseq, gene_colors)

  # Add a boxplot layer to the base plot

  p2 <- add_boxplot(p1, long_format_HeatdataDeseq)

  # Add a new tile layer for SSGSEA data

  p3 <- add_new_tile_layer(p2, ssgsea_kegg_HeatdataDeseq, gene_colors, "Ssgsea Term")

  # Add another boxplot layer with specific aesthetic adjustments

  p4 <- add_boxplot(p3, ssgsea_kegg_HeatdataDeseq, fill_color="#f28131", alpha=0.65, offset=0.32)

  # Add a new tile layer for GSVA data with specific alpha and offset adjustments

  p5 <- add_new_tile_layer(p4, gsva_kegg_HeatdataDeseq, gene_colors, "Gsva Term", alpha_value=c(0.3, 0.9), offset=0.02)

  # Return the final plot

  return(p5)

}