R/plot_bar_summary.R

In neurogenomics/MultiEWCE: Multi-Scale Target Explorer

#' Summary plot
#'
#' Generate a plot summarising the cell type-phenotype enrichment
#' results generated by \link[MSTExplorer]{gen_results}.
#' @param count_var Variable to get counts for per \code{group_var}.
#' @param group_var Variable to group counts by.
#' @param keywords Keywords supplied to search for phenotypes.
#'  Will be used to generate the plot title.
#' @inheritParams ggnetwork_plot_full
#' @inheritParams HPOExplorer::filter_descendants
#' @inheritParams HPOExplorer::make_network_plot
#' @inheritParams ggplot2::scale_fill_viridis_c
#' @inheritParams KGExplorer::filter_dt
#' @returns ggplot or plotly object
#'
#' @export
#' @import ggplot2
#' @importFrom plotly ggplotly
#' @importFrom scales pretty_breaks
#' @examples
#' keep_descendants <- "Neurodevelopmental delay"
#' plt_pheno_count <- plot_bar_summary(count_var = "hpo_name",
#'                                 group_var = "CellType",
#'                                 keep_descendants = keep_descendants)
#' plt_cell_count <- plot_bar_summary(count_var = "CellType",
#'                                 group_var = "hpo_name",
#'                                 keep_descendants = keep_descendants)
plot_bar_summary <- function(results = load_example_results(),
                            count_var = "hpo_name",
                            group_var = "CellType",
                            keywords = NULL,
                            q_threshold = 0.0005,
                            effect_threshold = 1,
                            filters = NULL,
                            keep_descendants = NULL,
                            hpo = HPOExplorer::get_hpo(),
                            option = "magma",
                            interactive = TRUE,
                            show_plot=TRUE,
                            verbose = TRUE){
  requireNamespace("ggplot2")
  #### Subset results ####
  phenos <- subset_phenos(keep_descendants = keep_descendants,
                          results = results,
                          hpo = hpo,
                          filters = filters,
                          q_threshold = q_threshold,
                          effect_threshold = effect_threshold,
                          verbose = verbose)
  phenos <- HPOExplorer::add_hpo_id(phenos = phenos)
  phenos <- HPOExplorer::add_hpo_name(phenos = phenos)
  #### Aggregate counts ####
  counts_df <- agg_results(phenos = phenos,
                           count_var = count_var,
                           group_var = group_var)
  #### Make subtitle ####
  ## plotly doesn't inherit subtitle arg from ggplot,
  ## so need to append it to the title.
  subtitle <-  paste0(
    if(!is.null(keywords)){
      paste0("keywords: ",shQuote(paste(keywords,collapse = ", ")),";")
      },
    "  ",formatC(length(unique(phenos[[count_var]])),big.mark = ","),
    " ",tolower(count_var),"(s)"
    )
  n_count_var <- paste("n",paste0(tolower(count_var),"s"),sep="_")
  #### Sort celltypes by counts ####
  data.table::setorderv(counts_df, n_count_var, -1L)
  counts_df[[group_var]] <- factor(counts_df[[group_var]],
                                   levels = unique(counts_df[[group_var]]),
                                   ordered = TRUE)
  #### Make plot ####
  plt <- ggplot2::ggplot(counts_df,
                         ggplot2::aes(x = !!ggplot2::sym(group_var),
                           y = !!ggplot2::sym(n_count_var),
                           fill = !!ggplot2::sym("mean_effect"),
                           mean_q = !!ggplot2::sym("mean_q"),
                           mean_sd_from_mean = !!ggplot2::sym("mean_sd_from_mean"),
                           values = !!ggplot2::sym(tolower(count_var))
                           )
                ) +
    ggplot2::geom_col() +
    ggplot2::labs(title = paste0("Enrichment results associated with:\n",
                        subtitle
    ),
    x = group_var,
    y = paste(count_var,"count"),
    fill = "Mean fold change") +
    ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(),
                       expand = ggplot2::expansion(mult = c(0, .1))) +
    ggplot2::scale_fill_viridis_c(option = option) +
    ggplot2::theme_bw() +
    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45,
                                     vjust = 0.5, hjust = 1,
                                     size = 10))
  if(isTRUE(interactive)){
    plt <- plotly::ggplotly(plt) |>
      plotly::layout(hoverlabel = list(align = "left"),
                     margin = list(l = 0, r = 0,
                                   b = 0, t = 70,
                                   pad = 0) )
  }
  if(show_plot) methods::show(plt)
  return(plt)
}