#' Prepare limma-voom DEG data for plotting

#'

#' This function reads a limma-voom DEG data frame from an RDS file, filters it using

#' \code{\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to P.Value,

#' and transforms P.Value for plotting. The final data frame is returned and

#' optionally saved to a new RDS file.

#'

#' @param input_path Path to the RDS file containing the limma-voom DEG data frame.

#' @param output_name Name for the processed data frame, also used as the RDS file name.

#' @return A data frame with processed limma-voom DEG data.

#' @export

#' @examples

#' limma_file <- system.file("extdata",

#'                           "DEG_limma_voom_test.rds",

#'                           package = "TransProR")

#' limma <- prep_limma(limma_file)

#'

prep_limma <- function(input_path, output_name = NULL) {

  # Read the limma-voom DEG data frame from an RDS file

  limma <- readRDS(input_path)

  # Filter DEG data using the deg_filter function from the same package

  limma_filter <- deg_filter(limma)

  limma <- limma[rownames(limma) %in% limma_filter, ]

  # Select columns of interest and adjust logFC values to absolute values

  limma <- limma[, c('logFC', "P.Value", "change")]

  limma$logFC <- abs(limma$logFC)

  # Add a small pseudo-count to P.Value to avoid log of zero and transform P.Value for plotting

  limma$P.Value <- limma$P.Value + .Machine$double.eps

  limma$P.Value <- -log10(limma$P.Value)

  # Extract the Gene column from row names

  limma$Gene <- rownames(limma)

  # Rename columns

  names(limma) <- c('logFC', 'Pvalue', "change", 'Gene')

  # Optionally save the processed data frame as an RDS file

  if (!is.null(output_name)) {

    saveRDS(limma, paste0(output_name, ".Rdata"))

  }

  # Return the processed data frame

  return(limma)

}