R/krsa_histogram_plot.R

In CogDisResLab/KRSA: KRSA: Kinome Random Sampling Analyzer

#' Generates kinase histogram plots based on the KRSA function output
#'
#' This function takes in Z score table, and count matrix (an output from krsa()) and generates distribution histograms for a list of kinases
#'
#' @param data Z score table from krsa()
#' @param data2 count matrix from krsa()
#' @param kinases a vector of kinases
#'
#' @return ggplot object
#'
#' @family plots
#'
#' @export
#'
#' @examples
#' TRUE
krsa_histogram_plot <- function(data, data2, kinases) {
    data2 %>%
        dplyr::rename(Kinase = Kin) %>%
        dplyr::filter(Kinase %in% kinases) %>%
        ggplot2::ggplot() +
        ggplot2::geom_histogram(ggplot2::aes(counts), binwidth = 1, fill = "gray30", color = "black") +
        ggplot2::geom_rect(
            data = dplyr::filter(data, Kinase %in% kinases), ggplot2::aes(xmin = SamplingAvg + (2 * SD), xmax = SamplingAvg - (2 * SD), ymin = 0, ymax = Inf),
            fill = "gray", alpha = 0.5
        ) +
        ggplot2::geom_vline(data = dplyr::filter(data, Kinase %in% kinases), ggplot2::aes(xintercept = Observed), color = "red", size = 1, show.legend = F) +
        ggplot2::facet_wrap(~Kinase, scales = "free") +
        ggplot2::labs(x = "Hits", y = "Counts") +
        ggplot2::theme_bw() +
        ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}