#' Get GTEx Expression Data for Specific Organ

#'

#' This function retrieves gene expression data from the GTEx project that is specific to a certain organ.

#' It performs various checks and processing steps to ensure that the data is consistent and relevant to the

#' specified organ. The filtered and cleaned data is saved as an RDS file for further analysis.

#'

#' @param organ_specific A character string specifying the organ to filter the gene expression data by.

#' @param file_path A character string specifying the path to the GTEx gene expression data file.

#' @param probe_map_path A character string specifying the path to the gtex_probeMap_gencode data file.

#' @param pheno_path A character string specifying the path to the GTEx phenotype data file.

#' @param output_path A character string specifying the path where the output RDS file will be saved.

#'

#' @details The function begins by checking if the gene expression and phenotype data files exist at

#'          the specified paths. It then loads these data files and processes them by setting appropriate row names,

#'          modifying column names for clarity, and filtering samples based on the specified organ. The function ensures

#'          that only samples present in both datasets are retained for consistency. It also removes any duplicate gene

#'          entries to prevent redundancy. Finally, the processed data is saved as an RDS file.

#'

#' @return A data frame containing gene expression data for the specified organ.

#'         Rows represent genes, and columns represent samples. Note that this function also saves the

#'         organ-specific GTEx data as an RDS file at the specified output path.

#'

#' @note The function will stop and throw an error if the input files do not exist, or if no samples are found

#'       for the specified organ.

#'

#' @note CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended

#'       that the path includes specific identifiers related to the target samples. Please structure the 'output_path' following this pattern: './your_directory/your_sample_type.gtex.rds'.

#'

#' @importFrom utils read.table

#' @importFrom dplyr distinct filter

#' @importFrom rlang .data

#' @export

get_gtex_exp <- function(organ_specific,

                          file_path,

                          probe_map_path,

                          pheno_path,

                          output_path) {

  # Check for the existence of the file paths

  if (!file.exists(file_path) | !file.exists(pheno_path) | !file.exists(probe_map_path)) {

    stop("One or more of the input files do not exist.")

  }

  # Load the gene expression, probe map, and phenotype data files from the provided paths

  # gtex.exp <- data.table::fread(file_path, header = TRUE, sep = '\t', data.table = FALSE)

  # gtex.pro <- data.table::fread(probe_map_path, header = TRUE, sep = '\t', data.table = FALSE)

  # gtex.phe <- data.table::fread(pheno_path, header = TRUE, sep = '\t', data.table = FALSE)

  # Load the gene expression, probe map, and phenotype data files

  gtex.exp <- utils::read.table(file_path,

                        header = TRUE,

                        sep = '\t',

                        stringsAsFactors = FALSE,

                        check.names = FALSE)

  gtex.pro <- utils::read.table(probe_map_path,

                        header = TRUE,

                        sep = '\t',

                        stringsAsFactors = FALSE,

                        check.names = FALSE)

  gtex.phe <- utils::read.table(pheno_path,

                        header = TRUE,

                        sep = '\t',

                        stringsAsFactors = FALSE,

                        check.names = FALSE)

  # Merge the probe map with the expression data

  gtex.pro <- gtex.pro[, c(1,2)]  # Assuming the columns of interest are the first two

  gtex.count.pro <- merge(gtex.pro, gtex.exp, by.x = "id", by.y = "sample")

  # Set the row names for the samples, facilitating subsequent operations

  rownames(gtex.phe) <- gtex.phe$Sample

  # Modify column names to be more intuitive

  colnames(gtex.phe) <- c("Sample", "body_site_detail (SMTSD)", "primary_site", "gender", "patient", "cohort")

  # Filter samples based on the specified organ

  specific_samples <- dplyr::filter(gtex.phe, .data$primary_site == organ_specific)

  # If no corresponding samples are found, halt the function with an error message

  if (nrow(specific_samples) == 0) {

    stop("No samples found for the specified organ.")

  }

  # Print the number of samples found for the specified organ

  message("Number of samples for", organ_specific, ":", nrow(specific_samples), "\n")

  # Ensure processing only for samples present in both expression and phenotype data through intersection

  valid_sample_names <- intersect(rownames(specific_samples), colnames(gtex.count.pro)) # merge_phe_count_gtex

  gtex_data <- gtex.count.pro[, c("gene", valid_sample_names)]  # Extract data for relevant samples

  # Remove duplicate gene entries and set row names as gene names

  gtex_data <- dplyr::distinct(gtex_data, .data$gene, .keep_all = TRUE)

  rownames(gtex_data) <- gtex_data$gene

  gtex_data <- gtex_data[, -1]  # Remove the 'gene' column, keeping only expression data

  # Save the results as an RDS file for future data analysis tasks

  saveRDS(gtex_data, output_path)

  return(gtex_data)

}