Plot tweaks for multi-page pdfs

R/bootstrap_plot.R

@@ -1,3 +1,116 @@
+#' Create a multi-page PDF with each cell type on a separate page
+#'
+#' This function creates a PDF where each unique value in a specified faceting 
+#' variable (typically "CellType") is plotted on a separate page. Each plot shows 
+#' expression data with points colored by expression levels.
+#'
+#' @param gene_data A data frame containing gene expression data with columns for 
+#'   "boot", "hit", and a faceting variable (typically "CellType").
+#' @param facet_var Character string specifying the column name to use for 
+#'   separating plots onto different pages (e.g., "CellType").
+#' @param output_file Character string specifying the path to save the PDF file.
+#' @param add_labels Character string specifying the column name to use for point labels,
+#'   or NULL to disable labels (default: NULL).
+#' @param verbose Logical indicating whether to print progress messages (default: TRUE).
+#'
+#' @return No return value, called for side effect of creating a PDF file.
+#'
+#' @details The function creates QQ plots of "boot" vs "hit" values, with points 
+#'   colored by "hit" values using a reversed viridis color scale. Each plot includes
+#'   a title showing the cell type being displayed. If add_labels is provided, text labels
+#'   from the specified column will be added to the points using ggrepel.
+#'
+#' @examples
+#' \dontrun{
+#' # Create plots without labels
+#' .create_multipage_plot(
+#'   gene_data = my_gene_data,
+#'   facet_var = "CellType",
+#'   output_file = "cell_type_plots.pdf"
+#' )
+#'
+#' # Create plots with gene symbol labels
+#' .create_multipage_plot(
+#'   gene_data = my_gene_data,
+#'   facet_var = "CellType",
+#'   output_file = "cell_type_plots_labeled.pdf",
+#'   add_labels = "gene_symbol"
+#' )
+#' }
+#'
+#' @import ggplot2
+#' @importFrom viridis scale_color_viridis_c
+#' @importFrom ggrepel geom_text_repel
+#'
+#' @keywords internal 
+.create_multipage_plot <- function(gene_data, facet_var, output_file, 
+                                   add_labels = NULL, verbose = TRUE) {
+  # Check for required packages
+  if(!is.null(add_labels) && !requireNamespace("ggrepel", quietly = TRUE)) {
+    stop("Package 'ggrepel' is needed for adding labels. Please install it.",
+         call. = FALSE)
+  }
+
+  if (verbose) messager("Saving plot -->", output_file)
+  # Check if the specified label column exists
+  if(!is.null(add_labels) && !(add_labels %in% colnames(gene_data))) {
+    stop(paste0("Label column '", add_labels, "' not found in data."),
+         call. = FALSE)
+  }
+
+  # Get unique cell types
+  cell_types <- unique(gene_data[[facet_var]])
+
+  # Start PDF device
+  pdf(output_file, width = 5, height = 5)
+
+  # Create and print a plot for each cell type
+  for (cell_type in cell_types) {
+    # Subset data for this cell type
+    subset_data <- gene_data[gene_data[[facet_var]] == cell_type, ]
+
+    # Create base plot for this cell type
+    g <- ggplot(subset_data, aes(x = boot, y = hit, color = hit)) +
+      geom_point(size = 1, alpha = .75) + 
+      xlab("Mean Bootstrap Expression") +
+      ylab("Expression in cell type (%)\n") +
+      scale_color_viridis_c(direction = -1) +
+      ggtitle(paste("Cell Type:", cell_type)) +
+      geom_abline(
+        intercept = 0, 
+        slope = 1, 
+        linetype = "dashed",
+        colour = ggplot2::alpha("red",.5)
+      ) +
+      theme_classic()
+
+    # Add labels if a column name was provided
+    if(!is.null(add_labels)) {
+      g <- g + ggrepel::geom_text_repel(
+        aes(label = .data[[add_labels]]),  # Use the column name dynamically
+        size = 3,
+        force_pull = 0.1, 
+        box.padding = 0.35,
+        point.padding = 0.5,
+        segment.color = "grey50",
+        max.overlaps = 25
+      )
+
+      if (verbose) messager("Added labels from column:", add_labels)
+    }
+
+    # Print the plot (adds it to the PDF)
+    print(g)
+
+    if (verbose) messager("Added plot for", cell_type)
+  }
+
+  # Close the PDF device
+  dev.off()
+
+}
+
+
 #' Bootstrap plot
 #' 
 #' Plot bootstrap enrichment results. 
@@ -27,6 +140,7 @@ bootstrap_plot <- function(gene_data,
 
     requireNamespace("ggplot2")
     requireNamespace("patchwork")
+    requireNamespace("ggrepel")
     Pos <- Rep <- Exp <- p <- significant <- CellType <- NULL;
 
     exp_mats_msg <- paste(
@@ -36,8 +150,11 @@ bootstrap_plot <- function(gene_data,
     plots <- list()
     #### Set up save path ####
     dir.create(save_dir, showWarnings = FALSE, recursive = TRUE)  
-    gene_data$symLab <- ifelse(gene_data$hit > 25, 
-                               sprintf("  %s", gene_data$gene), NA) 
+    gene_data$symLab <- ifelse(
+      test = gene_data$hit - gene_data$boot > 1 | gene_data$hit > 75, 
+      yes = gene_data$gene, 
+      no = ""
+    ) 
 
     #### Filter gene_data ####
     if(!is.null(signif_ct)){
@@ -55,46 +172,22 @@ bootstrap_plot <- function(gene_data,
         dir.create(dirname(f), showWarnings = FALSE, recursive = TRUE)   
     }
     ## Plot several variants of the graph ##
-    add_line <- function(){
-        geom_abline(intercept = 0, slope = 1, 
-                    linetype = "dashed",
-                    colour = ggplot2::alpha("red",.5)) 
-    } 
     #### Plot 1: Plot without gene names  ####
-    g1 <- ggplot(gene_data,
-                          aes_string(x = "boot", y = "hit",  color="hit")) +
-        geom_point(size = 1,  alpha=.75) + 
-        xlab("Mean Bootstrap Expression") +
-        ylab("Expression in cell type (%)\n") +
-        scale_color_viridis_c() +
-        facet_grid(facets = facets, 
-                   scales = scales) + 
-        add_line() +
-        theme_graph() 
-    plots[["plot1"]] <- g1 
-    messager("Saving plot -->", files[[1]], v=verbose)
-    ggplot2::ggsave(filename = files[[1]], 
-                    plot = g1,
-                    width = 3.5, 
-                    height = 3.5) 
+    .create_multipage_plot(
+      gene_data = gene_data,
+      facet_var = "CellType",  # Column name containing cell types
+      output_file = files[[1]],
+      verbose = verbose
+    )
 
     #### Plot 2: Plot with gene names  ####
-    g2 <- g1 + 
-        geom_text(aes_string(label = "symLab"),
-                   # fill=ggplot2::alpha("black",.5),
-                   color=ggplot2::alpha("black",.75),
-                   na.rm = TRUE,
-                  hjust = 0, vjust = 0, size = 3
-        ) + 
-        scale_x_discrete(expand = expansion(mult = c(0,.15))) +
-        scale_y_discrete(expand = expansion(mult = c(0,.15))) 
-    plots[["plot2"]] <- g2 
-    messager("Saving plot -->", files[[2]], v=verbose)
-    ggplot2::ggsave(filename = files[[2]], 
-                    plot = g2,
-                    width = 3.5, 
-                    height = 3.5) 
-
+    .create_multipage_plot(
+      gene_data = gene_data,
+      facet_var = "CellType",  # Column name containing cell types
+      output_file = files[[2]],
+      add_labels = "symLab",
+      verbose = verbose
+    )
 
     #### Plot 3 ####
     if(is.null(exp_mats)){
@@ -134,11 +227,13 @@ bootstrap_plot <- function(gene_data,
             scale_x_discrete(breaks = NULL) +
             theme_graph() +
             facet_wrap(facets=facets)
+        # cat("Names of melt boot:", paste(names(melt_boot), collapse = " "))
+        # wd <- 1 + length(unique(melt_boot[, hit])) * 0.25 
         plots[["plot3"]] <- g3
         messager("Saving plot -->", files[[3]], v=verbose)
         ggplot2::ggsave(filename = files[[3]], 
                         plot = g3,
-                        width = 3.5, 
+                        width = 8, 
                         height = 3.5) 
 
 
@@ -182,16 +277,18 @@ bootstrap_plot <- function(gene_data,
             labs(x=expression("Least specific" %->% "Most specific"),
                  y="Expression in cell type (%)\n") 
         #### Save ####
-        wd <- 1 + length(unique(melt_boot[,4])) * 0.175 
+        # wd <- 1 + length(unique(melt_boot[,4])) * 0.25 
         plots[["plot4"]] <- g4
         messager("Saving plot -->", files[[4]], v=verbose)
         ggplot2::ggsave(filename = files[[4]], 
                         plot = g4,
-                        width = wd, 
+                        width = 8,
                         height = 4) 
     }
 
     if(isTRUE(show_plot)) methods::show(plots)
     return(list(plots=plots,
                 paths=files))
 }
+
+

R/check_full_results.R

@@ -8,7 +8,8 @@
 #' 
 #' @keywords internal
 check_full_results <- function(full_results,
-                               sct_data) {
+                               sct_data,
+                               annotLevel) {
     if (!is.null(full_results)) {
         err_msg <- paste0(
             "ERROR: full_results must be a list of length 3 or 4",
@@ -23,13 +24,9 @@ check_full_results <- function(full_results,
             " sct_data. Perhaps the wrong annotLevel was used?"
         )
         if (sum(!as.character(unique(full_results$results$CellType)) %in%
-            colnames(sct_data[[1]]$specificity)) ==
+            colnames(sct_data[[annotLevel]]$specificity)) ==
             length(as.character(unique(full_results$results$CellType)))) {
             stop(err_msg2)
         }
-        if (sum(!as.character(unique(full_results$results$CellType)) %in%
-            colnames(sct_data[[1]]$specificity)) > 0) {
-            stop(err_msg2)
-        }
     }
 }

R/generate_bootstrap_plots.r

@@ -153,7 +153,8 @@ generate_bootstrap_plots <- function(sct_data = NULL,
     )
     check_full_results(
         full_results = full_results,
-        sct_data = sct_data
+        sct_data = sct_data,
+        annotLevel = annotLevel
     ) 
     results <- full_results$results
     #### Check gene lists ####