#' Count Genes Present in Pathways Above a Threshold

#'

#' This function filters pathways that meet a count threshold and then counts the presence of specified genes in those pathways.

#'

#' @importFrom dplyr filter

#' @importFrom rlang .data

#' @param GO A character vector of gene symbols.

#' @param count_threshold An integer specifying the count threshold for selecting pathways.

#' @param enrich_data A data frame containing pathway enrichment analysis results.

#' @return A data frame with columns "Symble" (gene symbol), "Description" (pathway description), and "Exists" (1 if gene is present, 0 otherwise).

#' @export

#'

#' @examples

#' # Simulated gene list

#' GO <- c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5")

#' # Simulated enrichment analysis data

#' enrich_data <- data.frame(

#'   ID = c("GO:0001", "GO:0002", "GO:0003"),

#'   Description = c("Pathway A", "Pathway B", "Pathway C"),

#'   Count = c(10, 4, 6),

#'   geneID = c("Gene1/Gene2/Gene3", "Gene4/Gene5", "Gene2/Gene6/Gene7")

#' )

#'

#' # Example usage

#' count_threshold <- 5

#' result_df <- pathway_count(GO, count_threshold, enrich_data)

#'

pathway_count <- function(GO, count_threshold, enrich_data) {

  # Filter pathways meeting the count threshold

  selected_pathways <- enrich_data %>%

    dplyr::filter(.data$Count > count_threshold)

  final_df <- data.frame(Symble = character(), Description = character(), Exists = integer())

  # Iterate through each selected pathway

  for (i in 1:nrow(selected_pathways)) {

    pathway_info <- selected_pathways[i, ]

    genes_in_pathway <- unlist(strsplit(as.character(pathway_info$geneID), "/"))

    # Create a record for each gene in the current pathway

    for (gene in GO) {

      final_df <- rbind(final_df, data.frame(

        Symble = gene,

        Description = pathway_info$Description,

        Exists = as.integer(gene %in% genes_in_pathway)

      ))

    }

  }

  return(final_df)

}