#' Create Pathway-Gene Mapping Data Frame

#'

#' This function takes multiple data frames and pathway IDs, merging them into a new data frame.

#' Each data frame represents a type of analysis (e.g., BP, KEGG, MF, etc.).

#' @param BP_dataframe Data frame for Biological Process analysis

#' @param BP_ids Selected pathway IDs for Biological Process analysis

#' @param KEGG_dataframe Data frame for KEGG analysis

#' @param KEGG_ids Selected pathway IDs for KEGG analysis

#' @param MF_dataframe Data frame for Molecular Function analysis

#' @param MF_ids Selected pathway IDs for Molecular Function analysis

#' @param REACTOME_dataframe Data frame for REACTOME analysis

#' @param REACTOME_ids Selected pathway IDs for REACTOME analysis

#' @param CC_dataframe Data frame for Cellular Component analysis

#' @param CC_ids Selected pathway IDs for Cellular Component analysis

#' @param DO_dataframe Data frame for Disease Ontology analysis

#' @param DO_ids Selected pathway IDs for Disease Ontology analysis

#' @return A new data frame that includes pathways, gene, type, and value columns

#' @export

#' @examples

#' # Simulating data for different analysis types

#'

#' # Simulate Biological Process (BP) data frame

#' BP_df <- data.frame(

#'   ID = c("GO:0002376", "GO:0019724"),

#'   geneID = c("GENE1/GENE2", "GENE3/GENE4"),

#'   Description = c("Immune response", "Glycosylation process")

#' )

#'

#' # Simulate KEGG data frame

#' KEGG_df <- data.frame(

#'   ID = c("12345", "67890"),

#'   geneID = c("GENE5/GENE6", "GENE7/GENE8"),

#'   Description = c("Pathway 1", "Pathway 2")

#' )

#'

#' # Simulate Molecular Function (MF) data frame

#' MF_df <- data.frame(

#'   ID = c("ABC123", "DEF456"),

#'   geneID = c("GENE9/GENE10", "GENE11/GENE12"),

#'   Description = c("Molecular function A", "Molecular function B")

#' )

#'

#' # Simulate REACTOME data frame

#' REACTOME_df <- data.frame(

#'   ID = c("R-HSA-12345", "R-HSA-67890"),

#'   geneID = c("GENE13/GENE14", "GENE15/GENE16"),

#'   Description = c("Pathway in Reactome 1", "Pathway in Reactome 2")

#' )

#'

#' # Simulate Cellular Component (CC) data frame

#' CC_df <- data.frame(

#'   ID = c("GO:0005575", "GO:0005634"),

#'   geneID = c("GENE17/GENE18", "GENE19/GENE20"),

#'   Description = c("Cellular component A", "Cellular component B")

#' )

#'

#' # Simulate Disease Ontology (DO) data frame

#' DO_df <- data.frame(

#'   ID = c("DOID:123", "DOID:456"),

#'   geneID = c("GENE21/GENE22", "GENE23/GENE24"),

#'   Description = c("Disease A", "Disease B")

#' )

#'

#' # Example pathway IDs for each analysis

#' BP_ids <- c("GO:0002376", "GO:0019724")

#' KEGG_ids <- c("12345", "67890")

#' MF_ids <- c("ABC123", "DEF456")

#' REACTOME_ids <- c("R-HSA-12345", "R-HSA-67890")

#' CC_ids <- c("GO:0005575", "GO:0005634")

#' DO_ids <- c("DOID:123", "DOID:456")

#'

#' # Generate the pathway-gene map using the gene_map_pathway function

#' pathway_gene_map <- gene_map_pathway(

#'   BP_dataframe = BP_df, BP_ids = BP_ids,

#'   KEGG_dataframe = KEGG_df, KEGG_ids = KEGG_ids,

#'   MF_dataframe = MF_df, MF_ids = MF_ids,

#'   REACTOME_dataframe = REACTOME_df, REACTOME_ids = REACTOME_ids,

#'   CC_dataframe = CC_df, CC_ids = CC_ids,

#'   DO_dataframe = DO_df, DO_ids = DO_ids

#' )

#'

#' # Display the resulting pathway-gene mapping data frame

#' print(pathway_gene_map)

#'

gene_map_pathway <- function(BP_dataframe, BP_ids, KEGG_dataframe, KEGG_ids, MF_dataframe, MF_ids, REACTOME_dataframe, REACTOME_ids, CC_dataframe, CC_ids, DO_dataframe, DO_ids) {

  # Create an empty data frame

  pathway_gene_map <- data.frame(

    pathway_description = character(),

    gene4 = character(),

    type = character(),

    value = integer()

  )

  # Helper function to extract information from a data frame and add it to the new data frame

  add_to_map <- function(df, ids, type) {

    if (is.null(df) || is.null(ids)) return()

    selected_rows <- df[df$ID %in% ids, ]

    for (row in seq(nrow(selected_rows))) {

      genes <- strsplit(as.character(selected_rows$geneID[row]), "/")[[1]]

      # Here, instead of the ID, we use the Description column

      description <- as.character(selected_rows$Description[row])

      for (gene in genes) {

        pathway_gene_map <<- rbind(pathway_gene_map, data.frame(

          pathway = description,

          gene = gene,

          type = type,

          value = 1

        ))

      }

    }

  }

  # Apply the helper function to add data for each type of analysis

  add_to_map(BP_dataframe, BP_ids, "BP")

  add_to_map(KEGG_dataframe, KEGG_ids, "KEGG")

  add_to_map(MF_dataframe, MF_ids, "MF")

  add_to_map(REACTOME_dataframe, REACTOME_ids, "REACTOME")

  add_to_map(CC_dataframe, CC_ids, "CC")

  add_to_map(DO_dataframe, DO_ids, "DO")

  return(pathway_gene_map)

}