R/old/plot_network_weight_distribution.R

In drostlab/edgynode: Comparison of Gene Regulatory Networks

#' @title Plot the weight distribution for all edges of a gene
#' @description This function takes a weighted adjacency matrix and plots
#' the weight distribution for all edges of a gene.
#' @param adj_mat a weighted adjacency matrix.
#' @param xlab x-axis label.
#' @param ylab y-axis label.
#' @param threshold we recommended to use \code{\link{network_rescale}}
#' before using this function. Re-scaling will transform all values into a range [0,100].
#' The threshold can either be a numeric balue in the interval [0,100] or a character string
#' specifying the following methods for automatically determining the threshold based on the input data:
#' \itemize{
#' \item \code{threshold = "median"}: compute the \code{\link{median}} over the entire input \code{adj_mat} and use this
#' \code{median} value as threshold for defining all edge weights of a genes equal or below the \code{median}
#' value as \code{0} and all values above the \code{median} value as \code{1}.
#' }
#' The threshold value will then be drawn as vertical line in the plot.
#' @param x_ticks number of y-axis ticks.
#' @author Sergio Vasquez and Hajk-Georg Drost
#' @examples
#' # path to PPCOR output file
#' ppcor_output <- system.file('beeline_examples/PPCOR/outFile.txt', package = 'edgynode')
#' # import PPCOR output into adjacency matrix
#' ppcor_parsed <- ppcor(ppcor_output)
#' # rescaling weighted adjacency matrix to range [0,100]
#' ppcor_rescaled <- network_rescale(ppcor_parsed)
#' # visualize weight distributions over all edges for each gene
#' plot_network_weight_distribution(ppcor_rescaled, threshold = 70)
#' @export

plot_network_weight_distribution <-
  function (adj_mat,
            xlab = "Edge weight",
            ylab = "Gene name",
            threshold,
            x_ticks = 10) {

    adj_mat_long <-
      tidyr::pivot_longer(tibble::as_tibble(adj_mat), cols = 1:ncol(adj_mat))

    value <- name <- NULL

    if (is.numeric(threshold)) {
      p <-
        ggplot2::ggplot(adj_mat_long, ggplot2::aes(x = value, y = name, colour = value)) + ggplot2::geom_point() +
        ggplot2::xlab(xlab) + ggplot2::ylab(ylab) +
        ggplot2::geom_vline(
          xintercept = threshold,
          color = "red",
          size = 1.5,
          alpha = 0.3
        ) + ggplot2::scale_colour_continuous(high = "#132B43", low = "#56B1F7")
    }

    if (is.character(threshold)) {
      if (threshold == "median"){
        p <-
          ggplot2::ggplot(adj_mat_long, ggplot2::aes(x = value, y = name, colour = value)) + ggplot2::geom_point() +
          ggplot2::xlab(xlab) + ggplot2::ylab(ylab) +
          ggplot2::geom_vline(
            xintercept = round(stats::median(adj_mat), 2),
            color = "red",
            size = 1.5,
            alpha = 0.3
          ) + ggplot2::scale_colour_continuous(high = "#132B43", low = "#56B1F7") +
          ggplot2::scale_x_continuous(breaks = scales::pretty_breaks(n = x_ticks))
      }
    }

    return(p)
  }